Setting Prices Based on Customer Segmentation

Is Your Tier-Pricing Mean? Or is it above average? (Pardon the stats pun) 🤣 Normal Distribution Curve can guide Data-led changes in pricing tiers. Pricing of products and services is directly Associated with the revenues & profits That your business earns over time. If you are in an industry with a lot of competitors, You should follow the accepted market pricing When you launch your product early on. However, if your product is unique, you will see That you might be leaving money on the table By following the market-driven tiered-prices. So, how do you pivot or adjust your pricing? Start by looking at your revenue data. Revenue per user is what we need. Find the mean X-bar and std. dev. s. As an example, let X-bar = 30 & s = 5. Normal distribution curve shows that: * 68% of customers fall in (25, 35) range. --> This is the middle tier or Standard Plan. --> Shown in orange in the graphic. --> Priced between $25-$35 per user. * 14% of customers will fall in (35, 40) range. --> This is the upper tier or Premium Plan. --> Shown in blue in the graphic. --> Priced between $35-$40 per user. * 14% of customers will fall in (20, 25) range. --> This is the lower tier or Basic Plan. --> Shown in green in graphic. --> Priced between $20-$25 per user. * 2% lower outliers in the (0, 20) range. --> This is not an actual tier per se. --> Represents unwilling-to-pay users. --> Shown in pink in graphic on the left. * 2% upper outliers in the >40 range. --> This is a custom tier for bigger clients. --> Willing to pay on a larger scale. --> Shown in pink in graphic on the right. Actionable Insights: 1. Get clear data on expected revenue per user. 2. Identify Mean, std. dev., and chart them. 3. Apply Normal Distribution principles. 4. Give the best value in middle tier. 5. Offer incentives for low outliers. 6. Differentiate tier experiences. 7. Seek feedback from users. 8. Hire experts as needed. Shifting pricing constantly is bad business practice. However, if you discover that your product is In high demand, it is okay to adjust prices. It helps you to cut out unnecessary costs, And eliminate the lowest value users Who might use more resources. Follow Dr. Kruti Lehenbauer & Analytics TX, LLC for #PostitStatistics #DataScience #AI #Economics tips To improve top and bottom lines in SMBs! P.S.: Which tier do you buy a product at, when purchasing? Would love to hear your thoughts in the comments!

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.

Price elasticity is more than just an economic principle—it’s the foundation of any robust Pricing & Revenue Growth Management strategy. Understanding how consumers and customers respond to price changes is crucial for optimizing profits while balancing market share with EBITDA goals. Traditional pricing methods, such as cost-plus or competitor-based pricing, often fall short. They miss the intricate relationship between price and demand, leading to missed opportunities and diminished profitability. With the rise of AI and ML, price elasticity modeling has become a powerful tool for making more informed, insights-driven pricing decisions at scale. Modern techniques go beyond basic linear models, leveraging vast amounts of internal and external data to provide a nuanced understanding of customer behavior. This allows companies to dynamically adjust prices, tailor strategies for different customer segments, and respond swiftly to market changes. Price elasticity provides the strategic insight needed to optimize pricing, maximize revenue, and protect margins in a competitive landscape by quantifying how demand fluctuates with price adjustments. AI/ML-powered models set new standards for pricing strategies by integrating real-time data and predictive/prescriptive analytics, enabling businesses to fine-tune their pricing approaches in ways traditional methods never could. To integrate price elasticity modeling into your pricing strategy, consider the following steps: 1. Data Collection: Gather high-quality, relevant data, including historical sales figures, inventory data, customer demographics, product reviews, competitive pricing, and other miscellaneous things like weather data. 2. Advanced Analysis with AI/ML: Utilize AI and machine learning to build robust price elasticity models. Approaches like the Double Machine Learning method uncover intricate relationships between pricing and demand that traditional models miss. 3. Customer Segmentation and Strategy Alignment: Different segments of your market will respond uniquely to price changes. By segmenting your customers based on their price sensitivities, you can tailor your pricing strategies to each group, maximizing revenue and profits. 4. Continuous Optimization: Implement small, controlled price changes and monitor their impact using A/B testing and analysis. Use real-time data to refine your pricing strategy continually, ensuring it evolves with market conditions and customer preferences. From our experience guiding mid-market companies through the transition from traditional to modern pricing models, the shift to AI/ML-driven elasticity modeling often results in meaningful gains in accuracy and pricing precision. To learn more, see the helpful links in the comments section. These include free resources that offer Price Elasticity modeling examples in R/Python using linear, ElasticNet, Random Forest, and Double Machine Learning methods.