Pricing Analytics Best Practices

I've seen countless companies relying on outdated models or gut instincts for price changes. That often leads to tactical, knee-jerk pricing, missed profits, or constant battles to justify pricing & promotional plans to supply chain partners. I just recorded a quick video explaining exactly how we combine four different approaches to model elasticity accurately: 1. Double Machine Learning (DML) - Delivers a robust causal estimate by predicting sales and price from confounders, then regressing the residuals. - We typically build one DML model per SKU. In our experience, this often reflects real-world behavior best. 2. Log-Log regression models - It is simple and interpretable - perfect if you have lots of historical data, a high volume of transactions, or price variation. - The log price coefficient directly translates to elasticity. It is quick to implement, though it often oversimplifies and is not a good method for B2B. 3. ElasticNet - A regularized linear model balancing Lasso and Ridge methods. - If you have many variables, such as our promos, competitor promos, distribution, comp distribution, etc., it helps prevent overfitting. 4. Random Forest - Handles non-linearities pretty well without having to do complex data engineering. - We use price perturbation, simulating different price points to see how predicted demand changes, thus estimating implied elasticities. In the video, I also share how we compare the four methods, track metrics like RMSE or MAPE, and deliver scenario-based recommendations about price, promotions, and competitive moves, helping you go from reactive to proactive pricing. The real payoff is that you can: 1. Proactively manage pricing: estimate the impact of competitor actions and optimize your strategy. 2. Maximize promotional ROI: estimate what truly drives incremental volume vs. what's wasted spend. 3. Earn insights-backed credibility: support your pricing with robust elasticity metrics that show retailers how you got to your recommendations. I'd love to hear your thoughts. If you're ready to take a deeper look at these elasticity models (complete with a whitepaper, sample code, and practical examples), check out the comment section for links and more details!

Many founders treat pricing as a revenue optimization problem. Figure out the product first, scale usage, then monetize. That's backwards. Pricing isn't about extracting money. It's about discovering whether you built something people actually value. At Gamma, we used pricing as a proxy for value and kept it pretty much the same for over 2 years. Free usage will lie to you (especially for B2B and prosumer products). Usage spikes feel like PMF. They're not. Usage without payment tests your onboarding, not your value. If you come out with too generous of a free plan, you'll never know what true willingness to pay looks like. Here's how to use pricing as a proxy for value: 1. Pick your value metric Choose the thing customers actually hire you for. Documents generated. API calls. Minutes transcribed. At Gamma, we gated by AI credits as the primary value metric, with business levers like custom branding. 2. Draw a hard boundary between free and paid Let people experience the "aha," then stop them at a generous but bounded gate. We gave users plenty of AI credits up front. Once they hit the limit: upgrade for access to more AI. 3. Research your range, then let behavior decide We used Van Westendorp to find our starting range. Ask users four price points: too cheap to trust, good value, getting expensive, too expensive to consider. Plot where these intersect to bracket your range. Then test a few prices within it. Research shows what people say they'll pay - conversion shows what they actually do. We watched free-to-paid conversion and early churn signals, picked the winner, and moved on. 4. Instrument retention and talk to customers Track whether paid users keep crossing your value threshold each week. Stay close to customers through power-user communities or direct outreach. Ask questions like: "What job were you hiring us for?" and "What would justify a higher price?" 5. Treat pricing changes like product pivots Once you've validated pricing, the only reason to change it is if you've fundamentally changed what you're selling. We haven't changed ours in two years because the value metric (AI usage) hasn't changed. Constantly repricing means you're still searching for product-market fit. Why this matters: Pricing early clarifies who values you, which channels convert, and which segments to double down on. You're better off launching pricing way earlier so you can see who's actually willing to pay for it.

There isn’t one pricing strategy that drives upgrades. What works depends on how and when customers realise value. A recent PricingSaaS breakdown highlighted five different approaches teams are using. They’re not silver bullets, but each solves a specific mismatch between pricing and usage. 1. Change the billing cadence ↳ Moving from monthly to quarterly or annual billing gives customers more time to see value before a renewal decision. ↳ This works best when time-to-value isn’t instant and early churn is driven by impatience rather than lack of fit. 2. Rethink what you meter ↳ Some teams removed limits like user caps and shifted to usage metrics closer to real value. ↳ The upgrade trigger becomes growth in usage, not hitting an artificial ceiling. 3. Use add-ons as a discovery path ↳ Add-ons let customers try advanced capabilities without committing to a higher tier. ↳ They work well when value is clear only after hands-on use. 4. Price onboarding and support intentionally: ↳ Defaulting to self-serve onboarding and reserving human support for higher tiers aligns cost with commitment. ↳ It also signals where the product expects customers to be more serious. 5. Adjust the entry point: ↳ Raising the floor price or tightening the lowest tier can naturally push customers toward plans where upgrades make more sense economically. Across all five, the pattern is alignment. Pricing works when it follows customer behaviour, not when it tries to correct it. Which part of your pricing feels most disconnected from how customers actually use your product today?

Brands throw darts at pricing blindfolded when they could use laser precision. This framework eliminates the guesswork (and it’s the exact framework we use for our clients): Step 1: Define Your Objective Get specific before you test anything: • Understanding fair pricing perception? • Measuring brand awareness impact on price sensitivity? • Finding gaps in the current pricing structure? Step 2: Use the Right Methodology • Survey your audience using tools like Pollfish • Split respondents: brand-aware vs brand-unaware • Ask Van Westendorp questions: → What price feels "too expensive"? → What price feels "too inexpensive"? → What price is a "bargain"? Step 3: Analyze Audience Segments These groups live in different worlds: Brand-Aware Customers: • Higher price tolerance • Accept broader price ranges Brand-Unaware Customers: • Prefer entry-level pricing • Need more education and trust-building Step 4: Identify the Optimal Price Range • Plot responses on Van Westendorp Price Sensitivity Meter • Find the Indifference Price Point (IPP)—where price feels "just right." Real example: • Brand-Aware IPP: $65 • Brand-Unaware IPP: $47 • Optimal range: $45–$75 That $18 difference changes everything, which is why you need to stop guessing and start measuring. What's your current pricing based on? If it's a gut feeling instead of data, you're leaving money on the table.

About 2-3 months back, I found out that one of my client’s page had around 570 people visiting the pricing page, but barely 45 booked a demo. Not necessarily a bad stat but that means more than 500 high-intent prospects just 'vanished' 🫤 . That didn’t make sense to me because people don’t randomly stumble on pricing pages. So in a few back-and-forth with the team, I finally traced the issue to their current lead scoring model: ❌ The system treated all engagement as equal, and couldn’t distinguish explorers from buyers. ➡️ To give you an idea: A prospect who hit the pricing page five times in one week had the same score as someone who opened a webinar email two months ago. It’s like giving the same grade to someone who Googled “how to buy a house” and someone who showed up to tour the same property three times. 😏 While the RevOps team worked to fix the scoring system, I went back to work with sales and CS to track patterns from their closed-won deals. 💡The goal here was to understand what high-intent behavior looked like right before conversion. Here’s what we uncovered: 🚨 Tier 1 Buying Signals These were signals from buyers who were actively in decision-making mode: ‣ 3+ pricing page visits in 10–14 days ‣ Clicked into “Compare us vs. Competitor” pages ‣ Spent >5 mins on implementation/onboarding content 🧠 Tier 2 Signals These weren’t as hot, but showed growing interest: ‣ Multiple team members from the same domain viewing pages ‣ Return visits to demo replays ‣ Reading case studies specific to their industry ‣ Checking out integration documentation (esp. Salesforce, Okta, HubSpot) Took that and built content triggers that matched those behaviors. Here’s what that looks like: 1️⃣ Pricing Page Repeat Visitors → Triggered content: ”Hidden Costs to Watch Out for When Buying [Category] Software” ‣ We offered insight they could use to build a business case. So we broke down implementation costs, estimated onboarding time, required internal resources, timeline to ROI. 📌 This helped our champion sell internally, and framed the pricing conversation around value, not cost. 2️⃣ Competitor Comparison Viewers → Triggered: “Why [Customer] Switched from [Competitor] After 18 Months” ‣ We didn’t downplay the competitor’s product or try to push hard on ours. We simply shared what didn’t work for that customer, why the switch made sense for them, and what changed after they moved over. 📌 It gave buyers a quick to view their own struggles, and a story they could relate to. And our whole shebang worked. Demo conversions from high-intent behaviors are up 3x and the average deal value from these flows is 41% higher than our baseline. One thing to note is, we didn’t put these content pieces into a nurture sequence. Instead, they were triggered within 1–2 hours of the signal. I’m big on timing 🙃. I’ll be replicating this approach across the board, and see if anything changes. You can try it and let me know what you think.

If you’re still pricing #GenAI like SaaS, you’re not “innovating” — you’re gambling with your margins. AI-enabled business models are just emerging, but a recent article from Bessemer Venture Partners, "AI Pricing & Monetization Playbook" (in the first comment) nails the core shift: AI doesn’t monetize access; it monetizes outcomes — in a world where every token (and human-in-the-loop) has a real COGS line item. Practically speaking, start with the business model you’re really building: Copilot vs. Agent vs. AI-enabled Service → different economics, different charge metrics. Then pick a charge metric as a strategic choice (consumption → workflow → outcome): tighter value alignment means you’re taking on more cost risk. Next, use hybrid pricing (base + usage/outcome tiers) to balance predictability with upside. Finally, test value-first, then “find the price through friction” (if it’s an instant yes, it’s probably too low). Most importantly, treat pricing as your operating model: it shapes sales motions, CS incentives, what you measure, and how you scale from 10 to 1,000 customers. This resonates strongly with what I’ve been seeing in my research and in the classroom at The Wharton School: in AI-enabled business models, pricing isn’t a “packaging” decision—it’s where strategy, unit economics, and organizational design meet. #AI #GenAI #Pricing #Monetization #BusinessModels #UnitEconomics #GoToMarket #SaaS #Wharton

People often ask whether pricing can be optimized. The answer is yes... but only if you are optimizing the right thing. It is not the prices themselves that should be optimized. It is the pricing strategy. That may sound like a subtle distinction, but in practice, it changes everything. Retail prices are not static decisions. They are outcomes of a complex environment. Costs shift. Competitors react. Demand fluctuates. A price that works in January may be a mistake by February. Trying to optimize a specific number in that context is like trying to hit a moving target while the wind is changing. But pricing strategies is where we have control. A pricing strategy is a rule. It is a logic that takes the current environment and turns it into an action. It tells you what price to post, given what you know. That is the decision. And that is what we can test, compare, and improve over time. When we run experiments, we are not asking whether $19.99 beats $17.49. We are asking whether Strategy A, which might lean on cost-plus logic, outperforms Strategy B, which might use elasticity estimates and competitor tracking. And we can do that experimentally. If I have 200 products, I can apply Strategy A to half and Strategy B to the other half. Let them run. Prices will change daily, even hourly. But over time, I will see which rule generates more margin, higher conversion, or better sell-through (whatever outcome I care about). This is not about locking in a number. It is about finding the decision logic that learns and adapts with the market. In Sequential Decision Analytics, we do not fixate on the outcome of one decision. We focus on the policy: the mapping from information to action. That is what gives us flexibility. That is what makes experimentation meaningful. And that is what allows us to learn systematically. In pricing, as in most dynamic environments, we do not optimize answers. We optimize policies. And that shift in mindset changes how we build, test, and improve every decision we make. #PricingStrategy #DecisionIntelligence #SequentialDecisionAnalytics #DynamicPricing #PolicyOptimization #RetailAnalytics #ABTesting

Most product teams don't know their pricing model is broken until it's too late. Revenue is flat. Deals are stalling. And you're wondering what went wrong. A couple of weeks ago, Supra hosted Marcos Rivera (founder at Pricing I/O), who has helped hundreds of companies capture a combined $400M in additional ARR. According to Marcos, there are 6 hidden signals that can tell you it's time to revisit your pricing - long before you see the impact on revenue. 1/ No one asks for discounts (20% or less) Counterintuitive, right? If customers always pay full price without negotiating, you're likely underpriced. Your value exceeds your pricing. 2/ Deep discounting is common (30-50% off) This isn't just about being expensive. Often, it means you're selling the wrong thing to the wrong customer. You might need a leaner entry plan or better value framing. 3/ Little revenue from expansion (< 20% of new MRR) If customers aren't buying more after their initial purchase, your packaging needs work. Your value metric might be wrong, or your expansion path isn't clear. And in SaaS, expansion revenue is often the difference between good and great companies. 4/ Everyone stays in the entry plan When 80%+ of customers stay in your lowest tier, either: ↳ Your entry plan is too generous ↳ The jump to the next tier is too big 5/ High churn This screams value-price misalignment. But look deeper - are customers struggling to realize value quickly enough? 6/ Low close rates (<20%) Your pricing might be turning off prospects before they even try your product. Often, this means your pricing model is too complex. Marcos recommends to not wait for multiple signals. Even one of these is reason enough to review your pricing strategy. What other signals would you add to this list?

Most businesses treat pricing as an afterthought—but the companies that scale fastest treat it as their #1 competitive advantage. There are five rules every business needs to follow if they want pricing to drive growth, not limit it: 1️⃣ Position your price based on value, not cost. Customers pay for perceived value, not how much it costs to produce. 2️⃣ Research willingness to pay—never assume. Data-driven pricing decisions lead to higher profits and better positioning. 3️⃣ Increase margins by testing elasticity, not discounting. Lowering prices rarely drives long-term growth—optimizing pricing does. 4️⃣ Communicate why your price reflects premium value. The right messaging makes customers accept higher prices without resistance. 5️⃣ Evaluate and optimize pricing continuously. Markets change. Your pricing should, too. If you are still guessing your price, following competitors, or setting it once and forgetting it, you are missing huge profit opportunities. Which of these pricing rules is your company already following? And which one do you need to implement today? #PricingStrategy #Profitability #BusinessGrowth #ThePriceWhisperer