Data-Driven Pricing Decisions

From 15% to 31% close rate in weeks. One pricing decision changed their business. Here's how to replicate this result... Something I've noticed working with B2B founders: Most are making pricing decisions based on gut feelings… …instead of actual market data. Here's what happens: They launch with pricing that "feels right" or matches what competitors charge. Then they wonder why deals aren't closing or why they're constantly negotiating down. The problem? They're missing the real insights sitting in their own data. For example, I worked with a SaaS founder who was pricing at $99/month because that's what seemed "reasonable." But when we analyzed his deal patterns, we discovered something interesting: Customers who paid $199/month had 3x higher retention rates and generated 40% more referrals. Why? Because higher-paying customers were more committed and saw greater value. We also found that 67% of his lost deals weren't about price - they were about unclear value positioning. So we restructured his pricing strategy based on: - Deal pattern analysis - Competitive context research - Customer feedback extraction - Growth opportunity mapping Result? His average deal size increased by 180% and close rate jumped from 15% to 31%. The lesson? Stop guessing what your product is worth. Start analyzing what your customers actually pay for and why. Your pricing should reflect real market evidence, not assumptions. ________________________________ 👋 I’m Marina Kogan 🌊 Follow for more insights on GTM strategies

Inflation can erode consumer purchasing power, forcing businesses to rethink their pricing and product strategies. #BigBazaar, one of India’s leading retail chains, turned to real-time sales data to make smarter, faster decisions—and here’s how they did it. 🔍 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: With rising inflation, BigBazaar noticed: ✔️ A decline in premium product sales ✔️ More customers opting for smaller pack sizes ✔️ A shift toward private-label and economy brands Without clear data insights, adjusting to these changes would have been a guessing game. 📈 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Instead of reacting late, BigBazaar leveraged real-time analytics to track purchasing patterns at the SKU level. This enabled them to: ✅ Identify a growing preference for budget-friendly alternatives ✅ Adjust procurement and stocking strategies to align with demand ✅ Optimize promotions by offering targeted discounts on trending products rather than blanket price cuts 💡 The Result: ✔️ A 12% increase in sales for private-label products (Tasty Treat, Golden Harvest) ✔️ A 9% improvement in customer retention among price-sensitive shoppers ✔️ Reduced excess inventory of slow-moving premium items 🎯 Key Takeaway: In uncertain times, data beats intuition. Businesses that track real-time trends can pivot quickly—ensuring they meet customer needs while protecting profitability. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒏𝒂𝒗𝒊𝒈𝒂𝒕𝒆 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏? #DataDrivenDecisionMaking #DataAnalytics #

Leaders often view price increases as necessary for margin protection. In my experience, the strategic risk is underestimating how consumer dissatisfaction reshapes revenue stability and long-term financial performance. When trust erodes, product demand patterns shift faster than financial models forecasting a bear market. Reality is the best teacher. “PepsiCo announced (February 3rd) that it will reduce the prices of its snack brands, including Lay’s, Doritos, Cheetos, and Tostitos, by up to nearly 15% after receiving feedback from unhappy consumers. The lower retail prices will begin rolling out ahead of the Super Bowl party food shopping. PepsiCo says they did this because consumers have become more price sensitive and have been shifting to store brands or cutting back on snack purchases altogether. The company also agreed to reduce prices and streamline its product lineup as part of an arrangement with activist investor Elliott Investment Management. PepsiCo adjusted its strategy to regain volume and trust because of consumer feedback. “per a recent article from NPR. There are three considerations for leaders in this story: ▶️Even small increases can materially reduce customer lifetime value and disrupt revenue forecasts ▶️Declining sentiment toward your product/service raises customer acquisition costs and slows market expansion ▶️Poorly managed price changes limit strategic flexibility requiring more resources to support later adjustments Before a price increase, obtain a financial analysis that incorporates both economic data and projected customer sentiment. Validate that your organization has a communication strategy designed to maintain trust and protect long term demand. Assess the partnership with marketing, product, and customer experience leaders to stress test the pricing decision across multiple scenarios, including retention impacts and reputational risk. CFOs who treat pricing as both a financial and behavioral inflection point drive sustainable growth. Check out the February 3 , 2026 article on the NPR website, “Pepsi will cut prices on Lay's, Cheetos by as much as 15%” #RiskManagement #CFO #Leaders Inside Edge Risk Advisors LLC 

At the start of my career, pricing was often treated as an afterthought. Decisions were made based on instinct, outdated models, or by simply matching competitors. I witnessed how this approach consistently led to underperformance, weak positioning, and lost revenue opportunities. That experience shaped my belief that pricing is one of the most overlooked drivers of business growth. To solve this, we built the Predictive Sales Engine an AI-powered tool that brings clarity to pricing strategy. It analyzes actual market behavior to forecast revenue and sales volume at different price points. More importantly, it segments data to reveal how different audiences respond to pricing, allowing companies to set prices with precision and confidence. After working with hundreds of companies, the pattern is clear. When pricing aligns with how customers perceive value, businesses grow faster and more profitably. In a competitive market, using AI to guide pricing decisions is no longer a luxury. It’s a requirement for those aiming to lead rather than follow. #PricingStrategy #ArtificialIntelligence #PredictiveAnalytics #RevenueGrowth #ProductMarketing

Most UX questions about features, pricing, and configuration are inherently trade off questions, yet we often measure them with isolated ratings or importance rankings. Conjoint analysis fixes the measurement problem by forcing realistic choices between multi attribute options, so the data reflects decision making rather than post hoc opinions. Modern conjoint becomes far more rigorous when you model heterogeneity instead of averaging it away. Hierarchical Bayes can estimate individual level part worths, which supports segmentation and personalization without relying on crude demographic splits. Mixed logit models provide a complementary framework by explicitly modeling preference variation across users, which is essential when different groups value speed, ease of use, and price differently. Once you have choice data, you can go beyond basic utilities. Machine learning methods like random forests can surface non linear relationships and interactions among attributes that traditional ranking or linear models may miss. Bayesian networks add a probabilistic layer to map dependencies between attributes and behaviors, and simulation techniques such as Monte Carlo let you forecast how changes in pricing or feature bundles are likely to shift choices before launch.

Early in my journey in Revenue Growth Analytics, I learned a crucial lesson about the significance of external insights in crafting 𝐏𝐫𝐢𝐜𝐞-𝐕𝐚𝐥𝐮𝐞 𝐌𝐚𝐩𝐬 (𝐏𝐕𝐌). An oversight in my approach—relying solely on internal expertise—once led to decreased gross profits and EBITDA, as it failed to account for essential market perspectives. Prior price increases that we felt were justified, given our position on the PVM, had to be entirely negated by increased price promotions. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 "𝐏𝐫𝐢𝐜𝐞-𝐕𝐚𝐥𝐮𝐞 𝐌𝐚𝐩"? A Price-Value Map is a strategic tool that juxtaposes your products' perceived quality and price against your competitors. This framework helps businesses understand the actual "value" in the market—defined as the difference between the benefits a customer perceives and the price they pay: 𝐕𝐚𝐥𝐮𝐞 = 𝐏𝐞𝐫𝐜𝐞𝐢𝐯𝐞𝐝 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 (𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬) - 𝐏𝐞𝐫𝐜𝐞𝐢𝐯𝐞𝐝 𝐏𝐫𝐢𝐜𝐞 𝐁𝐞𝐲𝐨𝐧𝐝 𝐏𝐫𝐢𝐜𝐞: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 In B2B markets, decision factors extend beyond price. Quality, time to service, convenience, and other non-price attributes often rank higher in customer priorities, even in quasi-commoditized industries. 𝐇𝐨𝐰 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐚𝐧 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐏𝐕𝐌: 𝟏. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐚 𝐓𝐡𝐢𝐫𝐝-𝐏𝐚𝐫𝐭𝐲 𝐄𝐱𝐩𝐞𝐫𝐭: Commission a market research firm to conduct extensive customer surveys, capturing purchase intent-driving attributes. 𝟐. 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐫𝐞: Apply regression analysis or machine learning techniques to derive survey data's weighted quality and pricing scores. 𝟑. 𝐌𝐚𝐩 𝐚𝐧𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐳𝐞: Position your products and competitors' on the PVM, identifying areas where your product can better meet customer expectations. 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: Our past missteps in pricing strategy, matched with lower perceived quality compared to competitors, taught us the importance of aligning our offerings more closely with market expectations. This insight is vital for making informed strategic decisions that enhance profitability and market share. 𝐀𝐥𝐰𝐚𝐲𝐬 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 actual 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐏𝐕𝐌𝐬. It's not just about adjusting prices but enhancing the overall value proposition through continuous improvements based on robust, external market insights. I invite thoughts and experiences on leveraging Price-Value Maps for strategic decision-making. How has it shaped your pricing strategies? #𝗿𝗲𝘃𝗲𝗻𝘂𝗲_𝗴𝗿𝗼𝘄𝘁𝗵_𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 #Analytics #PricingStrategy #MarketResearch #ValueMapping #B2BMarketing

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

If you work with brands on Amazon, you hear this a lot: ‘Is our product priced right?’ It sounds simple, but the stakes are high—a mispriced ASIN can drag down sales or kill margins overnight. Too many agencies still answer with, ‘I think we’re premium,’ or just compare to one or two visible competitors. It’s not enough. The marketplace moves fast, and fuzzy guesses put you a step behind. Why do most fail here? They lack instant access to precise, category-wide pricing data and competitor breakdowns. That leads to ‘hunches’ instead of hard answers. Brands end up adjusting prices blindly, often backtracking once poor ACoS or falling conversion rates hit home. Emplicit’s AI-powered process gives you proof from the start. With a few clicks, we see if your ASIN is 40% over category average—and show exactly which competitors set the pace. Our workflow goes further, revealing coupon gaps or opportunities you can tackle before the first strategy session. No more anxiety over the ‘are we too high?’ debate—just data-driven confidence and a stronger roadmap for every launch. Would you rather argue over price, or start your Amazon strategy with facts that provide instant leverage?

Pricing Is No Longer a Finance Exercise. It’s Behavioral System Design. Most companies are still interpreting pricing and PPA using relatively “flat” methods. Elasticity curves. Scanner data. Van Westendorp. Gabor-Granger. Basic conjoint utilities. Useful? Absolutely. Enough for today’s market? Not even close. The future of Revenue Growth Management is not about finding “the right price.” It’s about understanding the behavioral system behind the purchase. Because shoppers do not make decisions in isolation. They make decisions based on: * mission * retailer * basket size * fuel cost * economic pressure * digital shelf visibility * household stress * competing priorities * pack architecture * perceived value * time pressure The same shopper may react VERY differently to the same product depending on whether they are: * stock-up shopping at Costco Wholesale * doing a quick fill-in at Walmart * shopping premium at Whole Foods Market * buying for Taco Night * shopping for kids * managing a tighter budget after a fuel increase That changes: * elasticity * willingness to pay * promo responsiveness * pack preference * substitution behavior * premium acceptance This is why advanced PPA is evolving rapidly. The leading RGM teams are now combining: * conjoint * Bayesian modeling * AI simulation * retailer-specific testing * digital shelf analytics * mission segmentation * portfolio architecture * macroeconomic signals …into connected decision systems. And one of the biggest mindset shifts is this: Old thinking: “Pricing is a finance exercise.” New thinking: “Pricing is behavioral system design.” The companies winning the next decade will not simply react to historical scanner data. They will simulate future shopper behavior before it happens. Because syndicated data explains the past. But advanced conjoint and AI-driven scenario modeling help design the future. #RGM #PricingStrategy #Conjoint #PPA #RevenueGrowthManagement #FMCG #CPG #Retail #ShopperInsights #AI #Bayesian #DigitalShelf #RetailMedia #Elasticity #ConsumerBehavior