Pricing Benchmarking Methods

I've seen countless companies relying on outdated models or gut instincts for price changes.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

"Is $20/month too much for our product?" Instead of guessing, we used the Van Westendorp method to find our pricing sweet spot. 4 questions revealed exactly what users would pay (and we haven't touched our pricing since). Here's the framework any founder can steal: 1. Send a survey to actual users, not prospects We surveyed people already using Gamma. They understood the real value of our product, not hypothetical value. Too many founders survey their waitlist or randomly select people who have never used their product. That's like asking someone who's never driven about car prices. 2. Ask these 4 specific questions - At what price would this be too expensive for you to consider it? - At what price is it expensive but still delivering value? - At what price does it feel like a bargain? - At what price is it so cheap you'd question if it's reliable? These create bookends for perceived value. You're mapping the entire spectrum of price psychology, not just asking "what would you pay?" 3. Plot the responses and find where the lines intersect Graph responses from lots of users. Where "too expensive" and "too cheap" lines cross: that's your acceptable range. Where "expensive but fair" meets "bargain": this is your optimal price point. 4. Test within the range, don't just pick the middle The intersection gives you a range, not a number. We ran pricing experiments within that range to see actual conversion rates. A survey shows willingness to pay; testing reveals actual behavior. 5. Lean towards generous (especially for product-led growth) We chose to be more generous with AI usage than our "optimal" price suggested. Word-of-mouth growth matters more than maximizing initial revenue. Not everything shows up in the numbers. 6. Lock it in and stop tinkering Once you find the sweet spot through data, stick with it. We haven't changed pricing in 2 years. Every month debating pricing is a month not improving product. Remember: pricing is a signal, not just a number (Image: First Principles)

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.

Do your pricing research: because guessing can turn out expensive Here’s your complete guide: 1️⃣ Pick your method Relative Preference → helps you understand what features customers truly value Conjoint Analysis → reveals optimal feature/price combinations Van Westendorp → gives you clear price sensitivity insights Gabor Granger → shows exactly what customers will pay 2️⃣ Choose your tool wisely Full platform → Qualtrics Managed panels → QuestionPro Conjoint focus → Sawtooth Team analysis → Conjointly 3️⃣ Follow research best practices ✅ Target 30-50 responses per segment ✅ Keep surveys under 12 mins ✅ Start with internal hypotheses ✅ Test with small samples first This framework helps you hit pricing with precision. No more lost revenue from guesswork - just efficient, accurate research that pays for itself. And remember - good pricing research is iterative, not a one-time exercise. Anything else I’m missing?

Pricing Analysis: Pricing is more than just setting a number—it’s a strategic lever that directly impacts profitability, market share, and customer demand. Yet, many businesses either price too high (losing customers) or too low (leaving money on the table). So, how do you analyze and optimize pricing using data? 1️⃣ Cost-Based Pricing: Cover Your Costs First Ensure your price covers both fixed and variable costs while maintaining a healthy markup. 📌 Formula: Selling Price = Cost + (Cost × Markup %) ⚠️ Pitfall: This method ignores competition and customer perception. 2️⃣ Competitive Pricing: Know Your Market Position If competitors price lower, do customers perceive them as "better value"? If you price higher, can you justify it with brand or features? 📌 Price Difference % = ((Your Price - Competitor Price) ÷ Competitor Price) × 100 ✅ Action: Collect competitor pricing (via web scraping or market research) and adjust accordingly. 3️⃣ Profit Margin & Break-Even Analysis Before setting discounts, understand how price changes impact profitability. 📌 Profit Margin % = ((Selling Price - Cost) ÷ Selling Price) × 100 📌 Break-even Price = (Fixed Costs ÷ Sales Volume) + Variable Cost per Unit ⚠️ Warning: If your price is near break-even, excessive discounts can erase your profits. 4️⃣ Price Elasticity: Will a Price Change Affect Demand? If you increase the price by 10%, will demand drop by 5% or 20%? 📌 Price Elasticity = (% Change in Quantity Demanded ÷ % Change in Price) ✔️ Elasticity > 1 → Demand is sensitive to price (luxury items, non-essentials). ✔️ Elasticity < 1 → Demand is insensitive (necessities, brand-loyal customers). ✅ How to measure? Look at historical data, conduct A/B tests, or survey customers. 5️⃣ Dynamic & Tiered Pricing Strategies Smart businesses use data-driven pricing to adjust prices based on demand, seasonality, and customer behavior. 💡 Examples: ✔️ E-commerce platforms use real-time pricing based on competitor trends. ✔️ Subscription businesses offer tiered pricing for different customer segments. ✔️ Retailers adjust prices based on demand fluctuations. ❓ How do you approach pricing in your industry? Let’s discuss in the comments! 🚀 #Pricing #DataAnalytics #BusinessStrategy #PriceOptimization

Price benchmark and positioning is one of the most important aspects for a new fashion brand launch. More so if it is an international brand launching in the diverse and competitive Indian market. The key benchmark of course would be the brand's base market price positioning as a starting point. More importantly to consider its global competition brand’s existing price positioning in India. And try to marry both outside-in and inside-out perspectives to identify that sweet spot in the market. Just applying a multiple on to the brand’s base market pricing for India may not suffice to cut through. It’s more nuanced than that, below are some key factors to consider: 🔸Brand's own market price positioning and aligning India pricing with that. M&S had to revise and reduce its pricing within a few years of its launch in India back in 2001, to align more with the market and be competitive. 🔸Brand’s global competitors pricing in India and their positioning vis-à-vis brand’s global benchmark. For example, a European denim brand starting 100 euros mrp planning to launch in India, would need to see its price benchmark with Levi's both in Europe as well as in India market to compare and align accordingly. 🔸Net landed cost including custom duty, freight etc and India sourcing mix requirements to reach ideal gross margins while maintaining global product standards & price competitiveness in the local market. Many leading international fashion brands operating over many years in India have successfully been able to offer that with scale and continue to grow. 🔸Pricing basis product perceived value, core vs fashion, categories etc and may price at a premium as/if needed, or sharper to try and sell more on fullprice and less on discounts. Zara entry price products in India are priced much sharper vis-a-vis higher price products in comparison with global price benchmarks, just to cater to that sweet price point for its TG. Thanks to social media, today customers are well informed about brand price positioning in the global market and would compare its pricing in Dubai, Bangkok etc or even the EU and US markets with the one in India, and make their shopping choices accordingly across brands and markets as accessible. Sharing snapshots of SS25 season men's t-shirt basic entry price point comparison for like-for-like style across brands in India and its global base market for perspective. Your thoughts? #Pricing #Positioning #Benchmark #Fashion #International #Brand #India #Market #Launch #Strategy