Data-Backed Strategies For Pricing Optimization

I've seen countless companies relying on outdated models or gut instincts for price changes.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

I have spent years in the highs and lows of the consumer goods industry but never seen a pricing climate quite like this. Manufacturers are getting squeezed from every direction-tariffs, skyrocketing raw material costs, and relentless supply chain disruptions. The old playbook of raising prices to cover costs? That’s dead. Why? Because consumers are feeling the pressure too. A 2024 Nielsen report makes it clear: today’s shoppers are scrutinizing every dollar they spend, and brands that aren’t strategic about pricing risk losing market share fast. Here’s what I’m seeing from top CPG brands that get it: 1️⃣ Walmart is investing heavily in AI-driven pricing models to keep costs competitive-e-commerce now makes up 18% of total revenue. 2️⃣ PepsiCo is doubling down on pack-size innovation, offering smaller, affordable options to maintain volume without excessive discounting. 3️⃣ Luxury brands are using price elasticity models, testing demand thresholds before rolling out increases-avoiding consumer pushback. 4️⃣ Supply chain resilience is non-negotiable. Companies are shifting manufacturing away from China, despite short-term cost spikes, to avoid future geopolitical risks. The smartest brands aren’t just reacting. They’re rethinking. They’re moving toward Revenue Growth Management (RGM) frameworks that help them: ✅ Optimize pricing and promotions (because blanket price hikes are a losing game) ✅ Focus on margin-smart growth, not just revenue ✅ Leverage data analytics to make smarter, faster pricing decisions Brands that don’t evolve risk eroding profitability or pricing themselves out of the market. CPG leaders who master strategic pricing, operational efficiency, and consumer-driven value creation will own the future of this industry. Are you adjusting your strategy, or just reacting to rising costs? Because in 2025, only the most adaptable brands will win. #CPG #FMCG #PricingStrategy #RevenueGrowth #ConsumerGoods

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

Our most underestimated pricing tool? AI. It’s easy to assume that pricing is all about intuition or guesswork, but AI is transforming how businesses approach price optimization. However, AI isn’t a one-size-fits-all solution—it’s a tool that, when used right, can drive smarter, data-backed decisions. Here’s why AI matters for your pricing strategy: → Dynamic Adjustments AI helps businesses adjust pricing in real-time, responding to shifts in demand, market conditions, and competitor activity. It ensures prices are always competitive and aligned with the market. → Data-Driven Insights By analyzing large sets of data—like past sales, customer behavior, and trends—AI helps identify the best price points to maximize profit without alienating customers. → Personalized Pricing AI enables businesses to tailor prices to individual customer segments, increasing both loyalty and conversion rates while optimizing profit margins. → Simulated Scenarios AI allows companies to simulate different pricing strategies and predict their outcomes. This way, businesses can test new approaches without taking unnecessary risks. So, how can you leverage AI in pricing? → Start Small Begin by integrating AI tools that align with your existing pricing strategies, and gradually scale as you learn. → Combine AI with Human Insight AI is a powerful tool, but it needs human judgment to adapt to the nuances of the market and customer sentiment. → Embrace Dynamic Pricing Implement AI-powered dynamic pricing models that adjust in real-time based on factors like demand and competitor actions. AI isn’t just a trend—it’s a game changer for smarter pricing strategies. It’s time to stop guessing and start optimizing. How are you using AI to optimize your pricing strategy? Let’s talk!

At $10M+ ARR, You are losing money. Not because of bad product, But because of bad pricing. Why pricing? → Competitor pricing weakens positioning → Pricing doesn’t match customer value → Customers stay on the cheapest plan → No upsells, no expansion revenue → Too few users on annual plans → Enterprise deals lack flexibility → Pricing is never tested Lack of pricing strategy directly affects your revenue. Here are 7 steps to fix it. 1. Audit pricing by revenue segment → Where is pricing suppressing upgrades? 2. Reposition pricing against competitors → Own a category, not just a price point. 3. Expand revenue streams → Upsells, add-ons, usage-based models for high-value users. 4. Charge based on value, not just cost → Align pricing with impact and willingness to pay. 5. Move customers to annual → Build ACV and retention with incentive-based annual pricing. 6. Enable enterprise flexibility → Custom contracts, volume discounts, and deal-based pricing. 7. A/B test pricing regularly → At this scale, small price shifts = millions in ARR gains. At $10M+, pricing isn’t just a strategy, it’s a competitive advantage. P.S. How often are you testing your pricing strategy? ♻️ If you find value, let others benefit too. __________________________________________ Ready for more SaaS pricing insights? Follow me, Marcos Rivera🔔

We reached $4M ARR, then cut pricing ~40% to prioritize retention over short term revenue. Pricing can separate a nice $5M biz and a breakout. If launching a product, here's the tactical way to set pricing, based on your goals: 1. Recognize that pricing strategy is VERY different depending on if you're VC backed or bootstrapped. VC backed can undercut competitors with subsidized low pricing, grab market share, then increase prices over time (see: Uber, Doordash). Bootstrapped companies have no such luxury: they need to make a profit on every customer from day 1 - you need the cash, yesterday. *this post focuses on finding right pricing in a bootstrapped environment* 2. First, figure out the lowest possible price you can breakeven at. Consider all costs involved from bringing on and servicing a customer - from sales and AM, to variable product costs. This is now your absolute minimum pricing. 3. Take 50 calls, get 10 customers, as fast as you can, at whatever cost you can, (above min. pricing). Your first 10 customers aren't about making money, they are about gathering data. Every call is an opportunity to triangulate what people are willing to pay. Try min. pricing, try 3x min. pricing. Try 2x min. pricing for month to month, but say that you can drop that by 30% for 3 month commit. Keep pushing up price until people tell you that is ridiculous. Triangulate towards a price people will pay. 4. Classify these calls by customer type. One type of business might think pricing is ridiculous, whereas another finds it cheap. Make sure you are not letting all of this data get mixed in together. Half of pricing discovery is figuring out who your core customer is. 5. Sign 3 month deals, not annuals (to start). Eventually, you want annuals. But at first, annuals are dangerous. You're looking for data on retention, and locking someone into an annual prevents you from gathering that data. Signing 3 month deals forces the conversation earlier.... are people getting value for the price? 6. Revise. Assuming you care about retention, take note of who is staying on. Be honest that you're trying to find a price that works for them. People like honesty and this will get you more information then beating around the bush. Ask "what pricing would make this a no brainer to commit for the next year?" 7. Get real about what you are prioritizing - short term revenue, or long term retention? There is no singular right answer to this. So many factors come in to play: your end game, how big your market is, how easy/hard it is to attract new customers, and much more. But understand that price/margin and customer retention are opposing forces. Be intentional about what you are prioritizing for. (note: this can change at different times in company lifecycle). In short: - Take 50 calls, throw out wildly diverse pricing to gather feedback - Sign 3 month deals to rapidly understand value to price/retention - Be intentional about what your pricing will drive (margin v NDR)

How to build superior demand models in insurance pricing - best practices: 1. Price tests: It may sound like a cliché, but in demand modelling, data truly is everything. I often see attempts to build demand models without conducting price tests, and the results are just weird. Investing time upfront and using the right technologies and techniques for effective price tests is better. 2. Competitors' Data: How to enhance demand models? Try embedding competitors' data using the Best Price Model - you build a regression model that can predict the minimum price offered for a policyholder on the market. Then, you inject predictions of that model as an explanatory feature in demand modelling. 3. Monotonic Splines: Monotonic splines are ideal for modelling premium-related variables because they align with the typical behaviour of price elasticity. Additionally, they provide the smoothness needed for selected price optimisation algorithms.

Business Use Case for Data Scientists: How would you design a pricing strategy to maximize revenue for an e-commerce platform like Amazon or Walmart? 🤔 👉 Tackling dynamic pricing isn’t just about knowing a few pricing algorithms or throwing around buzzwords like A/B testing. In my latest article, I share three practical steps to approach this challenge: 1️⃣ Define the problem - Identify what to optimize for (revenue, customer retention, market share, etc.) - Ask what drives customer willingness to pay. - Identify what data is available (historical pricing data, demographics, etc.) 💡 Break it down: Pricing decisions should align with both customer behavior and business goals. 2️⃣ Choose the right approach - Use predictive models like gradient boosting to forecast demand. - Apply price elasticity modeling to determine optimal price ranges. - Incorporate real-time data for dynamic price adjustments. 💡 Think critically: What data and tools best capture these patterns? How will they scale to real-world complexity? 3️⃣ From predictions to decisions - Partner with marketing teams to target segments with tailored offers. - Leverage inventory insights to price strategically. - Validate strategies through simulations or small-scale rollouts. 💡 Insights are just the start. Value comes from how you apply them—whether it’s increasing revenue or improving customer satisfaction. ✅ If you’re preparing for interviews or want to understand how data science creates real-world impact, this framework will help you think like a business-ready data scientist (full article in the comments 👇)

If you’re not tracking real-time pricing signals, you’re making blind decisions in a volatile market. Let me explain. 👇 Tariffs, supply chain shifts, and aggressive discounting are creating pricing chaos across durable goods. The brands that win won’t be the ones reacting last…  They’ll be the ones anticipating where the market is moving before their competitors do. With Competitive Pricing Intelligence, you can: ✔ Monitor same-day price shifts in key product categories to get ahead of sudden increases. ✔ Track competitor promotions to see who’s absorbing costs vs. protecting margins. ✔ Identify SKU-level price discrepancies across retailers to optimize your pricing strategy. ✔ Adjust regional pricing and inventory placement to maximize profitability. ✔ Understand consumer price elasticity to know how much room you really have to adjust. If your competitors are adjusting prices and you don’t know why, you’re already behind. Read the full report to see how leading brands are staying ahead: https://lnkd.in/eStrM9NH #PricingStrategy #CompetitiveIntelligence #MarketData #RetailAI