Andrea Vassallo

Every week, the same tension fills boardrooms across the DTC landscape. As the week closes, the gap between projected and actual revenue widens, and the instinct is always the same: sacrifice price to force volume.

Blanket discounting becomes the business model. It starts as a temporary fix (a 30% off site-wide sale to bridge a revenue gap) but quickly metastasizes into a long-term dependency. While the immediate revenue spike provides short-term relief, the hangover is severe. Margins erode, brand equity dilutes, and most dangerously, customers are trained to wait.

Strategic discounting is a powerful lever for growth; panic discounting is a slow-motion liquidation of brand value. But if your business has real discount dependency, you already know that simply stopping isn't an option. You cannot turn off the tap without crashing cash flow.

The solution isn't to stop discounting overnight. The solution is to transition strategically. This guide outlines exactly why you are stuck and provides a phased operational roadmap to recover your pricing power without destroying your business.

## The Anatomy of Brand Erosion

To build the internal case for change, you must articulate the full cost of your current strategy. It goes beyond simple margin loss; it's about the structural degradation of your business model.

### Customer Conditioning

You are training your customers to be price-sensitive. Reference price theory explains why discounts work so well in the short term: customers enter with a price expectation shaped by the market, and when your offer undercuts it, the deal feels like a win. They buy.

But here's the trap: that discounted price becomes their new anchor. The next time they see your product at full price, it doesn't feel like the actual price. It feels like a markup. You won the acquisition and lost the customer's willingness to pay.

The pattern is consistent across the DTC brands we work with: customers acquired on deep discounts buy less often, churn faster, and generate significantly lower lifetime value than full-price cohorts. You aren't just acquiring cheaper customers; you're acquiring worse ones.

### Margin Math

The math of discounting is punishing. Consider a product with a $100 price point and $55 cost of goods (a healthy 45% gross margin). At a 30% discount, the selling price drops to $70 but the cost of goods stays at $55, compressing gross profit from $45 to $15 per unit. To maintain the same gross profit dollars, you now need to sell three units for every one you used to sell at full price.

At first, discount-driven audiences are large, cheap to reach, and fast to convert. The CAC looks great on a dashboard. But you've just tripled your volume while cutting your margin per unit by two-thirds, and you've filled your customer file with buyers who were attracted by price, not by your brand. They'll wait for the next sale, and the next one, and the next one. In the meantime, your CAC will keep rising as you exhaust your in-market audience, compressing your margins more and more until you're actually losing money, not making money.

## The Alternatives to Discounting

If blanket discounts are bleeding your margins, what's the alternative? You've likely tried some of these tactics before. The difference comes down to execution discipline, not the tactics themselves. These three strategies work when applied systematically, not as one-off experiments.

### Strategy 1: Lock Cash into Your Ecosystem

Instead of giving customers 30% off that they can take anywhere, give them that value back in a form that requires them to return to your brand. This means loyalty points or store credit.

The mechanics are simple: a customer who would have received a 30% discount instead receives equivalent value as points in a loyalty program or as a credit applied to their account. The cost to you is lower due to breakage (not everyone redeems), and you maintain your reference price. A $100 product stays $100; the customer just has $30 in their account to use on the next purchase.

Store credit works particularly well for win-back flows and cart abandonment. Instead of sending a percentage-off coupon, send a $20 credit. It maintains pricing integrity and typically increases AOV, as customers often spend more to use up the credit rather than letting it expire.

Loyalty points offer similar mechanics but with more flexibility in how customers redeem. Cash-back points are the default, but you can also allow redemption for other benefits depending on your program structure. The key advantage over direct discounts is breakage and the psychological commitment to your brand that comes with an account balance.

### Strategy 2: Add Value Instead of Cutting Price

Shift the offer from price reduction to value addition. The principle is simple: give customers something that feels valuable but costs you less than a percentage-off discount.

This includes tangible additions like free shipping, extended returns, or gifts-with-purchase using slow-moving inventory. But it also includes access and exclusivity: early access to new drops, exclusive products, VIP experiences, or priority support. These carry high perceived value at a fraction of the cost of a cash discount.

Value-adds work best when the perceived value is high but the actual cost is low. Free shipping on orders over a threshold costs you the shipping subsidy but increases AOV. An extended return window costs you nothing unless the customer actually returns something. Early access costs you nothing but makes customers feel special. A GWP using dead stock turns a sunk cost into a conversion driver.

### Strategy 3: Improve Your Discount Targeting

If you're going to offer cash discounts, stop broadcasting them. The damage comes from giving discounts to people who would have paid full price. Narrow targeting protects your full-price buyers while still giving you a tool for acquisition and reactivation.

This means retiring site-wide promotional codes and homepage sale banners. Instead, send discounts only to specific audiences via email or SMS: lapsed customers who haven't purchased in 6+ months, cart abandoners, first-time visitors who didn't convert. Use unique, single-use codes tied to the recipient so the offer can't leak to your full-price audience.

The key is channel discipline. Discounts sent via direct channels (email, SMS, retargeting ads) stay contained. Discounts displayed on-site or in social posts get shared, screenshot, and distributed to people who didn't need the incentive. If a customer is already on your product page ready to buy, don't show them a discount. If they abandoned their cart or haven't returned in months, that's when the offer goes out.

Narrow targeting also gives you attribution clarity. When every offer uses a unique code, you know exactly which customers are discount-dependent and which aren't. That data becomes the foundation for further refinement.

## Pulling the Plug on Discounts

With your alternative toolkit in place, you can begin the transition. This is not about flipping a switch overnight; it's about systematically replacing blanket discounting with targeted offers and alternative mechanisms that protect margin while maintaining revenue.

### Measure the Problem

Before you change anything, you need to understand how deeply discounting has penetrated your business model. Pull 12 months of order data and calculate three metrics:

1. **Discount penetration rate**: What percentage of total orders used a discount code? This tells you how normalized discounting has become in your customer experience.  
2. **Revenue at risk**: What percentage of total revenue came from discounted orders? If the majority of your revenue required a promotional code to convert, you have a structural dependency problem.  
3. **Average discount depth**: Across all discounted orders, what was the average percentage off? A brand running 15% average discounts has a different problem than one running 35% average discounts.

These three numbers determine your transition timeline. Brands with 70%+ discount penetration and deep average discounts typically need 12 to 18 months to fully recover pricing power without cratering revenue. Brands under 40% penetration can move faster, sometimes within a single quarter. The key is matching your pace to your dependency level.

### Execute the Transition

The transition is conceptually simple: stop giving discounts to people who don't need them, and change what you give to people who do. In practice, this means progressively applying your three strategies (narrow targeting, ecosystem lock-in, and value-adds) to replace blanket promotions.

Here's what that looks like over a typical quarter:

1. **Audit your promo calendar.** List every discount you're running: site-wide sales, email blasts with promo codes, homepage banners, cart abandonment offers, welcome discounts. Calculate how much revenue each one drives. This is your baseline.  
2. **Start tightening the screws.** Remove discount messaging from your homepage. Retire one site-wide sale and replace it with targeted offers sent only to lapsed customers via email using unique codes. Swap percentage-off codes in your cart abandonment flow for store credit. Test early access against cash discounts for engaged customers.  
3. **Measure the impact and iterate.** If revenue drops too much, you moved too fast. Stabilize and try a smaller change. If revenue holds or margins improve without significant volume loss, continue tightening.

Over time, your promotional architecture shifts. Lapsed customers get aggressive win-back offers via email, but never on-site. Cart abandoners get store credit, not percentage-off codes. Loyal customers get early access and exclusive products. New visitors see full price with strong brand storytelling.

As this happens, expect revenue to stay flat or dip slightly in the short term. You're replacing low-quality volume with full-price purchases, and that's a slower build. But the composition of that revenue changes fundamentally: gross margin dollars grow because you're no longer giving away significant percentages of every other transaction, and customer quality improves as your file tilts toward buyers who don't need a coupon to convert.

## What It Looks Like on the Other Side

Eighteen months into a disciplined transition, the business looks fundamentally different. Discount penetration drops from 70% to under 30%. Gross margin per order increases by 15 to 20 percentage points. Customer cohorts acquired at full price show 2x to 3x higher LTV than legacy discount cohorts.

But the hardest part isn't the tactics. The hardest part is the internal conversation. Finance sees short-term revenue risk. Marketing sees CAC spike as you stop subsidizing acquisition with margin-destroying discounts. The executive team has to hold the line while the business rebalances.

Need help planning and building the infrastructure to execute this transition? [We can help!](https://nebulab.com/contact-us)

Stop Killing Your Margins: A Guide to Smarter E-Commerce Discounting

Alessandro Desantis

Your potential customers are beginning to have conversations about your products with AI assistants like ChatGPT, Gemini, and Perplexity. When someone asks “What are the best wireless earbuds under $200 for running in all weather?” they’re getting instant recommendations complete with comparisons, specs, and reasons why.

[Nearly 60% of consumers](https://news.darden.virginia.edu/2025/06/17/nearly-60-use-ai-to-shop-heres-what-that-means-for-brands-and-buyers/) now use AI to help them shop, with [over half of Gen Z already using AI for product discovery](https://www.salesforce.com/news/stories/consumer-shopping-ai-trends-2025/).

This shift creates a new challenge for e-commerce brands: Generative Engine Optimization (GEO), the discipline of increasing your visibility within AI-powered discovery tools. Unlike traditional SEO, where you can pull up Search Console and see exactly where you rank, AI discovery is opaque. There’s no dashboard tracking how often ChatGPT mentions your brand, and no confirmed ranking factors. In other words, no one really knows what makes one brand show up over another.

Yet some brands consistently appear in AI responses while others don’t, and the industry is starting to make educated guesses about why that is.

In this post, we’ll focus on the high-level strategies for improving your AI visibility across platforms. We’ll explore the foundational tactics that work today and are most likely to continue to work tomorrow, regardless of how AI commerce evolves.

(Note: We’ll dive deeper into ChatGPT’s Instant Checkout feature and what it specifically means for Shopify brands in a separate, more technical post soon—stay tuned!)

## Setting Realistic Expectations About AI Commerce

Before diving into tactics, it’s important to understand where AI-powered commerce actually stands today versus where industry hype suggests it might be.

While nearly 60% of consumers report using AI to help them shop, usage doesn’t yet translate to transactions. A [comprehensive study of 973 e-commerce websites](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5585812) analyzing 12 months of data (August 2024 — July 2025) with $20 billion in combined revenue reveals that organic LLM traffic currently:

- **Represents less than 0.2% of total e-commerce traffic**—roughly 200 times smaller than Google’s organic search
- **Converts at lower rates than all traditional channels except paid social**, including underperforming organic search, paid search, email, and direct traffic
- **Generates lower revenue per session** than established channels across the board
- **Shows favorable engagement signals** (like bounce rate, indicating relevance) but **shorter session durations and fewer page views**, suggesting users are still learning to trust AI recommendations

**The good news:** The same research shows conversion rates from AI traffic are improving over time, suggesting the channel is maturing even if slowly. The performance gap is narrowing, though [projections indicate it won’t reach parity with organic search within the next 12 months](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5585812). This is a trend that will only accelerate with ChatGPT’s recent release of Instant Checkout.

In other words, GEO should be viewed as a long-term investment in future discoverability, not an immediate revenue channel. You’re building positioning for when—not if—AI-powered shopping reaches mainstream adoption. The brands that establish authority now will have significant advantages when the channel matures, but don’t expect meaningful traffic or conversion impact in 2025.

Think of this as 2005-era SEO: the fundamentals you build today will compound as the channel matures. The brands that act now—while the channel is still developing and the playing field is relatively open—will likely dominate their categories once adoption reaches critical mass.

## Measuring Your Share of Answer

Before you can improve your AI visibility, you need to understand where you currently stand. But unlike with Google’s Search Console, there’s no dashboard showing how often your brand appears in AI responses.

The solution is a surprisingly trivial “Share of Answer” approach. Here’s how it works.

Start by creating a comprehensive list of 25-50 prompts that represent how customers discover and research products in your category. If you’re struggling to generate this list, consider enlisting an LLM to help: you can provide context about your product category, target audience, and typical customer journey stages, and ask it to generate relevant queries. These should span different stages of the shopping journey:

- For product discovery, include queries like “Best laptop for graphic design students” or “What mattress should I buy for back pain?”
- For comparison and research, test prompts such as “[Brand A] vs [Brand B] comparison” or “Alternatives to Popular Product X.”
- For problem-solution matching, try “What helps with back pain while working from home all day?” or “How to choose the right standing desk.”

Once you’ve built your query set, it’s time to test your LLM by asking it each of the queries you’ve come up with. We recommend testing with ChatGPT, which [holds over 60% of the AI assistant market](https://gs.statcounter.com/ai-chatbot-market-share) and [ranks among the top 10 most visited websites globally](https://www.similarweb.com/website/chatgpt.com/). While it’s worth periodically checking other platforms like Gemini, Perplexity, and Claude to understand the broader landscape, your measurement and optimization efforts should center on where your customers actually are—and right now, that’s predominantly ChatGPT.

Calculate your Share of Answer score: the percentage of relevant queries where your brand receives at least one mention. The scoring breaks down like this:

- **Below 20%:** You’re invisible to the AI. Most queries in your category don’t mention your brand at all.
- **20-40%:** Limited presence. You appear occasionally, but competitors dominate most conversations.
- **40-60%:** Competitive visibility. You’re in the mix, showing up regularly alongside other established players.
- **60-80%:** Category leadership. You appear in most relevant queries, often with favorable positioning.
- **Above 80%:** You dominate AI discovery and are the go-to reference in your space.

When testing each prompt, don’t just check whether your brand appears. Document where it appears in the response, how it’s described, which competitors are mentioned alongside you, and what sources the AI cites. Context matters because it shapes how people perceive your brand.

This baseline becomes your benchmark. Without it, you’re making changes without knowing what’s working. With it, you have a clear starting point and a way to track progress over time.

## Prioritize Product Information Completeness

AI systems strongly prefer brands with detailed product specifications, clear imagery, accurate pricing and availability, comprehensive size guides, and compatibility information. Products with incomplete information are frequently skipped entirely, even from well-known brands.

Think of product data as table stakes. Without structured, complete information, LLMs have a much harder time understanding or recommending your products. To fix that:

- **Complete your specifications.** Every product should have comprehensive details covering dimensions, materials, compatibility, and performance characteristics. Include all details that help customers (and chatbots!) make confident decisions. As an example, if you sell a a standing desk, that means not just height range, but weight capacity, motor type, noise level, and cable management features.
- **Improve your imagery.** High-quality images showing products from multiple angles and in use help both customers and AI systems understand what you’re selling. Include lifestyle shots that demonstrate use cases, detail shots that highlight key features, and size comparison images that provide context.
- **Ensure pricing transparency.** Accurate product pricing, taxes, shipping costs, and availability clearly stated helps AI systems recommend products that customers can actually purchase.
- **Add detailed size and fit information.** For apparel, furniture, and any product where fit matters, comprehensive size guides are essential. Include measurements, fit guidance, and ideally customer photos showing the product on different body types or in different spaces.

Start with an audit of your top products. Where are the gaps? What information is missing or outdated? Prioritize fixing the products that drive the most revenue or that represent your brand in competitive searches. Then expand systematically across your catalog.

## Create Educational Content That Demonstrates Expertise

Educational content demonstrates genuine expertise in ways that product pages can’t. When you publish in-depth guides, detailed comparisons, and thoughtful how-to content, you signal to LLMs that you understand your space deeply, and you’re more likely to show up in long-tail queries.

- **Write comprehensive buying guides.** Address the real questions customers have when choosing products in your category. Not just “which product is best” but “what should I consider?” and “how do different features impact performance?”
- **Answer technical questions thoroughly.** If customers in your category wonder about compatibility, installation, maintenance, or optimization, create content that addresses these topics with real depth. Surface-level answers won’t do, as LLMs tend to prioritize sources that go deep.
- **Create comparison content.** Develop honest comparisons that help customers understand tradeoffs between different product types or approaches. When you can fairly compare alternatives in your category, you build credibility with both humans and machines.
- **Document use cases and applications.** Show how products work in real situations. Case studies, setup guides, and application-specific content help AI systems understand not just what you sell, but how and when customers should use it.

Start with your most frequently asked questions. What do customers want to know before buying? What problems are they trying to solve? What comparisons do they make? Create content that answers these questions comprehensively, and you’ll naturally build the educational foundation that AI systems look for.

## Build Authority Through Authentic Engagement

The tough truth about GEO is that your website is just one data point. What matters more is what the rest of the internet says about your brand.

LLMs train on and reference content from across the web. Even though [ChatGPT is citing Reddit less nowadays, ](https://www.investors.com/news/technology/reddit-stock-chatgpt-advertising-google/?utm_source=chatgpt.com) Reddit content is likely to still carry heavy weight in core model training. The same goes for YouTube videos showing your products in action, or social media content that demonstrates real customer satisfaction with your brand.

When your brand shows up consistently across Reddit, YouTube, industry publications, social platforms, and other sources, ChatGPT notices. It’s looking for patterns of trust across multiple sources. One mention is just noise. Ten consistent mentions across different platforms are signal.

- **Encourage authentic customer sharing.** Make it easy and natural for satisfied customers to share their experiences. Send post-purchase emails with prompts like “How are you using your new standing desk? We’d love to see your setup!” Include shareable moments in your product experience—unique unboxing, thoughtful packaging notes, or surprising product features that make people want to post.
- **Participate meaningfully in community discussions.** Find where your customers congregate online (e.g., industry forums, Reddit communities, Facebook groups) and find ways to amplify those channels. In some cases, this might mean contributing directly. In others, it will mean fostering the organic contributions that already happen (e.g., Reddit is notoriously hostile to brands hijacking the conversation).
- **Create shareable resources.** Develop tools, calculators, guides, or templates that others want to reference and link to naturally because they’re genuinely useful. An ergonomic desk setup calculator, a home office lighting guide, or a remote work productivity toolkit: these resources get shared, cited, and linked to organically, building the distributed presence that LLMs will recognize.

In other words, focus on building genuine value where your customers already spend time. The visibility will follow naturally.

## Monitor Your Progress and Iterate

Just like SEO, GEO isn’t a one-time project, but rather an ongoing practice that requires regular measurement and iteration. The AI landscape evolves quickly, and what works today may need adjustment tomorrow:

- **Re-run your Share of Answer analysis quarterly**. Track how your optimization efforts are actually impacting AI visibility. Use the same query set you established in your baseline, but don’t be afraid to add new queries as customer behavior evolves or as you expand into new product categories.
- **Track quality, not just quantity.** Look not just at whether your Share of Answer score is improving, but at how the nature of your mentions is changing. Are you appearing earlier in responses? Are the descriptions more accurate? Are you being mentioned alongside stronger competitors?
- **Test new approaches systematically.** When you hear about a new tactic or strategy, don’t just implement it everywhere. Test it on a subset of products or categories, measure the impact on your Share of Answer, and expand only if you see positive results. A disciplined approach prevents wasted effort on unproven tactics.
- **Document what you learn.** Keep a log of changes you make, results you observe, and patterns you notice. Over time, you’ll build institutional knowledge about what works for your specific brand, category, and customer base. This documentation will be invaluable when new team members join or when you need to justify continued investment.

Rather than implementing every possible tactic, you’ll want to systematically improve your Share of Answer score over time while building assets that serve customers across all discovery channels.

## Building AI Visibility Is Building Future-Proof Discovery

As covered in the expectations section above, AI-powered product discovery is still early-stage but showing clear momentum. The trajectory is undeniable: [Gartner predicts that traditional search engine volume will drop 25% by 2026](https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents), and conversion rates from AI traffic are [steadily improving over time](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5585812).

This creates a strategic window. The brands that build AI visibility now—while the channel is still developing—will likely dominate their categories once adoption reaches critical mass.

To do that, start with your baseline measurement, then systematically work through product data completeness, educational content creation, and authentic engagement. Finally, monitor quarterly and iterate based on what you learn. The approach is straightforward, but it requires commitment—a trait that is quite rare in an industry that tends to be very short-term oriented.

Need help navigating this transition? At Nebulab, we work with mid-market and enterprise e-commerce brands to build the technical infrastructure that drives visibility across all discovery channels—including emerging AI channels. We take a pragmatic, data-driven approach to GEO that balances long-term positioning with near-term ROI from your core acquisition channels. We can help you measure your baseline, prioritize optimization opportunities, and build a systematic approach to GEO that integrates with your broader e-commerce strategy. [Get in touch](https://nebulab.com/contact-us/) to discuss how we can help your brand show up in the conversations that matter most.

The Nebulab

Stop Killing Your Margins: A Guide to Smarter E-Commerce Discounting

The Early Mover’s Guide to AI Product Discovery