How Data Improves Pricing Decisions

I've seen countless companies relying on outdated models or gut instincts for price changes.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!

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 Decisions were made based on instinct, outdated models, or by simply matching competitors.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

A B2B SaaS company was regularly turning 30k deals into 100k in under 12 months—until their playbook caused new logos to dry up. Here’s what went wrong: Last week, I spoke with Kelly Ford Buckley, growth equity investor at Edison Partners. One of her recent PortCos just cracked the code on weak new logo bookings. Background: This company was trying to move upmarket—but they were still selling a lot of initial deals at approx. 30k. They wanted to go after bigger fish. So the C-suite made a bold choice: No more new logo deals for under 50k. On paper, it seems like a good strategy to only target higher-value customers. Unfortunately, this decision wasn’t based on robust customer data. And that’s when things started to unravel. Problem: After this company set the 50k floor, their new logo deals dried up. They couldn’t figure out why. Business was good, expansions were rolling. So why couldn’t they book new clients? Kelly had them dig into the data to see what was going on. Here’s what they found: – 67% of the previous year’s new logo bookings were signed <50k initial deal – Major success in turning a 30k deal into a 100k deal inside 12 months – Majority of 2024 expansion bookings were signed in 2023 Solution: The data told a clear story: – The 50k initial deal floor hurting their new logos (and therefore, future expansions) – They were segmenting their customers all wrong Instead of segmenting customers based on LTV, they were only looking at those initial deal sizes. Even though they quickly upsold existing customers. After looking at their data, this company took out the 50k floor and switched their growth strategy to focus on expansions. They started segmenting customers based on LTV (not just their current value). Now, they’re working a data-supported strategy to bring on new logos, grow them successfully, and reach their revenue goals. This is why data is critical. Without it, you don’t know what’s working in your business. You don’t know why you should do one thing over another. When you operate without data-based insights, you’re just guessing. You could end up breaking the part of the business that’s actually working. When you use data to make decisions, you can start taking actions that move the needle. For more, listen to my full conversation with Kelly Ford on “The Data Room.” Link in comments.