If you operate a retail brand today, you should pursue an omnichannel marketing and distribution strategy. And if you’re pursuing an omnichannel strategy, you should at least consider launching a mobile application for your online business. The benefits of eCommerce mobile apps are very alluring:

* **They make it easier to identify return customers,** providing richer, deeper opportunities for personalizing the customer experience.
* **They allow you to send push notifications to your users.** That’s a real-time, high-engagement, fully-owned marketing channel–and it’s free, too.
* **They offer your customers a faster, more convenient online shopping experience,** making for higher conversion rates, better retention, and larger baskets.

However, mobile apps are not for all brands: depending on what type of app you want to build, the financial investment can be significant. T**he more you want your app to act as a customer acquisition driver, the more expensive it will be to build and operate.** And ironically, brands that need acquisition-focused apps are usually the ones with the least money to spend on them, which makes this a complex juggling act.

The goal of this guide is two-fold: we want to provide you with all the context you need to make this decision in the most informed and balanced way possible, and if you decide to move forward, we want to offer practical tips to design and build an eCommerce app that will delight your customers while pursuing your business goals.

## Transactional Apps Drive Lifetime Value

If we asked the average user to picture a mobile app for an eCommerce brand they like, they would probably copy-paste the brand’s online store into a native UI, ending up with a shopping app that simply serves as an additional sales channel. We typically refer to these as “transactional apps” because their only purpose is to facilitate transactions between a brand and their customers.

While there’s nothing wrong with transactional apps per se, you should know that this is not the only type of app you can build–although it’s the easiest. **Transactional apps are incredibly useful if you already have significant brand equity or a high repeat purchase rate and want to get more value from your existing customers.** By providing a better, more user-friendly shopping experience than your other channels and potentially your competitors, you can improve your conversion rate, average order value, and repeat purchase rate.

![](https://cdn.sanity.io/images/kqgmf0g2/production/635f4d681b5cbf832e452b6f69380fc936f3600d-2400x1500.png?fit=clip)

**What a transactional app can’t help you with is customer acquisition.** Anyone who shops through a transactional app should be considered a high-intent customer and would have likely shopped with you anyway, although their orders might have been a bit smaller or less frequent in other channels.

One of the most popular transactional apps is Nespresso’s mobile app: it allows you to place orders, manage your Nespresso subscription, and manage your Nespresso coffee machine, all without opening their website. The app does a great job of eliminating any friction in the shopping experience, increasing repeat purchase rate and average order value for all existing Nespresso customers.

## Experiential Apps Drive Customer Acquisition

We’ve seen that transactional apps boost your customer lifetime value, not your acquisition rates. Does that mean you shouldn’t build a mobile eCommerce app if you’re focused on customer acquisition? Not quite–it just means your goal should be to build an app that’s experiential rather than transactional.

Experiential apps take the concept of an eCommerce app a step further. Rather than simply facilitating a transaction your customers can already complete on your website, **experiential apps provide value in and of themselves.** This can come in the form of exclusive content, a community, or a tool/service that is relevant to your physical product(s). These apps effectively become part of your brand identity rather than being another sales channel.

**The idea behind experiential apps is to build brand awareness and capture low-intent customers** by providing them with free, immediate value, only nurturing and converting them at a later stage. It’s a much longer-term bet than launching a transactional app but it can also pay off huge dividends over time. Experiential mobile apps are one of many ways you can pursue a [Product-Led Growth strategy.](https://nebulab.com/blog/product-led-ecommerce-growth)

![](https://cdn.sanity.io/images/kqgmf0g2/production/68db8d648a056e09c99d04923a473428bdf01c76-2400x1500.png?fit=clip)

Note that **most experiential apps also offer transactional capabilities,** allowing you to kill two birds with one stone: high-intent customers can use your mobile shopping cart to buy from you, while low-intent shoppers can use the experiential features to explore your brand at their own pace–and eventually convert into paying customers.

Personalized vitamin brand [Care/of](https://www.takecareof.com/) recently released an app that is equal parts transactional and experiential: the app sends you reminders and allows you to track your vitamin intake, as well as keep track of your daily habits. By performing these actions, you earn points, which you can redeem for free rewards–the perfect entry point into Care/of’s world. At the same time, existing customers can use the app to manage their Care/of subscription.

## Greenfield Builds vs. App Builders

Once you have a clear idea of what kind of app you want to build, you need to decide how you want to build it. While your tech team or mobile app development agency might have strong opinions over which tech stack they want to use, it’s essential to be aware of the implications of your choices.

**Traditionally, mobile apps have been built from scratch,** using technologies such as Swift (which is specific to iOS) or React Native (which allows you to power cross-platform native apps with a single codebase).

**Building your app from scratch is basically the equivalent of writing your own checkout code,** and it has many of the same advantages and limitations. It gives you tremendous flexibility to build whatever app you have in mind–which is fantastic if you’re launching an experiential app–but it also requires a significant investment and technical know-how.

**A greenfield app will take anywhere between 8 and 12 weeks and cost you $50-100K,** depending on your agency/tech team composition. These apps also tend to be more expensive to operate and maintain over time.

![](https://cdn.sanity.io/images/kqgmf0g2/production/a796fd0d1992c28ec77d4059b627ec951d39201d-2560x1850.png?fit=clip)

**The alternative to a greenfield build is to use an app builder.** Over the last years, several startups have emerged that offer a much cheaper and quicker way to launch a mobile app than building it from scratch: you simply connect them to your Shopify store or other eCommerce platform, customize your app by using a drag-and-drop interface that allows you to reuse predefined templates, and click a button to have your app published in the Apple and Android app stores.

App builders are especially popular in the eCommerce domain, because the user experience of purchasing a product online tends to be very repeatable. **Launching an app with one of these tools can take as little as two weeks, and pricing is usually fairly accessible.** [Tapcart](https://www.tapcart.com/), for example, is the most popular app builder for Shopify and starts from $300/month.

However, these tools have a downside: because you need to operate within the constraints of your app builder, you are much more limited in terms of the functionality you can implement. **This makes app builders a terrible choice for experiential apps, where you want the app to stand out and capture your customers’ attention.**

## What Type of App Should You Build?

Ultimately, there’s no one-size-fits-all answer: whether to build a transactional or experiential app depends on your eCommerce store’s stage, customer profile, business model, tech savviness, and a thousand other factors that are pretty complex to account for.

In general, **if you want to drive customer acquisition and have the resources, you should go with an experiential app.** It’s your best shot at capturing customers that are interested in what you have to offer but might not be ready to shop with you just yet.

However, **if your primary goal is to create a low-friction sales channel for your mobile users, you should likely build a transactional app instead.** It will give you the best bang for your buck, increasing customer satisfaction, repeat purchase rate, and basket size.

## Tracking Your Mobile App’s Performance

What KPIs to track for your mobile app depends on the type of app you’re building: tracking the performance of an experiential app will look very different from monitoring the performance of a transactional app, since the goals are fundamentally different (acquiring customers vs. increasing LTV).

However, you should always track both product metrics and business metrics:

* **Product metrics** measure how well the app itself is performing. How many users are downloading your app? How often are they opening it? What’s the average session length? What are the biggest points of friction?
* **Business metrics** measure how well your app is moving the business forward. How many paying customers is your app generating? What’s the AOV of an app order vs. a mobile/desktop website order?

To answer these questions, you need to make sure your app is instrumented correctly to capture this data. While this will come for free with app builders, you’ll need to be much more intentional about analytics when building an app from scratch.

![](https://cdn.sanity.io/images/kqgmf0g2/production/58a36d4bfc3bc7ff9e4358948a51e89862292ef9-2970x1980.jpg?fit=clip)

Finally, a note of caution: especially when building a transactional app, **you should be mindful that mobile app customers are a unique segment,** and you can’t draw one-to-one comparisons with your general customer population.

For example, you might be tempted to compare your average in-app conversion rate to your average on-site conversion rate, but that’s a pretty useless insight: almost by definition, your mobile app users are much higher-intent than your website users since they went through the pain of finding and downloading your app. The fact that your app’s conversion rate is 2X your website’s conversion rate doesn’t really mean anything.

Ideally, **you want to compare metrics across customer segments that are as similar as possible** (e.g., highly-engaged customers that regularly shop online vs. in-app) to ensure you’re assessing your app’s performance fairly.


## Ready to Launch Your App?

We’ve just provided a high-level overview of what it takes to design and build a mobile app for your eCommerce business. As you’ve seen, it’s a project that can have incredible benefits but also requires a clear, upfront understanding of your motivations.

If you’re unsure whether a mobile app–or what kind of app–is right for you, we’re here for you! Feel free to reach out for a free consultation with our brand strategists, and we’ll help you make the best choice for your brand.

The Honest Guide to Mobile eCommerce Apps

At the end of 2022, the entire frontend community was surprised by an unexpected announcement: [Shopify acquired Remix](https://shopify.engineering/remix-joins-shopify). At first glance, this news offers little information; after all, Shopify is famous for investing many resources into open-source projects. However, after further research, the reason for this merger became clear: Remix is deeply aligned with Shopify’s new framework: Hydrogen.

Shortly after the big news, the dev team behind Hydrogen [made another announcement](https://hydrogen.shopify.dev/roadmap/): the entire framework was being re-created from scratch using Remix, thus giving birth to Hydrogen v2. While the upgrade path will likely be a struggle for existing Hydrogen v1 users, Hydrogen v2 offers several benefits over its predecessor.

In this article, we’ll look at some of the most significant enhancements Hydrogen v2 brings to the developer experience, what they mean for your user experience, and how you can start adopting them in your storefront.

## A Brief Primer on Remix

To fully comprehend Hydrogen’s latest changes, we must first understand Remix.

For those unfamiliar with it, Remix is an up-and-coming Javascript framework based on React. It was developed by Kent C. Dodds, Ryan Florence, and Michael Jackson, all well-known contributors to the Javascript open-source community.

Unlike the previous generation of React frameworks such as Next.js, Remix is full-stack, monolithic, and fully server-side-rendered, and it will feel like a breath of fresh air to all the Ruby on Rails and Laravel lovers. In fact, Remix’s primary goal is to merge the benefits of the “old Web” with the smooth developer experience React offers. This way, developers get higher productivity, simpler code, better SEO, less backend-frontend communication overhead, smaller client bundles, and near-instantaneous page transitions.

![](https://cdn.sanity.io/images/kqgmf0g2/production/030082583dd37bf225378d98322d8b1993cf3e0b-2960x2400.png?fit=clip)

When developing the first version of Hydrogen, Shopify created a framework very similar to Remix in spirit: it was React-based, but it was also rendered on the server. This was a surprising choice back in the early days of Hydrogen, but it did make a lot of sense in retrospect: anyone who has tried to implement an eCommerce storefront quickly found that the dynamic nature of eCommerce doesn’t play well with static site generation and client-side rendering.

In October 2022, Remix announced that Shopify was acquiring it. As part of the deal, Shopify will offer continuous support to Remix’s authors to continue developing the framework, and Remix is becoming the React framework powering Hydrogen. On their side, Hydrogen’s development team can now delegate most of the core framework functionality to Remix, focusing only on Hydrogen-specific functionality.

## The Main Changes in Shopify Hydrogen v2

Even though the Shopify developer community (and Shopify themselves, [in some places](https://github.com/Shopify/hydrogen/blob/2023-04/packages/hydrogen/CHANGELOG.md#202317)) dubbed this release “Hydrogen v2”, this isn’t technically correct: Hydrogen simply follows calendar versioning, with each version number being a combination of the date and month of the release (e.g., v2023.1.0). However, for the sake of simplicity, we will refer to “Hydrogen v2” as the version of Hydrogen that uses Remix.

As you might expect, this release introduces _a lot_ of changes. In fact, pretty much everything in the framework has changed, meaning developers with an existing Hydrogen storefront have a whole bunch of rewriting to do.

### Retrieving Data with Loaders

The most significant change–which will affect pretty much every route in your project–is the introduction of Remix’s `loader` function.

The `loader` function is where all of your data retrieval logic is stored. The `useShopQuery` and `useQuery` hooks from Hydrogen v1, responsible for querying the Shopify Storefront API, have been replaced with `loader` in Hydrogen v2.

For example, imagine building a product detail page with a product options selector. In a typical React storefront, you would need to store the selected options in a state entry:

![](https://cdn.sanity.io/images/kqgmf0g2/production/e553a7b7b1f1e0d77e3cd95a9ecef7a1893b29c9-1200x520.png?fit=clip)

The problem with this implementation is that this component also needs to change the URL whenever a new variant is selected, so that users can easily bookmark or share the URL to the correct product variant. Unfortunately, there is no “native” way of achieving this with React. You would need to create a function that alters the URL when the user changes their selection.

With Remix, just like Ruby on Rails or Laravel, you can solve this by simply taking advantage of Web fundamentals. In this case, whenever the user selects a new product option, you simply direct them to a new URL, and the `loader` function gets executed again, querying the desired product variant from the Storefront API.

![](https://cdn.sanity.io/images/kqgmf0g2/production/3217fe54a31e57a20a6240808dd00e4937c4f7eb-1200x856.png?fit=clip)

The benefits of this new implementation are clear: we achieve the same outcome with less code and fewer abstractions to learn.

This approach was used to refactor all the major components in Hydrogen: the cart, analytics, SEO, and session management were rebuilt from scratch, resulting in a much more straightforward developer experience and a less steep learning curve!

[Learn more about the `loader` function in the Remix guides.](https://remix.run/docs/en/1.16.1/route/loader)

### Mutating Data with Actions

While the `loader` is used to _retrieve_ data, the `action` function is to _mutate_ data. As a consequence of segregating business logic into loaders and actions, you can now remove a bunch of code, especially client-side logic and state.

As an example, take a look at what the default `AddToCart` component looks like in the Hydrogen v1 demo store:

![](https://cdn.sanity.io/images/kqgmf0g2/production/253be981f9a0861351e4e2bcba39e77df56fec53-1374x2904.png?fit=clip)

And now, look at the same component in the Hydrogen v2 demo store:

![](https://cdn.sanity.io/images/kqgmf0g2/production/5c38c039f5c5132d9b9ba34c6d19d0b0075b7f0b-1424x1786.png?fit=clip)

As you can see, a lot of state management and querying logic has been removed in favor of a more straightforward implementation that relies on Web fundamentals such as... Web forms! This simplifies your code and makes your online store more resilient to network issues and more accessible to screen readers and old devices.

[Learn more about the `action` function in the Remix guides.](https://remix.run/docs/en/1.16.1/route/action)

### Handling Errors with Error Boundaries

A common and annoying problem with React applications is isolating errors. Typically, when you get any error on the frontend, the whole UI breaks, and the website becomes unusable. This is obviously not a desirable experience for our customers.

By taking advantage of Remix’s [nested routes](https://remix.run/docs/en/1.16.1/guides/routing#what-is-nested-routing), you can use [nested error boundaries](https://remix.run/docs/en/v1/guides/errors#nested-error-boundaries). If you export an error boundary function from a nested route and an error occurs, the error will be isolated, and the rest of your application will remain usable. This allows you to offer the best possible UX, even when things go wrong:

![](https://cdn.sanity.io/images/kqgmf0g2/production/e73f3849c1e52c9a44c721ff4b43976455174ab8-1207x1685.png?fit=clip)

[Learn more about error handling in the Remix guides.](https://remix.run/docs/en/1.16.1/guides/errors)

### Speeding Up Page Loads with Streaming

By using Remix’s [defer](https://remix.run/docs/en/1.16.1/guides/streaming#using-defer) function, you can easily decide which parts of your page should be rendered right away and which parts can be streamed after the page has loaded:

![](https://cdn.sanity.io/images/kqgmf0g2/production/d1fc77778b2760be478d391326acc7ede28f1abe-1207x1685.png?fit=clip)

This technique allows you to easily defer the loading of lower-priority parts of your UI, such as a “Related products” section on your PDP, making for a better user experience and faster loading times.

[Learn more about streaming in the Remix guides.](https://remix.run/docs/en/1.16.1/guides/streaming)

### Speeding Up User Interactions with Optimistic UI

Optimistic UI is a powerful technique that lets you completely hide loading states from the end user. For instance, when a user clicks on your Add to Cart button, rather than waiting for the cart to be updated on the server, you might immediately display the new item in the user’s cart. Then, if an error occurs, you can revert the state and display an error to the user:

![](https://cdn.sanity.io/images/kqgmf0g2/production/1cdb09cad49d184358eb840920173250a2061bc1-1250x800.png?fit=clip)

While not available yet, Hydrogen’s development team plans to adopt optimistic UI in all the standard components. Because Hydrogen is powered by Remix, you will also have the opportunity to use the same pattern in any custom user interactions you offer on your storefront.

[Learn more about optimistic UI in the Remix guides.](https://remix.run/docs/en/1.16.1/tutorials/jokes#optimistic-ui)

### Oxygen Is now Available to All Shopify Plans!

Although there was no official announcement, this is massive news for all Shopify developers! Hydrogen’s official deployment platform, [Oxygen](https://shopify.dev/docs/custom-storefronts/oxygen), which was only available to Shopify Plus users, is now available to all Shopify plans.

Oxygen is an excellent tool for engineers who want a seamless deployment and hosting experience. By keeping your storefront within Oxygen, you can not only reduce your infrastructure costs, but also have access to a fantastic developer experience that includes:

* Preview environments for testing changes in isolation.
* Custom environments (e.g., if you want a staging area).
* Version control and GitOps for your storefront.
* A Content Delivery Network (CDN) powered by Shopify.
* Support for both backend and frontend code.
* CI/CD via GitHub Actions and the Hydrogen channel.

If you’re familiar with Vercel, you’ll find that Oxygen offers a very similar development experience, but more accurately tailored to the needs of Shopify developers.

## Migrating from Hydrogen v1 to Hydrogen v2

If you are using Hydrogen v1 today, there’s a [handy migration guide](https://shopify.dev/docs/custom-storefronts/hydrogen/migrate-hydrogen-remix) in the Hydrogen docs that you can follow to upgrade your storefront.

The process takes a lot of effort, but the guide is comprehensive and accurate, so you should be able to upgrade your Hydrogen app by simply following the steps outlined.

## Is Hydrogen Ready for Production Use Yet?

That depends on your risk tolerance!

On a technical level, both Hydrogen and Oxygen are most definitely production-grade products, and they are being used successfully in the wild by large Shopify stores such as [Allbirds](https://hydrogen.shopify.dev/case-studies/allbirds). The developer experience is pleasant and powerful, and Shopify’s hosting infrastructure will easily handle any production load you throw at it.

On the other hand, it’s unclear whether Hydrogen will see any more extensive refactorings, such as the one we’ve just witnessed. As an example, [Remix v2](https://remix.run/docs/en/main/pages/v2) will be released soon, bringing many improvements over its predecessor, but also requiring developers to upgrade to a new API and file naming convention. These changes will most likely affect Hydrogen users as well. It’s also unclear what synergies Shopify will create between Hydrogen and [Commerce Components](https://www.shopify.com/commerce-components), but we expect some movement there.

![](https://cdn.sanity.io/images/kqgmf0g2/production/e038f0246b07a894d83a8ae5cb1d3fe545d2bc6a-2960x2335.png?fit=clip)

Another factor to consider is how long it will take for the Shopify app ecosystem to catch up to Hydrogen’s philosophy. While some Shopify apps don’t need to worry about the frontend at all, others will have to expose an API or JavaScript SDK of some sort to be usable with a headless commerce architecture. We expect the situation to improve over the coming years, but there’s still a long way to go, and many Shopify apps today cannot be used with Hydrogen.

Overall, Hydrogen is well worth a try if you have the engineering firepower. It solves a significant number of problems for Shopify developers and gives brands unmatched flexibility in building a custom storefront, allowing them to create shopping experiences that would be very hard to replicate with Liquid themes.

However, if you’re a small brand and don’t have the resources to invest in experimental technology, you’re probably better off sticking with Liquid themes until Hydrogen becomes a true first-class citizen in the Shopify ecosystem.

Shopify Just Released Hydrogen v2. What’s In It for You?

The modern direct-to-consumer brand has potential access to a plethora of information about their customers: post-purchase surveys, CSAT scores, site analytics, [A/B testing](https://nebulab.com/blog/ecommerce-ab-testing), market research… Often, it can all be a bit too much, with brands not being sure how to use this information in a structured and strategic way to scale their business.

Luckily, that is precisely the problem product management was born to solve. Product management is the job of defining _what_ you should build, _how_ you should build it, and _when_ you should build it. It is a proven problem-solving methodology that allows you to collect and deploy information to work toward your business objectives.

Even though you might not have direct product management experience, you may already be doing some product management. For example, if you are looking at your conversion funnel analytics to understand where users are dropping from your checkout flow and then [running A/B tests](https://nebulab.com/blog/ecommerce-ab-testing) to determine how to improve your conversion rates, you’re doing product management.

In this article, we’ll try to provide you with a more structured approach that will allow you to elevate your product management skills, turning them into a repeatable workflow for identifying, prioritizing, and pursuing the most impactful goals for your brand. These are the same techniques used by behemoths such as [Zappos](https://www.zappos.com/) and [Warby Parker](https://www.warbyparker.com/) to achieve the impressive scale they’ve experienced over the last decade, so you’ll be in good company!

## Understanding and Applying Research Methodologies

At the core of product management lies product research, the practice of collecting data and insights that can inform which problems you should solve and how to solve them best.

Over their career, experienced product managers learn to master various research techniques. You might have heard about some of them–such as A/B testing, customer interviews, and surveys–while others might be more obscure–such as tree sorting, fake door tests, and contextual inquiry. You’re encouraged to do your own research (no pun intended!) and learn more about each technique.

For now, you should know that every research technique exists on two spectrums: quantitative or qualitative and generative or evaluative. Let’s see what that means!

### Quantitative vs. Qualitative Research

Quantitative research is used to collect numerical data, generally through automated techniques such as closed-ended surveys or product analytics. The data is then consolidated and interpreted to identify a problem or opportunity in front of your business.

For instance, if your checkout conversion rate is well below the average for your industry, that might be something you want to work on with your team.

However, the numbers alone might not always tell you exactly _why_ something is happening: your conversion rate might be low because your prices are too high or the user experience of your checkout flow is confusing. That’s where qualitative research comes in.

Qualitative research, on the other hand, is used to collect in-depth information, generally through techniques such as customer interviews or open-ended surveys. The information is then collected, parsed, and distilled by a human being into insights–tiny nuggets of information that have some practical implication for your business.

For instance, if you interview ten customers who dropped out of checkout, and seven of them mention that they weren’t sure when they would receive their order, you might conclude that customers find your order tracking process challenging to understand. You can then use this information when prioritizing your work to improve the conversion rate (more on this below).

Note that quantitative research doesn’t always have to come before qualitative research: you will find yourself going back and forth between the two depending on the problem you’re currently focusing on and how you’re approaching it.

### Generative vs. Evaluative Research

The examples we have made in the above paragraph are all about _generative_ research, which is done to identify a problem to solve or an opportunity to tackle. In generative research, your goal is to gain an intimate understanding of your market, customers, and product to understand _what_ you should build and _when_ you should build it.

The research doesn’t stop when you have identified a problem or an opportunity. In fact, that’s just half of the equation. The other half is about using product research to evaluate potential solutions to the situation in front of you. That’s where evaluative research comes in.

In evaluative research, you aim to understand _how_ to solve the problem or tackle the opportunity. This can be done with various techniques, some of which can be generative or evaluative depending on the context: for instance, usability testing can be performed to identify problems with your existing checkout flow, or it can be performed to validate the UX of your proposed redesign.

Mastering some generative and evaluative research techniques will help ensure your work is product-driven end-to-end. Many retailers let the data and insights guide their product roadmap but fail to validate their design solutions. This can lead to doing work that doesn’t move the needle or even moves it in the wrong direction.

### Establishing a Continuous Discovery Practice

While it’s tempting to do product research only when you run out of things to do, the best teams are running research systematically–a practice referred to as “continuous discovery.” Continuous discovery combines automated and manual work to ensure a stream of qualitative and quantitative insights flows into your brand.

There’s no one-size-fits-all approach to continuous discovery, but there are some techniques that we’ve seen work for the majority of eCommerce and direct-to-consumer brands:

* **Post-purchase surveys.** Every eCommerce business should be running post-purchase surveys. At a minimum, you want to ask customers how they found out about you, which will help enrich your attribution data, but you can also use these to collect feedback about other aspects of the customer experience.
* **On-site analytics.** Solid on-site analytics will help you collect valuable first-party data, which you can use to spot challenges and opportunities and validate the effectiveness of new features, products, or UX improvements. The trick is not to overdo it: otherwise, you risk drowning in a sea of metrics.
* **Business reports.** Your eCommerce platform should offer some out-of-the-box reporting that you can use to quickly answer critical business questions, such as where your customers are coming from or what products are driving the most sales. However, you shouldn’t be afraid of implementing custom reports as needed.
* **Continuous interviews.** We’ve left this for last, but it’s arguably the most crucial research technique in your tool belt. You should try interviewing at least two customers a week, asking a mix of generative and evaluative questions about whatever you’re currently working on.

As you’ve seen, the key is embedding little product research opportunities at critical moments of the customer journey so that you don’t have to think about them consciously. If you need more in-depth information about a specific aspect, you can still go out and execute a full-blown research project, but continuous discovery will provide you with a solid foundation to start.

## The 6-Step Process to Accomplish Anything

Now that you understand what product management is and how to conduct product research, you might wonder how to tie these techniques to your business goals in a structured, systematic way. That’s precisely where the Opportunity Solution Tree (OST) comes in.

The Opportunity Solution Tree is a visual framework that helps your team stay focused on the current business goal while also ensuring they’re exploring the entire problem space. Depending on what you’re doing, you can either see the whole picture at a glance or drill down into the nitty-gritty of each project. Furthermore, an OST is designed for continuous optimization and evolution, which makes it the perfect tool in environments where you want to move fast but with intentionality.

![](https://cdn.sanity.io/images/kqgmf0g2/production/f4efe697bad7b33a7525c24f001cf2936087db01-2056x3272.png?fit=clip)

Here’s how you build an OST:

1. **Start from the desired outcome.** This is usually informed by your overarching business strategy. For instance, an outcome might be “Lower the return rate for our online store to 20%.”
2. **Identify opportunities to achieve the outcome.** You will typically do this through generative research. For instance, you might interview ten customers who have returned a product in the last month and find that they got the wrong size. You would then create an opportunity labeled “Customers purchase the wrong product size.”
3. **Brainstorm solutions.** In this phase, involve as many relevant stakeholders as possible: your marketing team, your development team, etc. Use research insights and your team members’ intuition to develop ideas for tackling each opportunity. Ideally, you refine the solutions to 1-3 per opportunity to keep the process manageable.
4. **Design experiments.** Each solution should come with an experiment, which ensures the solution is actually going to tackle the opportunity. This is a crucial step: by adopting a data-informed approach, you’ll eliminate your biases, assumption, and emotional attachment to specific ideas. In some cases, you might want to run multiple experiments to evaluate different aspects of a solution.
5. **Evaluate your solutions.** The next step is to run the experiments you’ve designed to test the validity of your solutions: are they creating the impact you expected? Can you see any side effects to address before moving forward?
6. **Implement the winners.** At this point, you should have a list of solutions your team can implement to achieve the desired outcome!

Here’s a bonus tip: in the OST included above, you might notice that we have broken down the main “Customers get the wrong size” opportunity into two sub-opportunities: “Customers get the wrong size when buying for themselves” and “Customers get the wrong size when buying for others.” This is a common and encouraged practice when designing an OST: you should split your opportunities into the smallest possible size while ensuring that your opportunities are Mutually Exclusive, Collectively Exhaustive (MECE). This will help you work in small chunks, which makes the process faster and more efficient.

The OST is a dead-simple yet incredibly powerful tool that can help you pursue virtually any business goal when appropriately followed. It might not be obvious, but OSTs aren’t limited to your eCommerce site: you can apply this process to improve other areas of your brand, such as fulfillment or customer service. The value of this technique lies in the fact that it forces you to structure your work and follow facts, not intuitions.

## Good Product Management Takes Practice

Most eCommerce companies are skeptical of product management at first. When you’re a small startup, every bit of process you add to your day-to-day seems like a luxury you cannot afford.

And yet, we’ve seen time and again that the brands with a clear vision and strong product management fundamentals to execute it are the ones who thrive: they exhibit lower customer acquisition costs, higher retention rates, and higher average order values. Most importantly, they move more quickly and intentionally than their competition.

You might be very excited to go out and try the new techniques you’ve just learned, and we very much encourage you to do so. However, remember that we’ve barely scratched the surface of product management in this article: while the fundamental principles are relatively simple, product management remains a vast discipline.

We have not said anything about when to use which research technique, how to develop an overarching product strategy rather than simply tackling one outcome at a time, what systems and tools are best to collect the outcomes of your research, and many other aspects that take years of practice to master fully.

If you’re curious to try product management or already have a product-driven approach and want to take it to the next level, we’re happy to help!

Supercharge Your DTC Growth with Product Management

With the rising cost of advertising and inflationary pressures making it hard to acquire, activate, and retain customers profitably, direct-to-consumer (DTC) brands are increasingly looking for ways to lower operating costs and scale more efficiently. This often takes the form of wholesale partnerships or marketplace expansions, which allow brands to tap into existing audiences and offload last-mile distribution.

We strongly believe a wholesale motion should be a crucial part of any modern brand’s go-to-market strategy. However, the DTC channel offers several opportunities that would allow brands to disengage from performance marketing as their only methodology of customer acquisition. At the same time, DTC brands can benefit from unique advantages, such as end-to-end control over the customer experience and direct access to zero- and first-party data.

This article will explore product-led growth, a methodology adopted by startups such as Dropbox, Slack, and Zoom to achieve low-cost, exponential growth. We’ll unpack the fundamentals and benefits of product-led growth, how it can be applied to eCommerce and DTC, and how you can take your first steps toward establishing a product-led growth strategy for your DTC presence.

## What Is Product-Led Growth?

Product-led growth (PLG) is a business methodology originally popularized by [OpenView](https://openviewpartners.com/) in 2016. It consists of techniques that allow you to shift away from spending marketing dollars to capture and retain customers, letting your product do the selling instead.

In contrast to more traditional approaches, such as market-led and sales-led growth models, companies that adopt product-led growth put their product experience at the center of the brand journey and take a product-first approach to scale their revenue and customer base.

They also automate secondary functions such as customer success and invest heavily in self-serve onboarding experiences, which allows potential customers to experience and play with the product before committing to the purchase.

For SaaS startups, this can take different forms, such as: offering customers a free trial, taking them through a self-guided onboarding tour, automating customer support, or facilitating word-of-mouth by rewarding customer referrals.

A well-executed product-led growth strategy has numerous benefits:

* **Lower Customer Acquisition Cost (CAC):** by letting your product and existing customer base do the selling and relying less on marketing campaigns, brands can reduce their CAC.
* **Higher Lifetime Value (LTV):** product-led growth leads to more engaged customers, which exhibit a higher Average Order Value (AOV) and Repeat Purchase Rate (RPR), making for a higher customer lifetime value.
* **Higher Revenue per Employee (RPE):** by automating as much as possible of the customer journey, you can lower your operating costs and free up resources to focus on more strategic initiatives.
* **Deeper customer understanding:** to adopt a PLG strategy, you’ll need an intimate knowledge of your customer base. This positively impacts all areas of the business, such as your fulfillment, customer service, and marketing teams.
* **Improved team alignment:** since product-led growth encompasses the entire organization, team members in a PLG company are more aligned and focused than marketing-led or sales-led teams, which makes them happier and more efficient.

As you can see, product-led growth allows you to create a flywheel that positively impacts key metrics across all areas of your organization. As you become more experienced at practicing product-led growth, you’ll start seeing the world through a different lens: rather than relying exclusively on paid acquisition channels, you will begin experimenting with ways your product can generate traction and create efficiencies.

## Does Product-Led Growth Apply to eCommerce?

The answer is: it depends. While product-led growth has been initially designed for traditional software companies, you can–with some creativity–adapt many of its elements to work for the business model and customer journey of an eCommerce/DTC brand.

For an eCommerce brand to become a product-led company, you must broaden your perspective of what constitutes your “product.” Many brands identify this directly with their physical product and consider the rest of the shopping experience an implementation detail. This mindset severely limits your ability to benefit from a product-led growth strategy.

![](https://cdn.sanity.io/images/kqgmf0g2/production/16f5b011c01d62e0d9a223638a5fc607bf5a84df-2560x2402.png?fit=clip)

Instead, product-led brands consider every customer touchpoint part of their product, and they’re constantly thinking about how to create new touchpoints to engage, activate and retain the customer. Here are a few examples of brands that are using techniques borrowed from product-led growth:

* Vitamin brand [Care/of](https://careof.org/) has a [mobile app](https://apps.apple.com/us/app/care-of/id1273387955) that allows to personalize their subscription plan, track their vitamin and health routines, and get rewarded for consistency. The app allows potential buyers to experience Care/of’s product’s value while building further engagement with established/active users.
* Swiss sportswear brand [On](https://www.on-running.com/) recently released [Cyclon](https://www.on-running.com/en-us/collection/cyclon), a program that allows customers to “subscribe” to specific products, such as running shoes and tees. They have taken a sporadic event, such as buying a new pair of shoes, and turned it into a SaaS-ified experience that boosts customer retention.
* Rather than asking customers to pay for their orders upfront, apparel brand [Stitch Fix](https://www.stitchfix.com/) allows subscribers to try outfits and only pay for what they keep. This allows customers to experience the value of the brand before committing.

Subscription-based and membership-based brands have an inherent advantage over brands that favor a one-time purchase journey. This is because they start with a much larger “surface area” to engage their customers. However, brands that don’t have a subscription or membership program–and have no intention of establishing one–can still see success by borrowing and implementing individual elements from product-led growth.

## Embracing Product-Led Growth for Your Brand

Brands looking to start with product-led growth often get discouraged by the number of initiatives they could experiment with. If you’re coming from a traditional marketing-led approach, it can be hard to adopt the right mindset. 

If you don’t know where to start, an excellent first step is to map customer touchpoints across different stages of your shopping funnel and user experience, from brand awareness to order fulfillment. There are a few ways to do this:

* Involve stakeholders from different departments to understand how they interact with the end-user, directly and indirectly.
* Talk to your most loyal (and least loyal!) customers to collect feedback on how your brand fits into their day-to-day, their current pain points, and any ideas they might have.
* Look at quantitative analytics to uncover usage and engagement patterns.

The more feedback and data you can collect, the more holistic your understanding of your brand-customer relationship will be, and the more opportunities you’ll uncover for a product-led growth transition!

Once you have your customer touchpoints mapped out, look at each critically and ask yourself the following questions:

* **How can you command a greater share of customer attention?** Many DTC brands have a transactional relationship with their customers and fail to invest in a longer-term relationship. In reality, zero-party and first-party data allow most brands to create deeper connections: what are some “aha” moments you can generate?
* **How can you deliver value before capturing value?** For a DTC brand, this often takes the form of content-driven education or offering a tool that helps customers enter your brand’s world and experience its value _before_ a traditional activation. Sneaker marketplace [The Edit](https://www.theeditldn.com/), for example, provides a free [personal shopping service.](https://www.theeditldn.com/en-eu/pages/personal-shopping-service)
* **Can you create efficiency by investing in automation?** This is often more nuanced than a simple yes-or-no answer. Any investment in automated/self-service workflows must offer a justifiable return on team efficiency or customer engagement (or both!). Generative AI, in combination with low-code/no-code platforms, creates some fantastic opportunities to automate customer service, product quizzes and similar while mainaining a human touch.
* **How can you spark virality?** Product-led companies are masters at making their products viral. eCommerce brands have many opportunities in front of them: packaging, transactional notifications, and even customer service interactions can become ways to generate positive word-of-mouth.

If you come up with an endless list of initiatives, don’t panic! Start with smaller experiments that allow you to build momentum and train your product muscle, and see how your customers respond. A strong product management foundation can help identify the biggest point of leverage.


## 5 Examples of Product-Led Initiatives for eCommerce Growth

For inspiration, here are the five most common product-led initiatives that we see eCommerce and DTC brands apply:

* **Membership programs.** While traditional loyalty programs primarily reward spending through cashback or similar models, well-designed membership programs take the brand-customer relationship one step further by leading customers to identify with the brand. This can take the form of early access to new products, in-person or virtual get-togethers, direct access to company founders, and any other perks relevant to a brand’s unique customer base.
* **Referral programs.** Happy customers love to spread the word, and referral programs allow them to do just that. It’s common for brands to reward referrals with discounts and promo codes, but we encourage brands to design a bespoke strategy that will resonate more with their customer base. If you’re a luxury brand, offering swag/products in partnership with other brands might be better than offering $20 off.
* **Try before you buy.** Allowing your customers to experience your physical product before committing to a purchase is the epitome of delivering value before capturing value and can be a great way to engage undecided customers (especially when paired with some of the other initiatives in this list!).
* **Educational content.** Content is one of the most common organic marketing efforts pursued by DTC brands, and with good reason. Producing valuable, educational content for your audience is a straightforward way to generate brand awareness by solving a genuine problem. If you go down this path, nvesting in a solid [content infrastructure](https://nebulab.com/blog/ecommerce-content-management) will pay dividends.
* **Web-based/mobile apps.** Often, brands design mobile apps that are purely transactional, making them useless to customers that have not already decided to shop with the brand. In doing so, they miss invaluable opportunities to nurture their audience through content and interactions that don't aim for a direct sale.

While these are all good starting points that will apply to many brands, you should have the courage to explore unique ideas that will make you stand out. Rolling out a successful product-led strategy requires a deep understanding of your customers, how they experience your brand, and what opportunities you can capitalize on to become an indispensable part of their daily routine.

If you need help rolling out your product-led approach, [reach out!](https://nebulab.com/contact-us) Since 2012, we’ve helped tens of DTC brands scale their presence by developing unique experiences. We’re happy to help you figure out if product-led growth is right for you and how you can get started on your product-led journey.

Scaling DTC Brands Through Product-Led Growth

In our [latest article](/blog/ecommerce-ab-testing), we wrote about the value of A/B testing (also
referred to as “split testing”) and how eCommerce brands can set up and scale an effective A/B testing strategy. By
getting the fundamentals right, brands can use A/B testing to generate unbiased, data-informed market insights. Large
brands often run tens of experiments at any given time and use the results to directly inform their product strategy.

However, we also touched briefly on the hidden complexity behind an A/B testing operation: in fact, **one of the curses
of A/B testing is its apparent simplicity.** It’s easy to get lured into signing up for an A/B testing tool without a
clear understanding of how it works, only to find ourselves weeks later with tests that are inconclusive or just plain
incorrect.

This article will dig deeper into this complexity, highlighting some of the most common pitfalls brands face when
running experiments. We will also discuss the scenarios in which A/B testing is _not_ the way to go and the alternative
practices brands should adopt instead.

## The 7 Most Common A/B Testing Mistakes

A/B testing is a form of statical hypothesis testing. As such, it is prone to all the typical pitfalls of statistical
analysis: most notably, **if the data you’re using to run the analysis is incorrect, biased, or skewed in any way, the
results of your analysis are also very likely to display the same problems**–garbage in, garbage out.

Even with modern A/B testing platforms, it is still unbelievably easy to skew the results of an A/B test. Your tools can
help you write less code and run the math for you, but they won’t be able to make strategic decisions for you–such as
which metric to pick–or to shield your test from external factors–such as a concurrent A/B test skewing your test
population.

With this in mind, let’s see some of the most common mistakes and oversights a brand might make when designing,
executing, and analyzing an A/B test.

### 1. Type I and Type II errors

All A/B tests are subject to two different types of errors:

* **Type I errors (false positives)** occur when we detect a non-existent difference between the experiment and the
  control group, i.e., we conclude that our experiment affected our target metric when it actually didn’t.
* **Type II errors (false negatives)** occur when we fail to detect a difference between the experiment and the control
  group, i.e., we conclude that our experiment failed to affect our target metric when it actually did.

We can guard our tests against Type I and Type II errors by picking an appropriate statistical significance and power:
the _higher_ our significance, the more resilient our test will be against Type I errors; the _lower_ our power, the
more resilient our test will be against Type II errors. Combined, these numbers represent our desired confidence in the
test result.

As an example, if we pick a significance of 80% and a power of 5%, it means that:

* There’s only a 20% probability that our experiment group will outperform our control group by chance.
* There’s only a 5% probability that our control group will outperform our experiment group by chance.

**Higher confidence levels mean we can take more drastic actions as a result of our test but also require larger test
populations,** which may be challenging to reach for smaller brands.

An 80% significance and a 5% power is a good starting point for most A/B tests, but you should use your judgment: in
some tests, the cost of making a mistake in either direction might be higher or lower than usual; in others, you might
only want to worry about false positives or false negatives.

### 2. Novelty Effect and Change Aversion

Two interesting dynamics can threaten the validity of A/B tests, and they both stem from how your users react to change.
These are more relevant for products with a high percentage of returning users, so you should be extra careful if your
brand offers subscriptions, for instance, as they could prevent you from getting reliable results.

**The Novelty Effect occurs when users engage with a product more than usual because they’re fascinated by a new feature
or a change in an existing feature.** For instance, users might purchase more if you introduce a brand-new loyalty
program to drive incremental recurring revenue. However, as they adapt to the change, most people will gradually regress
to the average user behavior patterns.

Change Aversion is the exact opposite and often happens with significant redesigns of existing functionality: **you
might find that affectionate users engage less with your experiment,** as they were used to the previous way of doing
things and don’t like that their workflows got disrupted. Over time, they will likely get used to the new design and
re-engage with your brand as usual.

For both problems, **the solution is to segment your A/B test results by new and existing users.** By analyzing the
performance of these two buckets separately, you can more easily isolate the effect of your experiment from the effect
induced by novelty/change aversion. Of course, you'll have to figure out what counts as a “new” or “existing” user, and
it might differ depending on the test you’re running.

### 3. Network Effect

**The Network Effect occurs when users of an A/B test influence each other,** effectively “leaking” outside the bucket
they belong to (i.e., the control group interacts with the experiment, or the experiment group reverts to control). This
most often occurs with “social” functionality, where users interact with each other directly.

Consider the case of a second-hand marketplace that wants to offer users the ability to exchange goods directly without
a cash transaction. By definition, such a feature will require two users to participate: the sender and the receiver.
One quick solution would be to assign users randomly to the experiment or control group and only allow users in the
experiment group to use the new functionality. However, this might lead to a sub-par UX, causing frustration and skewing
the test results.

Instead, **you should cluster users into groups, with all users in the same group being more likely to interact with
each other.** For instance, you might cluster users by state, assuming that most of your marketplace’s transactions
happen within the same state. You would then assign each state to the control or experiment group. Users might still
interact across different states, influencing each other, but clustering helps minimize the likelihood of spillover.

The Network Effect is relatively uncommon in eCommerce, but it’s still worth understanding if your brand allows users to
interact with each other (as in the case of a marketplace). It can lead to unexpected results and can be incredibly
sneaky to detect. As a rule of thumb, you should always consider whether any tests you’re designing have the potential
to introduce a network effect and plan accordingly.

### 4. History Effect

**The History Effect occurs when an event in the outside world affects the results of your A/B test.** This can be
anything from a marketing campaign to a shopping holiday. These events are likely to change the behavioral patterns of
your visitors compared to the average, which will cause you to extrapolate incorrect insights.

For example, let’s assume you’re testing whether offering a discount in exchange for newsletter signups significantly
increases subscribers. If you decide to run the test during a site-wide sale (e.g., because of Black Friday/Cyber
Monday), your test might be inconclusive.

**To mitigate the History Effect, ensure you have solid processes to ensure you’re not running A/B tests concurrently
with major media coverage and PR events.** It also helps to run your A/B tests for at least two complete business
cycles, which typically translates into 2-4 weeks for most brands.

### 5. Simpson’s Paradox

**Simpson’s Paradox is a particularly sneaky type of error that occurs whenever you inadvertently introduce weighted
averages into an A/B test,** which can happen in a few cases:

* **When you adjust the traffic split for an A/B test during test execution.** Example: you start your test at 50%/50%,
  then change it to 90%/10% when you see that the experiment is outperforming the control.
* **When your website traffic segmentation is not consistent across different variants of your test.** Example: you
  segment your test by returning vs. new visitors, but you have 1,000 new customers visit the experiment, and only 500
  visit the control.

In these scenarios, you might find that any correlation identified in individual test segments disappears or is inverted
in your aggregate results. This is because the aggregate result calculation effectively becomes a weighted average, and
your larger segments will “overwhelm” the smaller ones.

You can find a more academic explanation of Simpson’s paradox
[here](https://learn.microsoft.com/en-us/archive/msdn-magazine/2005/december/test-run-software-testing-paradoxes), but
there are a few things you can do to prevent Simpson’s Paradox from finding its way into your test results:

* **Ensuring your test populations are accurately randomized** and evenly distributed among different test variants. In
  other words, you should have the same number of visitors from each segment to each variant of your test.
* **Running an A/A test** (i.e., a “fake” A/B test where both variants are the same) can be helpful to ensure proper
  randomization.
* **Looking at test results across different segments** ensuring and not just at the aggregates so that you can spot any
  inconsistencies.
* **Relying on your A/B platform’s built-in capabilities,** e.g., Optimizely’s
  [Stats Accelerator](https://support.optimizely.com/hc/en-us/articles/5326213705101-Stats-Accelerator-Acceleration-Under-Time-Varying-Signals),
  to make your tests more resilient to Simpson’s Paradox.

### 6. P-hacking

P-hacking is an extremely common A/B testing pitfall–so much so that it used to be _encouraged_ by A/B testing tools
such as Optimizely. Simply put, **P-hacking is the practice of changing a test’s original parameters to reach a
pre-determined conclusion.** This can come in different forms:

* Some teams stop a test as soon as it reaches statistical significance rather than letting it run for its established
  duration.
* Some teams increase the test population until they reach statistical significance.

**P-hacking is often caused by pressure from your leadership or digital marketing team** to get a specific result from an
A/B test or to maximize the impact of a successful experiment as quickly as possible. Unfortunately, this is not how
traditional A/B testing works: once you have established your sample size, you simply need to let your run test run its
course and evaluate the results only at the end.

Because this methodology isn’t particularly well-suited to the speed at which startups typically move, many A/B testing
platforms ended up implementing alternative algorithms. Optimizely, for instance, introduced Stats Engine in 2015, which
allows A/B testers to peek at test results without the risk of taking action prematurely–you can learn more about how it
works in
[this introductory article](https://www.optimizely.com/insights/blog/statistics-for-the-internet-age-the-story-behind-optimizelys-new-stats-engine/)
by the Optimizely team.

While features such as Optimizely’s Stats Engine or VWO’s Bayesian engine help A/B testing teams avoid pitfalls such as
P-hacking, they don’t eliminate the need for proper test planning.

### 7. Instrumentation Effect

While most pitfalls outlined in this article are statistical, the Instrumentation Effect is much simpler: **it occurs
when your analytics, A/B testing infrastructure, or test implementation don’t work correctly**, skewing test results.
Here are a few examples:

* Your analytics setup sometimes reports conversions twice, skewing the total number of conversions you use to evaluate
  your test results.
* Your A/B testing setup sometimes puts the same customer in different buckets throughout the same A/B test.
* One of your test variants has a user experience edge case that prevents specific customers from completing their
  interaction with the webpage.

Because these bugs happen at the very source of your data, no statistical methods can solve any of these problems for
you. Instead, you need to regularly and rigorously test every part of your A/B testing infrastructure:

* Before running an experiment, **make sure that all the metrics you are interested in are being reported accurately.**
* **Test your experiments for usability and integrity** with the same attention you’d reserve to a permanent feature.
* **Run A/A tests to verify that your A/B testing setup works correctly** and that your upstream data collection is
  reliable.

## A/B Testing Is Not a One-Size-Fits-All Methodology

Reading this article, you might think that we want to discourage eCommerce brands from running A/B testing, and you’d
be–at least partially–correct. It’s not that A/B testing doesn’t have its place in an eCommerce brand’s strategy. But
**the effort involved in planning, executing, and analyzing an A/B test, assuming that you want to get significant
results, is often not sustainable by most early-stage brands.**

Anyone not adequately trained in the statistical techniques behind A/B testing will have a tough time guarding their
tests against the pitfalls we’ve outlined–which, by the way, are a subset of all the different statistical and practical
errors A/B testers can incur. Tools can help mitigate some of these errors to an extent, but they can’t turn an
inexperienced team into data analysis experts overnight.

Furthermore, **A/B testing is very often not the best research methodology.** There are many scenarios in which A/B
testing falls short.

* First and foremost: **A/B testing, by its very nature, is focused on short-term gains.** As we have seen, these can
  sometimes come at the expense of long-term business results: focusing on click-through and conversion rates might come
  at the expense of retention and customer LTV.
* As a result of the above: **A/B testing tends to deprioritize disruptive innovation,** leading teams to focus on
  incremental gains instead—its typical application is in conversion-rate optimization. If your brand is working on a
  massive product launch, or a brand new membership program, A/B testing will not offer any meaningful insights into how
  your customers might respond.
* **The cost of designing and launching an A/B test is high:** every test you run effectively creates two “branches” in
  your digital experience. This means your team will have to tiptoe around your A/B test until it’s run its entire
  course, slowing down further innovation and improvements in that product area.
* **A/B testing is a quantitative and evaluative research methodology.** As such, it needs an initial hypothesis to
  validate–and one that can be clearly and unequivocally validated with a data-driven approach. Anything requiring
  generative or qualitative research, which often involves talking to your customers, is not a good fit for A/B testing.

## Start A/B Testing When You Have Product Management Fundamentals

So, are we suggesting that retailers shouldn’t A/B test? Not at all: A/B testing has its place, and when employed
correctly, it can be instrumental in improving a business’s KPIs and bottom line. To dismiss A/B testing as too
complicated to be worth the effort would be incredibly short-sighted and detrimental.

However, **many eCommerce businesses dive head-first into A/B testing without proper product management fundamentals.**
We’re talking about generative and evaluative research methodologies such as heatmaps, user interviews, user testing,
on-site surveys, session replays, historical analytics, feature flags, and many other techniques.

These practices have a broader set of potential use cases and are also a prerequisite for being able to A/B test
intentionally. Plus, they’re almost always _simpler_ to implement and leverage than an effective A/B testing
infrastructure!

The next time you–or someone else on your team–want to run an A/B test, stop for a moment and ask yourself: **is there a
more straightforward, more efficient way to answer this question/validate this testing hypothesis?** You might be
surprised about how many alternatives you have at your disposal.

In the next few weeks, we’ll be exploring precisely these product fundamentals, explaining when and how they are best
used, and how you can orchestrate them together to form the basis of a strong product management practice for your
eCommerce brand.

How to Make Your eCommerce A/B Tests Useless

eCommerce brands have a variety of techniques at their disposal for understanding their customers and optimizing their KPIs: most of these techniques have been borrowed from modern product management practices and can be directly applied to eCommerce–A/B testing is one of these.

A/B testing (sometimes referred to as “split testing”) is a technique that uses statistical algorithms to determine which variation of a given behavior is most likely to optimize for a predetermined quantitative outcome. **By using live traffic to analyze user behavior, A/B testing can provide an objective, data-driven answer to nebulous questions**, such as whether offering free shipping to your customers will significantly affect add-to-cart rates.

With that said, **A/B testing is one of many techniques brands can employ and arguably one of the most complicated to execute correctly**. Unfortunately, we often see brand founders and executives jump into A/B testing without clearly understanding the required skillset or effort, only to arrive at disappointing conclusions and abandon the practice altogether.

We firmly believe that A/B testing has its place. Still, we also have strong opinions about when it should and shouldn’t be employed and the requirements for running a successful A/B testing operation. In this guide, we will break down some of the complexity behind A/B testing, help you understand whether it’s a good option for your brand, and offer practical tips and considerations for rolling it out.

## A Primer on How A/B Testing Works

At its core, A/B testing is a simple concept: we design two variations of the same web page (version A and version B), distribute our traffic evenly among the two variations, then see which one reported the best performance.

Going back to the “free shipping” example, an online store could show a free shipping banner to half their customers (this is the experiment group) and use the original page for the other half (this is the control group). At the end of the test, whatever version got the highest conversion rate would be considered a winner (with some statistical caveats).

This is the simplest possible type of A/B testing, but there are a few alternatives:

* **In A/B/n testing**, more than two variations are tested (e.g., no free shipping indication vs. a free shipping banner vs. a free shipping popup).
* **In multivariate testing**, each variation alters multiple elements on the page (e.g., a free shipping banner and a popup, just the banner, just the popup, no free shipping indication).
* **In bandit testing**, more and more traffic is automatically directed to the winning variation (e.g., if the free shipping banner converts more than the original page, we automatically start showing it more and more often).

![MailChimp A/B testing](https://cdn.sanity.io/images/kqgmf0g2/production/9c659965ed7f4ff19fa622d3e58e7b19fc883353-2560x2512.png?fit=clip)

It’s important to note that while this article talks specifically about A/B testing web pages, the technique can be applied—at least to an extent—to email marketing, social media, and other types of digital properties. Furthermore, eCommerce platforms, marketplaces, and marketing tools often provide native A/B testing capabilities:

* Amazon allows its merchants to A/B test content such as product images
* Hubspot allows testing landing page templates and email campaigns.
* Google Ads allows testing different ads to optimize clickthrough rates.

These concepts are deceptively simple, which is why so many A/B testing platforms promise exceptional ROI and quick results. **In reality, A/B testing comes with many pitfalls and “gotchas” that practitioners should understand thoroughly**. Running an effective A/B testing operation on an eCommerce website requires significant preparation, effort, and discipline.

## What an Effective A/B Testing Process Looks Like

Let’s take a peek into what a successful, effective A/B testing operation looks like in detail.

### 1. Picking a Problem Space

There are many resources online advertising “the best 99 A/B testing ideas,” and it might be tempting for inexperienced executives to jump in and start testing. Unfortunately, these blanket experiments are unlikely to move the needle–in some cases, they might even be detrimental to a brand’s KPIs. Furthermore, they completely ignore the true purpose of A/B testing, which is not conversion rate optimization but refining our understanding of our market, business, and customers.

For this reason, before diving head-first into changing the color of our CTA buttons, we must understand the problem space we want to explore. This can be a very narrow product opportunity, such as whether a new version of the search functionality will lead to better recommendations, or a broader area of the business that we want to understand better, such as how we can improve our conversion rates during the checkout process.

![POA template](https://cdn.sanity.io/images/kqgmf0g2/production/a7dfcc5783e16119a81831284987627f9fcb093f-2560x2512.webp?fit=clip)

Whatever the case, **we need to articulate precisely what questions we want to answer and why we think an A/B test is the correct methodology to answer them**. This is most often done through a Product Opportunity Assessment (POA), a document that explains what problem we’re solving and for whom, analyzes the market and the problem space, any business- and customer-facing implications of solving the problem, and provides a final recommendation (go/no-go) based on the information we have collected.

A/B testing, when employed correctly, is an invaluable tool that allows eCommerce product managers to quantitatively confirm or disprove existing hypotheses related to a particular opportunity. Still, it’s not a replacement for picking and refining an opportunity in the first place. Unfortunately, brand executives and marketers sometimes fall into the trap of running mindlessly through a list of standard A/B tests, leading to short-term, black-box optimizations at best and a waste of resources.

### 2. Designing the Test

Once the opportunity is well understood and prioritized, it’s time to design the A/B test. The first step is formulating a hypothesis that we want to confirm or disproven, which usually looks like this:

> We think that, by [experiment], we can increase [metric] by an [absolute/relative] [Minimum Detectable Effect]%.

Stating the hypothesis is the first step, but we will need a few more elements before launching our experiment. In particular, we need to establish the following:

* **Our current conversion rate**. If we don’t have it, this is a good time to stop and put the proper instrumentation in place.
* **The desired statistical power**. This indicates how likely we are to detect a difference between the control and the experiment, assuming it exists. Higher values give us more confidence in the test results but require larger samples.
* **The desired significance level**. This indicates how likely we are to detect a difference between the control and the experiment when it doesn’t exist. Lower values give us more confidence in the test results but require larger samples.

![Evan Miller's A/B testing sample size calculator](https://cdn.sanity.io/images/kqgmf0g2/production/e69b939e8d2e7df81f552d122ff5d4ffd00fba9e-2560x1534.webp?fit=clip)

Once we have established these parameters, we can plug them into one of many A/B test sample size calculators–Evan Miller’s is arguably the most popular, but any popular A/B testing platform (e.g., **LaunchDarkly**, **Optimizely**, **VWO**) will also do the math for you.

Finally, we must decide how long we want to run our test. **It’s usually recommended to run tests for at least two complete business cycles**. This helps to protect our experiment from a few common pitfalls, such as external validity threats, novelty/maturation effects, and change aversion. We’ll cover these in a separate article!

### 3. Launching the Experiment

The next step is to work with our product team to design and implement our experiment.

Most modern A/B testing platforms allow non-technical users to launch tests with complete autonomy. After copy-pasting a snippet of JavaScript code to their website, brand operators can design and launch experiments that alter UI elements without touching a single line of code: users can alter page layout, CTA styles, and other elements. This capability allows product managers and stakeholders to move quickly and saves engineering bandwidth for high-impact product work. However, it also comes with some caveats of which users are typically unaware.

First of all, **experiments that have been implemented without the assistance of a product team might present bugs or inconsistencies that make them less palatable to new and existing brand customers**. This might skew our test results: what if our latest category page experiment failed to outperform the control because the customer experience was counterintuitive? In some cases, such pitfalls can completely invalidate an experiment, resulting in a waste of resources.

![Optimizely homepage](https://cdn.sanity.io/images/kqgmf0g2/production/f8e3234adf8087323de71b30402eb2e6bdc7133e-2560x1702.webp?fit=clip)

Secondly, it’s not uncommon for a large brand to run tens of experiments simultaneously. **When experiments run in the browser, they can have significant performance implications and harm the user experience**.

**Brands should invest in A/B testing infrastructure that allows them to design and launch experiments quickly while continuing to involve the product team.** This often comes in the form of streamlined processes, strong design/development primitives–such as a reusable design system and UI kit–and clear guidelines on what is considered “good enough” for an experiment.

### 4. Analyzing Test Results

Once the test is completed, it’s time to analyze the results. Typically, there are two questions we’re looking to answer for each A/B test.

**The first and most obvious is whether the experiment outperformed the control**–or, in other words, whether our hypothesis was confirmed or disproven by the test. If the experiment outperforms the control, we can say that our hypothesis is correct–within the established levels of statistical confidence, of course. We might also find that the control outperformed the experiment or that the test was inconclusive (i.e., neither variation significantly outperformed the other, and no winner can be established).

**The second question is what secondary implications the test had on our experiment population.** This might involve looking at other metrics or specific customer segments to understand whether the test affected our customers in non-immediate ways. Here are a couple of examples:

* A test might tell us that offering 50% off on the first order of our subscription product increases our subscription activation rate. However, when we look at secondary metrics, we might find that the experiment group also has a much lower retention rate than the control group, causing us to lose money on every conversion. Is this outcome desirable for our eCommerce business?

* A test might be inconclusive or present the control group as a global winner. However, when we dive into the data, we might find that the experiment blatantly outperformed the control for specific segments. Sometimes, this might indicate an opportunity to offer a more [personalized experience](https://nebulab.com/blog/ecommerce-personalization).

![LaunchDarkly metrics view](https://cdn.sanity.io/images/kqgmf0g2/production/35a9f4a0a28701605f61c311163f4345cd12eec8-1200x857.webp?fit=clip)

As a rule of thumb, it can be helpful to map some of these secondary considerations when designing the test. However, this analysis is very exploratory, and it’s impossible to list all the possible outcomes in advance. **Brands should take the time to review test results with different stakeholders so that everyone can bring their KPIs and perspective to the table.**

Once we have finished analyzing the results, it’s time to archive them. This is a step that most practitioners skip, but it’s incredibly valuable: **archiving the results of our A/B tests allows us to establish them as a first-class research methodology** rather than a short-term approach to conversion optimization. Ideally, insights derived from A/B tests should systematically become part of our organizational knowledge.

### 5. (Bonus) Re-Testing as Needed

What’s true today might not be true tomorrow. When they first embrace a product-led approach to brand growth, we see many founders make the mistake of thinking that any insights they have uncovered are set in stone and will never need to be revisited. In reality, markets change constantly, and so does our business.

We don’t encourage re-running A/B tests for the simple sake of it–in most situations, the effort of re-testing far outweighs the benefits. However, **practitioners should feel comfortable re-running an old test or testing old assumptions in novel ways**.

## An End-to-End A/B Test Example

Let’s see what an end-to-end A/B test execution might look like by following the process we have just outlined:

1. As we’ve mentioned, it all starts with identifying a problem space we want to explore. In this case, let’s assume we are trying to understand why our product page has a particularly high bounce rate. We draft a Product Opportunity Assessment, collecting all the information we have gathered.

2. We collect a few hypotheses about why this might be happening, based on what we know about the business and our customers: maybe visitors can’t find critical information in the product description; maybe the particular variant they are looking for is out of stock; or maybe they aren’t willing to pay shipping costs.

3. We start by testing the shipping cost hypothesis. After looking at Google Analytics, we conclude that increasing add-to-cart rates by an absolute 10% would be considered a win.

4. Because offering free shipping comes at a considerable cost, we want to be very confident about our test results, so we pick a 95% statistical power and a 10% significance level for our test.

5. We plug the numbers into our favorite A/B test sample size calculator, which tells us we’ll need to direct 858 visitors to each branch of our test. Based on our traffic patterns, we think we can accomplish this in two weeks.

6. We work with our design and engineering team to implement an experiment that offers customers free shipping and displays the offer prominently on the product page.

7. We use our favorite A/B testing tool to launch the test.

8. After two weeks, we look at the results of the test. It turns out that the test was overall inconclusive: offering free shipping did not significantly affect the add-to-cart rate. However, when we look into the results across different segments, we find that the experiment group outperformed the control group for products over $150.

9. We archive the results of the test and refine our POA accordingly.

![LaunchDarkly results view](https://cdn.sanity.io/images/kqgmf0g2/production/bb3782925740b9b70a5349429382e14e496c3113-1200x860.png?fit=clip)

Now that we have the results of the test, we may decide to:

* Offer free shipping to everyone. Our business might have a policy of offering the same shipping conditions to everyone.

* Offer free shipping only on products over $150 to maximize our ROI. We might even play with different placements of the free shipping indication: what if we included the banner in the shopping cart or checkout page?

* Run another A/B test on a more promising target audience. We could test free shipping for products over $150, to be confident that it will actually move the needle for that segment.

* Collect more information through alternative product research methodologies like user testing or heatmaps.

* Not do anything: a more detailed evaluation might find that free shipping is not the highest-impact solution to this problem (e.g., perhaps we’d be better off adding testimonials to the product page or offering an upsell).

As we’ve mentioned, **an A/B test doesn’t have to lead to immediate action**. By treating our tests as a source of insights rather than a mere CRO methodology, we can deepen our knowledge of our customers with statistically verifiable, data-driven information that will enrich our product strategy and ultimately increase our bottom line.

## What Comes Next

We could talk for ages about the pros, cons, prerequisites, and implications of A/B testing, but this is more than enough for a single article.

**In the following articles, we’ll explore the most common pitfalls operators, marketers, and product managers incur when setting up and executing an A/B testing strategy**. We’ll talk about statistical significance, the shortcomings of using A/B testing as your only source of product knowledge, and what alternative strategies brands must employ before considering running an A/B testing operation.

In the meantime, if you have any questions about any topics we have discussed or need help designing an A/B testing strategy for your eCommerce site, [don’t hesitate to reach out!](https://nebulab.com/contact-us)

eCommerce A/B Testing: Strategies for Success

While the total penetration of headless commerce in the US is still low, we have witnessed an increase in headless builds in recent years. And even though [we don’t believe headless is the end-all solution to all eCommerce problems](https://nebulab.com/blog/headless-commerce), we think it’s about time the industry realized the power of this architecture and started using it responsibly.

The only way for Shopify users to go headless for a long time was to implement their own single-page application from scratch, using a JavaScript library such as React (and hopefully a framework, such as Next.js). Shopify does provide SDKs to make the API integration work slightly more manageable, but that was about it: until recently, there was very little being done for performance, state management, personalization, or SEO.

In November 2021, Shopify released a developer preview of [Hydrogen](https://hydrogen.shopify.dev/), their new framework meant to address precisely these problems. Here at Nebulab, we’ve recently had the chance to take Hydrogen for a spin, and we want to share our learnings.

## What is Hydrogen?

Hydrogen is an open-source, React-based framework that can be used to build custom storefronts on the Shopify platform. It provides a lot of useful functionality:

* A sensible structure for your application code.

* eCommerce-specific components and pages.

* An SDK and hooks to interact with Shopify’s GraphQL-based [Storefront API](https://shopify.dev/api/storefront).

* It uses [Tailwind CSS](https://tailwindcss.com/) for styling.

* It’s optimized for high-performance builds.

Combined, these features almost entirely eliminate the amount of boilerplate code engineers need to write to build their storefronts and allow engineers to spend most of their time on functionality that truly moves the needle.

![Shopify Hydrogen website](https://cdn.sanity.io/images/kqgmf0g2/production/9865cb4da04e7cd861e38c97cc7015d8ea7a1e22-2560x2512.png?fit=clip)

## What are the pros of using Hydrogen?

For Shopify stores that want to go headless, Hydrogen is a huge help. As we’ve mentioned, it abstracts away all the complexity of integrating with the Shopify API and developing your storefront’s skeleton: all you need to do is clone the Hydrogen [demo store](https://shopify.dev/custom-storefronts/hydrogen/getting-started/templates#demo-store-template), configure it, then focus on styling and custom functionality. This alone is a huge productivity boost, and the developer experience has been outstanding in our experiments.

It’s also important to note that Hydrogen is officially developed and supported by Shopify, which should provide peace of mind to brands looking for a stable, robust solution that’s not going anywhere. While some Shopify-specific Next.js templates already existed before Hydrogen, they were mostly small, community-sponsored projects, which made them unsuitable for enterprise use.

## What are the cons of using Hydrogen?

Seasoned developers know that productivity comes with a hidden cost: the more a tool tries to abstract away technical complexity, the more opinionated it is. When it comes to Hydrogen, it’s exceptionally opinionated:

* It uses React server components.

* It uses streaming server-side rendering (SSR).

* It has built-in components, hooks, and caching mechanisms.

* It’s heavily coupled with Shopify’s Storefront API.

* If you need complete control over your React application, Hydrogen is most likely not a good candidate for you.

You should also know that some of these features are bleeding edge in the React ecosystem, and because Hydrogen is tightly coupled with Shopify, the community around it is still relatively small. Hydrogen brands can expect to have a harder time finding online resources and support, and they will have to tap into a smaller pool of agencies and engineers than if they used a more popular framework. That said, much of that will likely change as Hydrogen gains popularity.

Finally, most Shopify Apps and plugins were built to integrate with Liquid templates and won’t be compatible with Hydrogen (or other headless architectures, for that matter). If you rely heavily on specific apps and your app doesn’t provide an API your engineers can integrate with, you’ll need to switch to a headless-friendly alternative or build the functionality yourself.

## Can Hydrogen be used without Shopify Plus?

It’s complicated. The official answer is yes: since Hydrogen is an open-source project whose source code is available to anyone, and because none of the API endpoints it uses are restricted to Shopify Plus customers, at first glance, the framework can be used without Shopify Plus.

In reality, things are more nuanced.

![Shopify Oxygen documentation](https://cdn.sanity.io/images/kqgmf0g2/production/a893aabf4ed6f21f992598100837e4856353c79f-2560x1620.png?fit=clip)

First of all, there’s the matter of hosting your Hydrogen application. Plus users can host their Hydrogen app on [Shopify Oxygen](https://shopify.dev/custom-storefronts/oxygenhttps://shopify.dev/custom-storefronts/oxygen), Shopify’s proprietary hosting solution, which provides native support for directing users who visit your Shopify domain to your Hydrogen storefront.

If you don’t use Shopify Plus, you should still be able to deploy your Hydrogen app to other platforms, but it won’t be a painless experience. For example, Vercel has published a blog post announcing the availability of a [zero-configuration deployment for Hydrogen](https://vercel.com/changelog/hydrogen-projects-can-now-be-deployed-with-zero-configuration). But when we tested it, we had to manually tweak the environment variables used by Hydrogen to match the names used by Oxygen. This was a minor inconvenience overall, but it shows how support for Hydrogen is still in its infancy.

Once you’ve figured out the hosting, it’s time to deal with domains and DNS records. If you manage your domains through Shopify, Shopify will attempt to redirect any requests to your [myshopify.com](http://myshopify.com/) domain to your Hydrogen storefront, which will completely break the checkout. This could be easily solved by using a [routing domain](https://help.shopify.com/en/manual/domains/domain-type/routing-domain) which, ironically, is only available to Shopify Plus customers. (When we reached out to Shopify engineering about this catch-22-esque paradox, they seemed utterly taken aback by this realization.) The current solution is to set up your Liquid storefront to redirect to your Hydrogen storefront via JavaScript, which is far from ideal.

Another aspect to consider is support: in our experience, if you’re not a Shopify Plus customer, you’re only going to get extremely limited technical support from Shopify, with most requests being redirected to Shopify’s Discord community. No one in the Discord community seems to have much experience with Hydrogen, except for Shopify’s own employees, whose presence in the community is very erratic.

To be fair, Shopify did tell us that they are working to address these challenges and make Hydrogen truly available to non-Plus customers. However, _when_ this will happen exactly is unknown.

To sum it up, if you’re not on Shopify Plus and don’t have an experienced engineering team or agency backing you up, we would advise against using Hydrogen right now: you run the risk of spending weeks or months of development only to run into deal-breaking roadblocks.

![Shopify Discord](https://cdn.sanity.io/images/kqgmf0g2/production/ce5f2c4f4565702286b8a1df6cd0d17d418c8538-2560x2068.png?w=450)

## Who should be using Hydrogen?

Shopify’s Hydrogen framework is the perfect solution for online stores that are already running headless, or are planning to go headless, can count on a robust engineering team, and want to lean into a powerful, officially-supported solution that will spare them from boilerplate work. The developer experience is enjoyable, and most React engineers will be more than happy to work on a Hydrogen app.

If, on the other hand, you don’t have an experienced engineering team or don’t want to deal with the complexity that comes with a headless solution, Hydrogen is not a good choice: as much as it simplifies things, it will still require much more upkeep than a Liquid-based Shopify build.

At this point, Hydrogen is also a less-than-ideal solution for brands not on Shopify Plus due to missing functionality and limited support from Shopify. That is likely to change as the framework matures and becomes more widely available, so keep an eye out for any announcements from Shopify (and from us!).

If you’re considering going headless with Hydrogen or other frameworks, and are unsure of the implications, [feel free to reach out](https://nebulab.com/contact-us)! Our team has extensive experience and will help you make the choice that best supports your digital strategy.

Everything You Need to Know About Shopify Hydrogen

With unlimited digital estate, DTC brands can tell their stories, announce, entertain, educate, and keep their name front and center in customers’ minds, all of which is very hard to do in the limited shelf space afforded by wholesale partners. Now that even die-hard digitally-native brands are going omnichannel in pursuit of reach and profitability, **it’s paramount to double down on the exclusive capabilities of each channel to create complementary experiences—for DTC, content is one of the most obvious levers to pull**.

A slew of platforms has emerged over the past decade that promise to offer the definitive solution to all content management pains. These solutions provide functionality that covers the entire content management lifecycle, from writing to publishing, presentation, and SEO, but they do it in very different ways. In fact, we’ve seen at least four generations of content management tooling emerge in these years, all of which coexist today, serving different use cases and market segments.

For eCommerce operators, finding a North star to follow in this vastness can be tricky, and engineering teams are usually not much better off. If you haven’t been exposed to all these tools and understand the nuances of each, it’s easy to get lured in by the promise of unlimited flexibility and endless automation, only to find yourself stuck with a complex piece of software that requires weeks of integration work, or doesn’t play nice with your editorial workflow.

In this article, we will provide an overview of the content management space, as seen from the perspective of an eCommerce brand. You will learn the different types of tools you can use, their advantages and limitations, and some practical recommendations about which tool to adopt depending on your goal, use case, and growth stage. The goal is to equip you with all the knowledge you need to make an informed decision for your eCommerce brand.

## The four generations of content management

Different content management solutions have been launched in the last twenty years. While there’s no universal taxonomy, we can classify them under four categories: monolithic CMSs, headless CMSs, site builders, and page builders. In the following paragraphs, we’ll explore these different solutions, their pros and cons, and the leading players in each space.

### Monolithic CMSs

Monolithic CMSs were the first content management solution to emerge in the market. **These tools take over your entire content experience by handling every single aspect**: they provide an interface that allows you to edit and publish your content and present the content to your audience via a public-facing website.

You’ve probably heard about [WordPress](https://wordpress.org/), arguably the most popular CMS in the world. Initially built for blogging, WordPress has been expanded over the years to encompass an incredible number of use cases, allowing you to publish blogs, entire websites, and even online stores. It’s estimated that WordPress powers over 43% of all websites.

![WordPress website](https://cdn.sanity.io/images/kqgmf0g2/production/908b2d57d03ec193412387c8af8d151b6df2393b-2560x2136.png?fit=clip)

**Monolithic CMSs have been around for over fifteen years, and they’ve stood the test of time**. They offer a user-friendly, streamlined content editing experience and many plugins and apps that make them easy to repurpose for different use cases. Since they have a considerable market share, they also boast rich ecosystems of system integrators.

However, they also come with some limitations: th**ese content management systems are meant for content-centric websites, not for eCommerce brands.** They usually take over your entire website and don’t play nice with other parts of your tech stack. This makes them a less-than-ideal choice for a DTC brand, which needs to present the content on their storefront or even enrich it with dynamic data such as product information.

### Headless CMSs

Headless CMSs were built to solve precisely these limitations. **Compared to monolithic solutions, headless CMSs are much more nimble**: a headless CMS only provides an interface to define your content structure and manage your content. Presenting your content is up to you, and typically requires a design and engineering team to integrate the headless CMS into your existing frontend.

[Contentful](https://contentful.com/) has long been the primary contender in the headless CMS space, but many competitors have launched their solutions over the years: [Prismic](https://prismic.app/), [DatoCMS](https://datocms.com/), and [Sanity](https://sanity.io/) are among the most popular alternatives, offering slightly different functionality such as custom editorial workflows/UIs and internationalization, and wildly different price points.

![Contentful homepage](https://cdn.sanity.io/images/kqgmf0g2/production/551c31bc9b5a60be3f663151ba0172e06f790da5-2560x1810.png?fit=fill)

**Headless CMSs enable brands to treat content as data, making it fully portable and allowing them to present their content wherever they want**. Most headless CMSs also connect to your backend: for instance, an eCommerce brand will be able to pull product and pricing information from Shopify and include it right in the middle of an editorial piece. Such functionality allows you to blur the lines between content and commerce, making for a much richer customer experience.

Headless CMSs are still very popular thanks to their flexibility, but they come with their own limitations: mainly, **they require design and engineering work when marketers want to integrate new types of content into a company’s existing digital properties, or whenever they want to make visual changes to the existing experience**. This may be problematic for brands operating with low budgets or in high-velocity environments.

### Site builders

While site builders are not technically new (Webflow was born in 2012, one year before Contentful), they’ve seen a recent surge in popularity. You can think of site builders as a modern take on monolithic CMSs: like monolithic solutions, site builders also take over your entire website. However, **they offer more modern and advanced functionality**, such as support for drag-and-drop editing across your entire website, responsive design, custom code, API access, and integrations with popular third-party design and development tools. Almost all site builders also include a native headless CMS, or will optionally integrate with a third-party headless CMS.

The most popular site builder is [Webflow](https://webflow.com/), which is often used by SaaS companies to manage their marketing websites, or by eCommerce brands to spin up self-contained editorial experiences. Webflow also allows creating gated content and building Zapier-esque workflow automation. A recent competitor is [Framer](https://framer.com/), which allows designers to build, test and publish websites straight from Figma wireframes.

![Webflow homepage](https://cdn.sanity.io/images/kqgmf0g2/production/0082e34207f075b05269676c871a8d0d49b1601a-2560x2034.png?fit=fill)

**If your team has limited technical experience or you don’t want to use your engineering bandwidth, using a site builder can be an excellent way to quickly spin up new editorial experiences that will meet the standards of the modern Web.** And if you need to implement deeper customizations or complicated workflows, your engineers can still make those adjustments at a reasonable pace.

The main disadvantage with site builders is that, for all their flexibility, **they will still be pretty self-contained, and they’re hard to integrate with other digital properties.** Maintaining brand consistency will also be more challenging since your design system will live both in your site builder and across any other frontends you have. This is fine if you’re building a standalone experience such as a microsite, but can hurt your brand reputation when used for integrated experiences such as a company blog.

### Page builders

After coming full circle with site builders, the content management pendulum is swinging the other way once again with page builders. Like site builders, page builders also offer drag-and-drop editing, built-in headless CMSs, and all the modern developer tooling you might expect. **Unlike page builders, site builders try to be non-obtrusive: rather than taking over your entire website, they allow you to model specific pages or sections of it.** For example, you might let your eCommerce platform power your product catalog and checkout experience while your page builder powers your site navigation and blog.

The most popular page builder is [Builder.io](http://builder.io/), used by several eCommerce brands such as Everlane, J.Crew, and Harry’s. It allows you to use their native visual editor or to import your own components, connects with several third-party data sources, offers built-in A/B testing, and lets you publish your content across the Web, mobile, and wherever else you want. An early-stage contender is [Plasmic](https://plasmic.app/), which offers a very similar feature set, but pairs it with a more modern UX.

![Builder.io homepage](https://cdn.sanity.io/images/kqgmf0g2/production/29e681ed0cb541c8da8d8c65119b10cfe3f08b8d-2560x1528.png?fit=clip)

**Page builders can be an excellent compromise between headless CMSs and site builders**: they allow you to easily integrate your content into your existing digital properties in a non-obtrusive, brand-consistent way, and they offer a visual interface that helps non-technical stakeholders to quickly iterate on both content and presentation.

However, they are not a one-size-fits-all solution. **While an essential page builder integration can be a reasonably low-effort process for your engineering team, getting page builders right can be a complex initiative.** Once you start bringing in your own UI components, performing search engine optimization, running A/B tests, integrating with external data sources, or implementing rich user interactions, the level of complexity goes up dramatically, and keeping everything running smoothly can be a challenge.

## Evaluating the right solution for your brand

As you’ve seen, there’s no easy answer: each eCommerce CMS has its pros and cons, which you should weigh against your company’s stage, requirements, and technical savviness. The three main factors we keep into account when picking a solution for our clients are:

* **Business goals**: what do you want to achieve with your content? This is closely tied to where you’ll want to place your content, which in turn informs your choice of tooling. Are you looking to inform, inspire, or convert? (Often, it will be a mix of these, which is perfectly fine—just make sure you cover all your bases!)

* **Span of control**: site builders give operators maximum flexibility to change whatever they want, whenever they want it. This is both a blessing and a curse: a non-designer making design changes can quickly become a disaster. What’s the sweet spot between not enough and too much control for your team?

* **Implementation effort**: page builders are more complex to integrate than headless CMSs, which in turn are more complex to integrate than a monolithic CMS. Do the benefits outweigh the extra effort of integrating the solution with your tech stack?
Apparel brand and Nebulab client [Huckberry](https://huckberry.com/), for instance, relies heavily on their blog, [The Journal](https://huckberry.com/journal). The Journal is a fully integrated part of Huckberry’s shopping experiences: articles typically feature one or more Huckberry products, which users can add to their cart with a simple click. It’s a very conversion-centric approach.

![Huckberry’s blog, The Journal](https://cdn.sanity.io/images/kqgmf0g2/production/7ce46f90390f88961bbc568a2fafc78deb24b464-2560x2512.png?fit=clip)

Travel brand [Away](https://awaytravel.com/), on the other hand, publishes the travel and lifestyle magazine [Here](https://www.heremagazine.com/). Away’s magazine has a laid-back, inspirational approach with its content, and you can hardly notice that a DTC brand is behind the project.

![Away’s magazine, Here](https://cdn.sanity.io/images/kqgmf0g2/production/85383a0a4b600d7279c82359e0f0da9edf9209b8-2560x1664.png?fit=clip)

### Picking tools vs. building infrastructures

**Rather than only adopting one solution, we recommend creating a content management infrastructure, i.e., a suite of tools that integrate and build on top of each other.** For example, here’s a typical journey a DTC brand might undergo:

1. As a first step, they adopt a headless CMS, which allows them to decouple content and presentation. They use their headless CMS as the source of truth for all their content and integrate it with their existing digital properties (e.g., their product pages).

2. At some point, the marketing team starts complaining that they can’t make visual changes to how the content is presented on their product pages without asking the engineering team. This leads the team to integrate a page builder for specific website sections (e.g., the PDP and the blog). The page builder is connected to their headless CMS, which is still used for managing the actual content.

3. The brand eventually outgrows the page builder as well: the marketing team wants to start experimenting with standalone experiences such as microsites for contests, promotions, and product launches. This is when the team adopts a site builder, giving marketing full power to launch new properties with little to no engineering assistance.

**As marketing teams gain more freedom, brands need to create checks and balances to avoid brand dilution.** This can mean having a content inventory that is continuously monitored for consistency, ensuring the same person is orchestrating all digital experiences, or having a full-fledged design system that can be applied across different platforms. **Ultimately, how much centralization you need depends on your brand’s scale and your reliance on content.**

## Our favorite CMSs for eCommerce brands

At Nebulab, we have helped tens of brands design, launch and optimize their content stack, and we have developed some pretty strong opinions on which CMS platforms to use:

* **Monolithic CMS:** [WordPress](https://wordpress.org/). As mentioned, we rarely pick monolithic CMSs for eCommerce brands—but when we do, we go with WordPress. It has a beautiful, intuitive UX and an incredibly vast ecosystem of plugins and integrations, making it a good fit for many use cases and deployment options.

* **Headless CMS:** [Sanity](https://sanity.io/). A few years ago, we would have recommended Contentful or Prismic ([and we have](https://nebulab.com/blog/five-no-code-ecommerce-management-tools#the-best-headless-cms-prismic)). Ever since we’ve had a chance to try Sanity, we fell in love with its flexibility. While traditional headless CMSs force users to use a standardized UI for managing content, Sanity’s content studio is open-source and can be tailored to your content management needs and editorial workflows.

* **Page builder:** [Plasmic](https://plasmic.app/). While Builder.io has been around for slightly longer, Plasmic offers a more modern UI for their editing backend. With that said, the two tools are incredibly similar, so we encourage you to experiment with both and see which one you prefer.

* **Site builder:** [Webflow](https://webflow.com/). Even though products like Framer offer a more streamlined experience for non-technical users, Webflow remains an incredibly powerful tool if you know how to use it. With support for custom workflows and gated content, your team can quickly spin up rich micro-sites that cover various use cases.

As always, your mileage may vary. Before committing to a solution, you should thoroughly assess your present and anticipated requirements and experiment with different setups to build a stack that can scale with you as your needs evolve. If you need any advice or want to speed up the process, [we’re happy to help](https://nebulab.com/contact-us)!

Building Content Infrastructures for eCommerce Brands

With 9 out of 10 online shoppers stating that they want retailers to offer recommendations based on their interests, and
considering the vast amount of data and technology brands now have, there are few excuses not to have a personalization
strategy in place. eCommerce personalization is proven to increase conversion rates, customer retention, and lifetime
value, creating a virtuous cycle that can turn your visitors into loyal customers.

And yet, we often see brands struggle with rolling out an effective eCommerce personalization strategy: founders aren’t
sure how to segment their customers, evaluate the effectiveness of their personalization program, or what technologies
to adopt.

This article will demystify eCommerce personalization, providing you with a straightforward, data-informed approach for
designing, implementing, and validating your personalization program.

## What Is eCommerce Personalization?

Much confusion stems from the vagueness of the term “personalization.” If you Google it, you’ll find books, articles,
and SaaS products that all give it a different meaning: they talk about A/B testing, machine learning algorithms,
retargeting, user-generated content… It can be hard to understand where to start.

At its core, eCommerce personalization is the technique of using the information you have about your customers to
display content that is tailored to them. You can personalize across different dimensions (e.g., personalize by country,
browsing history, demographics), at different touchpoints (e.g., your website, your email campaigns, your ads), and with
different techniques (e.g., machine learning, user-generated content, human curation). No matter *how* you personalize,
the goal is to create a customer experience that feels unique.

## Designing Experiments with GTDT

When we coach founders that want to start experimenting with personalized eCommerce, we encourage them to think of
personalization as the means, not the end. Like all initiatives, personalized shopping experiences must be designed
thoughtfully and intentionally for maximum effectiveness. Adopting a tool or installing a plugin will only take you so
far if you don’t have a clear understanding of what you want to achieve with your personalization program.

Unfortunately, most resources offer an endless list of personalization examples from famous online retailers but no
indication of *why* or *how* those brands designed their personalization programs. This leaves operators scratching
their heads as they try to replicate the success of these brands. As a result, they tend to go for “blanket solutions”
that are easy to implement but ultimately leave the most exciting opportunities on the table.

Over time, we have developed a model that helps brands roll out thoughtful personalization experiments that get them
closer to their goals. **We call it GTDT, which stands for Goal, Technique, Dimension, and Touchpoint.** In the
following paragraphs, we will explain this process, providing you with all the tools you need to design your
personalization program.

### Goal

This might seem obvious, but surprisingly often, online stores will launch personalization capabilities to tick off a
checkbox with no clear goal in mind. Unfortunately, this not only means they will roll out an ineffective
personalization program, but it also means they will not be able to validate the effectiveness of their program: if you
don’t know what metric you’re optimizing for, you will not know what metric to look at.

The first step for any good personalization experiment is to understand what goal you’re aiming for—everything else
follows. Depending on your brand’s SKU catalog and business model, a personalization experiment for increasing your
average order value will likely look much different from an experiment to improve your add-to-cart or repeat purchase
rates.

If you’re unsure what goals you should be prioritizing, talk to your team and pick the one that feels most urgent right
now. Where are you falling behind compared to the industry benchmarks and your (or your board’s) expectations? What are
your customers telling you in your interviews? (You ***are*** doing customer interviews, right?) What are your plans for
the next 12-18 months?

Don’t make the mistake of falling into analysis paralysis: the beauty of running experiments is that you can quickly
pivot if you find that you’re optimizing for the wrong things. Make a data-informed decision, launch your experiment and
move on!

### Technique

With eCommerce personalization rising in popularity, several tools have emerged that promise to offer the definitive
solution to all your personalization problems. A/B testing tools, content management systems, eCommerce platforms, UGC
aggregators… almost every tool provides some level of personalization nowadays.

Unfortunately, without a clear personalization plan, it’s easy to get carried away by shiny object syndrome and try
every tool in hopes that it will solve all your problems. Do this enough times, and you’ll end up with a Frankenstein
monster of solutions that don’t integrate and annoy your customers with endless popups and broken UIs.

Before picking a tool or technology, decide **how** you want to personalize. You should do this, first and foremost, by
continuously referring back to your experiment goal and dimensions, but you should also lean on your brand’s strengths
and values. If you’re a fitness brand that’s all about community, you might want to leverage the power of user-generated
content, at least in *some* of your experiments. If you’re a luxury brand selling elevated essentials, perhaps you can
offer human-curated recommendations to your customers.

This last example brings us to the next point: don’t be afraid to do things that don’t scale, especially in the early
stages of your company. Many brands go from zero to a hundred by integrating deep learning models that are complex to
tweak and customize, and they don’t realize that they’re missing out on many lower-hanging opportunities. You’d be
surprised by how much you can accomplish with some basic customer segmentation and a human touch.

### Dimension

Once you know what you’re aiming for and how you want to get there, it should be easier to understand which dimensions
you want to personalize across. For example, you might want to personalize content based on geography, customer lifetime
value, previous purchases, demographics, or other factors.

It might help to group personalization dimensions into three big buckets:

- **Historical.** These are actions your customer has taken before. For instance, you might segment customers based on
  their AOV or purchase history.
- **Demographical.** These are traits that will always be true about your customer. For instance, you might segment
  customers based on geography, gender, or age.
- **Contextual.** These are actions your customer is taking now. For instance, you might segment customers based on
  their cart contents or session length.

When thinking about dimensions, don’t get limited by data availability: just because you don’t know your customers’ age,
it doesn’t mean you shouldn’t use it in a personalization experiment. Part of your personalization program should be
designing ways to collect or infer the information you need to carry out your experiments, which has the added benefit
of enriching your understanding of your customers over time.

You also don’t want to limit yourself to generic customer data: we find that the most successful personalization
programs use proprietary brand data to create win-win outcomes. If you sell pet food subscriptions, you might want to
curate your product catalog based on the customer’s pet type or age. If you sell sports equipment, you could send your
customers content specific to their favorite sports. Think about the different ways customers engage with your brand,
and try to design your experiments to optimize and improve those interactions.

### Touchpoint

With your goal, dimension and technique nailed down, it’s time to pick a personalization touchpoint. With this term,
we’re referring to the specific customer interaction you want to personalize. This will likely depend on your goal and
dimension: for example, if you want to increase AOV (goal) by looking at cart contents (dimension) to suggest upsells
(technique), you can only do this after the customer has added something to their cart or completed their purchase.

When deciding which touchpoint to personalize, we encourage brands to look beyond their product pages or on-site user
experience. Even with the average brand, there are so many ways your customers might interact with you and so many
opportunities to personalize those interactions. The most effective personalization programs are omnichannel and focus
on often-neglected interactions, such as transactional notifications, or create entirely new touchpoints, such as
sending customers a personalized card for their birthday.

Again, don’t get limited by your current touchpoints: if you want to send personalized email campaigns but are currently
not doing any email marketing, don’t be afraid to start now. If you think push notifications might resonate with your
customer base, why not give them a try?

The goal is to catch your customers when they need personalization even though they don’t know it, and leverage the
power of data to turn a mundane interaction into an intimate, personalized experience.

## Measuring Experiment Effectiveness

Designing and launching your experiment is only the first step. For an effective personalization program, you want to
validate the effectiveness of your experiment, double down on the ones that work, and kill the ones that don’t.

To do this, you must go into every experiment with a clear definition of success. This means answering the following
questions:

- What metric(s) do you expect the experiment to move, and in what ways?
- Are you already measuring the metrics you need? If not, how can you measure them? Do you need time to get a baseline
  for comparison?
- For how long will you let the experiment run after measuring the results?

### A/B Testing vs. Attribution Models

A special mention here goes to A/B testing: like with AI/ML, inexperienced founders often jump into A/B testing without
a clear understanding of the technique. While it’s tempting to run all personalization experiments as A/B tests,
sometimes this can be overkill or even detrimental: A/B tests require specific expertise, dedicated infrastructure, and
relatively high traffic volumes to reach statistical significance.

Typically, we only recommend A/B testing for large brands, running experiments where the results are highly uncertain,
and a clear control group can be established (e.g., customers that see the regular homepage vs. customers that see the
personalized homepage). In all other scenarios, a robust attribution model is a more reliable way to measure the
effectiveness of your experiment—even better when it’s paired with qualitative customer insights.

### Following Through on Experiment Validation

However you decide to validate your experiment, make sure it’s easy to roll back if needed and remember to look at the
data and take action accordingly! A recurring “experiment review” meeting with your team, where you review all
experiments that need to be validated and decide how to move forward, can do wonders to keep you accountable.

Finally, don’t be too rigid: if you expected a cross-selling experiment to drive $100K in revenue, but it only drove
$80K, it might still be worth keeping, depending on the effort you need to turn it into a permanent, first-class
capability. For some experiments, it might be helpful to establish different thresholds of success. Going back to our
example:

- Anything below $50K might be a clear failure.
- Anything between $50K and $100K might be up for discussion.
- Anything above $100K in incremental revenue might be a clear success.

Generally speaking, whenever you make decisions about your digital experience, you want to be data-informed, not
data-driven.

## Ready to Start Personalizing?

eCommerce personalization can create remarkable outcomes: when leveraged intentionally, personalization programs
increase revenue, customer loyalty, conversion rates, and lifetime value. It’s a tool that should be part of every
eCommerce store’s marketing strategy.

With the GDTD model, you have a streamlined process to design, launch and validate your personalization experiments.
It’s a high-impact tool that evolves with your brand as you scale, better understand your customers, and develop more
sophisticated ways to engage them.

If you have questions or need help with your brand’s personalization program,
[we’d love to hear from you.](https://nebulab.com/contact-us) Let’s start personalizing!

The GTDT Model for Intentional eCommerce Personalization

Your stakeholders are complaining that the app is slow. You work tirelessly to shave millisecond over millisecond off your server’s response time. Yet, at the end of the month, the stakeholders still complain about the suboptimal performance, to only later discover that they are using the application on a smartphone connection, inside a building with hundreds of other people using a smartphone, and while taking the elevator to move from one meeting room to the other.

This is just a story, but maybe you have experienced (or are experiencing) something similar in the real world and are looking for a more scientific approach to performance tuning. Perhaps you are here looking for an easy solution to improve your performance by just tweaking a single parameter in your configuration.

Unfortunately, most of the time, that’s simply not possible. All the overnight success stories you read about are the result of long experimentation, and they conveniently forget to mention all the failed attempts. Performance tuning is complex: it heavily depends on your application’s context, architecture, and [tech stack](https://nebulab.com/blog/ecommerce-tech-stack). To succeed, you must get your hands dirty and enter into a continuous measure/tune/verify cycle.

In this article, we will showcase a process for experimenting with the ideal performance setup for a [Solidus](https://solidus.io/)-based eCommerce application, as well as some interesting conclusions we’ve reached.

## Starting with Puma threads

When configuring a Puma-based Rails application, you can tune a lot of values to optimize its performance. Still, we can simplify our process by focusing on four major levers:

* The number of Puma workers

* The number of Puma threads

* The amount of CPU in your environment

* The amount of RAM in your environment

For now, let’s assume the amount of CPU and RAM and the number of Puma workers are constant, which is the most straightforward configuration you can have. We’ll talk about Puma workers and horizontal vs. vertical scaling in a separate article.

That means we should just focus on the number of threads, which allows us to keep things simple and straightforward. In general, when tuning performance, we recommend tweaking one parameter at a time: touching multiple levers simultaneously will lead to inconclusive results, and you might end up making things worse.

## Works on my machine!

For this experiment, we have run all tests locally. This allows us to have full control over environment in which the load tests are running. It also gives the Rails server plenty of CPU and RAM, preventing those from becoming a bottleneck in our testing.

Another advantage of running the tests locally is the almost complete lack of DB latency. In the real world, DB latency might impact the ideal number of threads, since each DB request will block its originating thread for a little longer. However, you can fine-tune this parameter in production. In this case, we’re more interested in getting a ballpark number that we can start with.

## The setup

In this experiment, we will optimize a Solidus-based eCommerce application, running PostgreSQL and Redis for data storage and caching/background jobs.

We’re using [k6.io](http://k6.io/) for load-testing because the open-source version is free, easy to set up and use, and it’s scriptable.

As a first step, let’s bootstrap a simple Solidus store. We won’t go through the setup because the process is already covered in the [official Solidus documentation](https://guides.solidus.io/getting-started/installing-solidus#in-a-new-app). You can find all the code in the [GitHub repo for this post](https://github.com/nebulab/repo-for-globpost-are-you-leaving-free-performance-on-the-table-).

We wrote a k6 script that simulates a complete visit to the website and performs a guest checkout. Note that this approach has some limitations: for example, we’re not testing the authentication system. However, it’s a good approximation of what a typical user journey would look like on my store.

As recommended by k6, we’re not testing asset delivery: in production, assets will most likely be served by a CDN, so testing them in a local environment would just muddy my data. Right now, we’re only interested in optimizing the application server.

We’ve decided to load-test the system with 50 concurrent users, each completing a single journey in my application. We’ve run the script against our Solidus application ten times, progressively increasing the number of Puma threads from 1 to 10.

## The results

Here are some graphs showing our results:

![Requests per second](https://cdn.sanity.io/images/kqgmf0g2/production/01b5ecdc96e1fa7859b614281aee8419c0ecfa39-600x371.svg?fit=clip)

![Request duration](https://cdn.sanity.io/images/kqgmf0g2/production/82090ce65420951346613c6f146c18b5e94ffa49-600x371.svg?fit=clip)

![Elapsed time](https://cdn.sanity.io/images/kqgmf0g2/production/88a998380731794a35b121d895e51122c54b74a0-600x371.svg?fit=clip)

Like most eCommerce applications, our Solidus store is a DB-heavy codebase, and it blocks each thread for a relatively long time. This is noticeable in our benchmark because the application is heavily underperforming with fewer than 4 threads.

It’s also interesting to note how the default Puma value of 5 threads is a good starting point for our application: anything beyond 5 threads is only giving us marginal gains. It also looks like performance is actually *decreasing* above 8 threads, which is probably due to the excessive thread context switching.

## Conclusions

k6 is a powerful tool with many applications: for instance, it could be used to enforce service-level objectives through automated performance tests.

When it comes to performance tuning, it’s always a good idea to put all decisions under the microscope. It was interesting to confirm that the default value of Puma workers is indeed a sane default. For a Solidus store, I would probably recommend bumping that number to 8 as a starting point to squeeze out each bit of performance.

However, the number of Puma threads is just the beginning. For instance, in a real-world performance tuning project, we should also:

* Calculate the amount of RAM I need to accommodate my ideal number of threads.

* Experiment with the number of Puma workers with 1 CPU.

* Experiment with the advantages of having a dedicated CPU versus a shared one.

* Compare different PaaS solutions to evaluate the pricing of our ideal setup.

* Experiment with horizontal and vertical scaling for our needs, creating to a well-defined scaling strategy.

As we’ve mentioned in the introduction to this article, there is no silver bullet for performance tuning: each project needs to account for the context, technical and business factors at play. However, by adopting a rigorous, scientific approach, you can tame the usual chaos of performance tuning, reaching your desired results with much less effort.

Over the next few weeks, we’ll continue to expand on this topic. *Stay tuned*!



The Nebulab