Price Elasticity Considerations

I've seen countless companies relying on outdated models or gut instincts for price changes.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

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

Bigger isn’t always better—especially with Average Contract Value (ACV). Let’s talk about when chasing ‘big deals’ can backfire… As a head of sales and advisor, I’ve seen a lot of businesses chase higher and higher ACVs. And yes, those big deals look great on paper. They often require complex negotiations and multiple stakeholders, so when you finally land one, it’s a moment to celebrate. But sometimes, boosting your overall company-wide ACV can backfire. Here’s a real-life example: 💸 A company raised prices year after year, causing ACV to climb—investors cheered. 🕶️ This covered rising internal costs and improved margins, which looked good… 🤨 But they didn’t consider competitors or test price elasticity—no one was worried about if buyers would balk... 📉 Renewals dropped, and sales cycles increased, while competitors landed new customers with lower upfront commitments. 🫤 New category competitors delivered the same ROI at lower entry prices, leading to slower sales, down-sells, and lost renewals. 3 Pricing Lessons To Remember ------------------- ✅ Don’t price in a vacuum: You must understand—and test—your market’s price elasticity. 🔎 Know your competition: Arm your sales team with the right positioning, offers, and enablement to win. 🦋 Match value to price: If you’re at a premium, be sure you’re consistently delivering value at a higher level. I've seen both sides of this coin... it's a balancing act, but you absolutely can increase pricing without sacrificing growth. Have you been caught in this cycle before? What tactics have worked—or backfired—in your experience? What did I miss?

“What’s My Price Elasticity?” – The Question That Makes My Face Do This 😵💫 If I had a dollar for every time a client asked me this, I’d be retired on a beach by now. The idea that price elasticity is a single, fixed number is one of the biggest myths in pricing. Most pricing discussions boil elasticity down to a single number—“our product has an elasticity of -1.5” or “our customers are price-sensitive.” But what does that actually mean? What Does a Price Elasticity of -1.5 Mean? Price elasticity of demand (PED) measures how sensitive customers are to price changes. The formula: Price Elasticity=% change in quantity demanded/ % change in price Why Elasticity is Not a Fixed Number??? Many companies assume they have one fixed elasticity value. But in reality, demand elasticity changes depending on the price level, market conditions, and customer behavior. 📊 How Price Elasticity Changes Along the Demand Curve 🔹 At lower price points, demand is often relatively inelastic (e.g., -0.5). Customers don’t pay much attention to price changes because the price is already perceived as affordable. A small increase won’t significantly affect demand. 🔹 As the price rises, demand becomes more elastic (e.g., -2). Consumers start noticing price changes, comparing alternatives, and making trade-offs. This is where price sensitivity increases, and raising prices can lead to a meaningful drop in sales. 🔹 At even higher prices, elasticity can become very high (-4 or more). Here, a small price increase can lead to a drastic drop in sales. Customers start considering substitutes, delaying purchases, or dropping out of the market entirely. 🚀 Why This Matters More Than Ever Many businesses still rely on outdated elasticity assumptions, measuring it once and assuming it stays the same across different price points and customer segments. But in reality, elasticity is dynamic—and failing to recognize this can lead to costly mistakes. Take this example: 📌 If your price elasticity is -2 and you raise prices by 10%, elasticity could increase to e.g. -2.9. What does this mean? The expected drop in sales could be much greater than anticipated, significantly reducing revenue. This is particularly relevant in times of high inflation or economic uncertainty, when customer sensitivity to price changes is amplified. Companies that ignore how elasticity shifts risk setting prices that alienate customers or leave money on the table. ----- 📢 Curious about navigating the dynamic world of pricing and staying ahead of the curve? Hit the 🔔 icon and follow me to receive timely updates on pricing strategies, industry trends, and more!

When prices go up, people usually buy less. Simple, right? Inflation's throwing a wrench in cannabis pricing. And it's made even more complicated with a bit of pricing compression sprinkled in. So, how has Vetrina been running pricing analysis? Using pricing elasticity. It measures how much your sales/volume increases or drops when prices vary or are adjusted. What you want? Price INELASTICITY. It's when you can increase prices without customers switching to a different brand or product. 2024 Inflation has made this tricky to spot ○ Your customer's perception of the value of money has changed ○ Not all customers get price-shy, in the same way → What we consider ■ Relative Price Changes: Prices compared to the overall inflation rate ■ Segmented Analysis: How elasticity varies across different customer segments, sub-product categories, or packaging sizes ■ Time-Based: Elasticity trends, how has it changed this year versus last year ■ Cross-Price Elasticity: How price changes in complementary or substitute products affect the demand of another product (Customers switching from 8ths to prerolls) ■ Keeping an Eye on it: Pricing isn't a one-and-done game → How you apply the data: 1. Optimizing pricing of an assortment 2. Segmenting customers based on price sensitivity 3. Identifying price thresholds to inform product development and marketing decisions → What to look out for: Pricing elasticity as a method assumes all other factors remain constant, which isn't always true. It doesn't capture non-linear relationships (how customers change their behaviour to a different category). Historical data by state may not always predict future behavior because of the variety of economic climates we have experienced in cannabis since the pandemic. So, if you want to raise prices in 2024 – it's about finding that sweet spot where your customers stick around. We write about this and other tactics in the Vetrina newsletter.

𝐏𝐫𝐢𝐜𝐞 𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐢𝐭𝐲 𝐨𝐟 𝐃𝐞𝐦𝐚𝐧𝐝(𝐏𝐄𝐃) (explained with calculations): In August, the retailer price was reduced to 3,939 with sales of 9,634 units. For other months, the price is usually 4,539, and this product contributes 3% of the total portfolio sales. Due to the price reduction in August, the product's contribution increased to 5%. If the contribution had remained at 3%, sales in August would have been 5,614 units, resulting in a delta of 4,020 units attributed to the reduced price. The percentage change in sales is 72%, while the percentage change in price is 13%, giving a price elasticity of demand (PED) of 5.51. Is this calculation correct? How can we predict the sales for September if the price drops by 13% again, or by 15%? Considering demand and price variations may not be linear, how can we extrapolate this? One of my connections asked this question. ----- Let’s break down the calculations and predictions step by step: 𝟏. 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝑮𝒊𝒗𝒆𝒏 𝑫𝒂𝒕𝒂: - Retailer Price (August): 3,939 - Sales (August): 9,634 - Other Months Retailer Price: 4,539 - Other Months Contribution to Portfolio Sales: 3% - August Contribution to Portfolio Sales: 5% - Sales (August) if Contribution was 3%: 5,614 - Delta (attributed to reduced price): 4,020 - % Change in Sales: 72% - % Change in Price: 13% - Price Elasticity of Demand (PED): 5.51 𝑽𝒆𝒓𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝒐𝒇 𝑷𝑬𝑫: a) Percentage Change in Sales = [(Sales in August - Sales if Contribution was 3%) / Sales if Contribution was 3%]* 100 = [(9,634 - 5,614) / 5,614 ] * 100 = 71.60% ~ 72% b) Percentage Change in Price = [{ Price(Other Months) - Price(August) } / Price(Other Months) ] * 100 = [(4,539 - 3,939)/4,539] * 100 = 13.21% ~ 13% c) Price Elasticity of Demand (PED) = Percentage Change in Sales / Percentage Change in Price = 72% / 13% = 5.54 ~ 5.51 Hence, the calculation is correct. 𝟐. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 𝐒𝐚𝐥𝐞𝐬 𝐟𝐨𝐫 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 Assumptions: - The same percentage change in price will be applied in September. 𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝟭: 13% 𝑫𝒓𝒐𝒑 𝒊𝒏 𝑷𝒓𝒊𝒄𝒆 A) New Price for September: = Price(August) * (1-Percentage Drop) = 3,939 * (1-0.13) = 3,426.93 B) Predicted Sales for September: = Sales(August) * {1+ (PED * Percentage Drop)} = 9,634 * (1 + (5.51 * 0.13)) = 9,634 * (1 + 0.7163) = 9,634 * 1.7163 = 16,543.84 𝑺𝒄𝒆𝒏𝒂𝒓𝒊𝒐 𝟮: 15% 𝑫𝒓𝒐𝒑 𝒊𝒏 𝑷𝒓𝒊𝒄𝒆 A) New Price for September: = Price(August) * (1-Percentage Drop) = 3,939 * (1-0.15) = 3,348.15 B) Predicted Sales for September: = Sales(August) * {1+ (PED * Percentage Drop)} = 9,634 * (1 + (5.51 * 0.15)) = 9,634 * (1 + 0.7725) = 9,634 * 1.7725 = 17,076.27 Note: Demand and price variation may not always be linear. For more accurate predictions, consider using more sophisticated forecasting methods or models that account for non-linearity and external factors.

Despite the temporary easing of the U.S. tariff situation, the question remains: how do consumers actually respond when prices rise? At Appinio, we set out to explore that, combining fresh consumer sentiment with modeled demand elasticity based on real pricing thresholds. Our new U.S. Price Elasticity Report captures how inflation, tariffs, and economic uncertainty are shaping behavior today. The report shows where pricing power is still strong, and where it’s slipping. We used an advanced elasticity method that links price perception to actual purchase intent, giving brands a realistic view of how consumers will really behave under pressure. It’s the kind of insight that can guide promo planning, marketing messaging, defend margin, or justify a price move, all backed by data. Key takeaway: even small price increases can lead to significant drops in purchase likelihood, often in categories where consumers don’t expect to be price-sensitive. The full report is live: 👉 https://okt.to/1dUOYv

Incidence vs burden of taxes/tariffs I'm not saying everyone who works in pricing needs a degree in economics, but everyone needs to understand how price elasticity impacts whose pockets the tariffs are ultimately paid from. When goods and services are highly inelastic, price increases have minimal impact on quantity demanded (at least in the short run until substitutes can be made). When goods and services are highly elastic (aka Price Sensitive) an increase in price tanks demand. In order to estimate how inflationary tariffs will be we have to understand elasticity of demand which is NOT uniform across all products and services. As tariffs come they will have uneven impacts across sectors. The Incidence of the tariff is who mechanistically pays the fee, but the Burden depends on how much of the increased cost is passed on in the form of high end consumer price as well as how much total demand evaporates as some products and services become economically unviable to segments of the market at higher prices.

Your pricing strategy is one of the 𝐦𝐨𝐬𝐭 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 levers you have, but also one of the 𝐦𝐨𝐬𝐭 𝐦𝐢𝐬𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐨𝐨𝐝. Let’s break down one of the biggest opportunities hiding in plain sight... As costs increase and economic pressures squeeze profitability, many brands are feeling the heat of margin compression. The natural instinct is to raise prices or run more discounts. But the real challenge? Navigating price sensitivity with precision, not panic. 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐏𝐫𝐢𝐜𝐞 𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐲, 𝐑𝐞𝐚𝐥𝐥𝐲? Price sensitivity refers to how responsive your customers are to price changes; economists call it "price elasticity" — the degree to which a price increase, or decrease, impacts demand. Some products are inelastic (👋 Birkin Bag), but the vast majority (probably yours) are highly elastic and extremely vulnerable to even small price changes. Step one is understanding where your product sits on that spectrum. Step two is using that insight to build a smarter pricing strategy. 𝐓𝐡𝐞 𝐌𝐚𝐫𝐠𝐢𝐧 𝐒𝐪𝐮𝐞𝐞𝐳𝐞 𝐈𝐬 𝐑𝐞𝐚𝐥 Margin compression is accelerating across the board and costs are only going up. Do you absorb it and eat into margin? Or do you pass it on and risk losing customers? Neither option is ideal, primarily because some products can tolerate higher prices, others will see demand fall off a cliff — and navigating that nuance is exactly where static pricing tends to fail. 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐯𝐬. 𝐃𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞 There’s a crucial difference between being forced to discount and choosing not to. A lot of brands are discounting out of desperation, trying to chase volume and salvage cash flow. But that often just conditions customers to wait for a sale, eroding brand value long-term. Conversely, brands with extremely strong positioning can maintain pricing power, but they still need to gauge market shifts and buyer behavior. The goal isn’t to avoid discounting, it’s to be strategic about when you use it. 𝐁𝐮𝐲𝐫: 𝐁𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐏𝐫𝐢𝐜𝐞-𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐲 This is exactly where Buyr can help you navigate price without compromising your brand or bottom line. ✅ Want to raise prices? Buyr helps you test intelligently so you don’t sacrifice your most valuable customers. ✅ Worried about price sensitive shoppers? Buyr creates dynamic engagement, leveraging customer input, so you can capture demand without discounting blindly. ✅ Facing rising costs? Buyr gives you the flexibility to experiment with real-time pricing, without locking yourself into long-term change. With the right tools, you don’t have to choose between protecting profitability and staying competitive. The future of pricing is dynamic, adaptive, and deeply informed by customer behavior. We believe price shouldn’t be a gamble. It should be a growth tool. 𝐁𝐮𝐲𝐫 𝐢𝐬 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐬𝐮𝐫𝐯𝐢𝐯𝐞 𝐭𝐡𝐞 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞, 𝐛𝐮𝐭 𝐮𝐬𝐞 𝐢𝐭 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞. DM me, let’s talk.