Pricing Strategy Insights

We made 5 pricing decisions that turned out to be costly mistakes. 💸 1. We didn't put usage limits on our 14-day free trial.  Having no usage limits made it compelling for people to take advantage of our trial by signing up multiple times and using it as much as they want. Once, we lost $8k in 10 days because we weren't aware of streaming costs and a user pumped 160k attendees into their webinars. 2. We didn't offer annual plans because it was extra dev work. Not having annual plans meant that we couldn't collect cash up front even when customers wanted to pay. It also meant that we couldn't offer bigger commissions with partners and affiliates. We only have time to revisit this 2.5 years after launch. 3. We wanted to be fair and offer prorated credits when users move to lower tier plans. We copied Slack's fair use policy because we loved how customer friendly it was. But offering monthly credits made annual plans more complicated, so we kept putting it off. We created a situation that encourages customers to downgrade so they can get credits back instead of staying at a higher tier. 4. We grandfathered existing customers when we doubled pricing. Before raising our prices, multiple founders advised me to increase pricing for existing customers as well if our product is offering more value. There would be some churn but the revenue increase would be net positive, and it'd get us to profitability faster. I thought grandfathering would be the honorable thing to do. As a result, we missed out on $30k/month of revenue which we could've invested towards growing platform costs and hiring. 5. We didn't limit the one thing that costs us the most on all plans. When we lost $8000 on a trial user, we finally figured out the true cost of a webinar attendee on our platform. Turns out, our streaming costs are signficant especially when people use ads to drive registrants to their eWebinars. The more people attend webinars, the higher our cost per account. We should've had clarity on our costs and applied limits to avoid losing money on supporting high use customers. These decisions were hard to reverse because they required significant dev work and/or delicate customer communications. 🎙️On ProfitLed S2E24, I dove into each one of these pricing mistakes in detail, what we learned, and what we should've done differently. Find this episode on your favorite podcast app. PS. Season Two of ProfitLed is about "Our Journey to $1M ARR", bootstrapping eWebinar. ___ 🔔 I'm Melissa Kwan, 3x bootstrapper with 1 exit. Cofounder of eWebinar, Host of ProfitLed, and author of 'your founder next door', my newsletter on building a company without an abundance of resources or friends in high places.

Early on, I noticed a common pattern—businesses were setting prices based on instinct instead of real market insights. Some underpriced their products and lost profit, while others overpriced and drove customers away. The issue wasn’t the product—it was the lack of a strategic, data-backed pricing approach. AI is changing the game. Companies that overlook AI-driven pricing aren’t just missing opportunities—they’re losing revenue. AI analyzes market trends, customer behavior, and competitive positioning to determine the optimal price that maximizes profit and fuels growth. Businesses that embrace AI pricing strategies see higher margins, faster sales cycles, and better customer retention. Pricing isn’t just a number—it’s a powerful strategy, and those who fail to optimize it are getting left behind. Are you pricing strategically or just guessing? It’s time to make data work for you. #PricingStrategy #AIinBusiness #MarketResearch #RevenueGrowth #DataDrivenDecisions #Profitability

Most businesses think they have a pricing problem. What they actually have is a value leakage problem. The price you set and the price you pocket are rarely the same number. That needs to change. Finance has the data, the mandate, and the cross-functional view to lead the pricing agenda. Here are 10 signs your pricing is leaving money on the table, and what Finance should do about each one: 1️⃣ Your gross-to-net price is invisible during negotiation Problem: Sales reps agree deals without seeing what the business actually pockets. Solution: Build a gross-to-net waterfall and make it visible in every commercial review. 2️⃣ You discount to close almost every deal Problem: Your opening position isn't credible, and your margin isn't managed. Solution: Analyse discount patterns by rep, customer, and segment. Show the marginal cost of every concession. 3️⃣ You price based on cost, not value Problem: Cost-plus tells you what you need, not what the outcome is worth. Solution: Model value-based scenarios and push the team to justify pricing on outcomes, not costs. 4️⃣ Your best customers pay the same as your worst Problem: Your highest-value relationships are almost certainly underpriced. Solution: Run a customer profitability analysis and make the case for differentiated pricing. 5️⃣ Sales owns pricing without Finance in the room Problem: Discounts feel free when there's no margin visibility. Solution: Establish a pricing governance structure with Finance as a standing member, not a reviewer after the fact. 6️⃣ Contract renewals happen without automatic price-ups Problem: Prices agreed years ago silently hold. Solution: Own the renewal calendar and flag every contract where price hasn't kept pace with inflation or scope growth. 7️⃣ Your pricing varies wildly across the sales team Problem: Two reps, same deal, different price, that's a governance problem. Solution: Set and enforce price floors. No discount beyond a defined threshold without Finance sign-off. 8️⃣ You compete on price because you can't articulate value Problem: Racing to the bottom is a symptom. Solution: Quantify the value delivered to existing customers and arm the sales team with the numbers to defend the price. 9️⃣ New products launch at the same margin as old ones Problem: You're funding R&D without capturing the return. Solution: Set minimum margin thresholds for new launches and gate approval on pricing strategy, not just cost structure. 🔟 Nobody owns pricing as a strategic capability Problem: Pricing sits between sales, finance, and marketing, so it belongs to no one. Solution: Finance should step into that gap. The best CFOs don't just report on margin. They protect it. Which of these is your biggest pricing leak right now? ♻️ Like, comment, and repost to help more finance teams ---------- 🧑🏼💼 I am a Partner at Implement Consulting Group 🗣️ Reach out to talk about how finance can drive value

Discounting kills more deals than it saves. How I helped a sales team reduce price-cutting by 62% and increase margins by $2.3M last quarter. Your salespeople are slashing prices unnecessarily. Right now. Every time they do, they're telling the customer: "Our solution isn't worth what we're charging." The problem isn't your pricing strategy. It's your team's price conviction. After working with thousands of salespeople, I've discovered something shocking: The difference between your top and middle performers isn't product knowledge. It's their unshakeable belief in your solution's value. When a prospect says, "That's too expensive," most sellers: • Immediately offer discounts • Start justifying the price  • Lose control of the conversation But your top performers? They lean in. They've mastered what I call "the confidence pause" – that critical moment between hearing an objection and responding. This tiny gap separates: - Profit-protecting closers - Discount-dependent sellers The million-dollar question isn't "How do we close more deals?" It's "How do we close more deals at full price?" Because confidence isn't just about feeling better. It's about selling better. And your revenue results will prove it.

Discounts aren’t killing your profit margins. They’re killing your brand. Bold? Maybe! But after working with high level e-commerce clients, I’ve seen this pattern repeat far too often. Here’s why discounting is a trap and what you should do instead: One client of mine was stuck in a "discount or die" cycle Offering 20-30% off constantly. Their sales were decent, but: - Profit margins? Shrinking. - Customers? Loyal only to the discounts, not the brand. So, what did we do? We threw the discounts out the window and Implemented this no-discount blueprint: 1️⃣ Stack the Value →Instead of cutting prices, we built bundles with exclusive perks: Premium products + personalized add-ons. ↳ Result: 45% higher average order value – no discounts needed. 2️⃣ Scarcity That Matters → We launched limited-edition products Based on actual customer demand. No fake urgency, just genuine exclusivity. ↳ Impact: A 167% increase in full-price purchases. 3️⃣ Reward Loyalty, Not Bargain Hunters → We created a loyalty program focused on engagement: Early access, exclusive content, priority service. ↳ Result: 78% higher customer lifetime value. 4️⃣ Premium is a Mindset → Redesigned their brand story to scream exclusivity: - Behind-the-scenes storytelling - Expert-led masterclasses - Premium unboxing experiences ↳ Outcome in 6 months: ✅ Profit margins: +34% ✅ Customer retention: +56% ✅ Brand perception: +89% Discounts train customers to wait for sales. Value trains them to stay for the brand. P.S. - Want to escape the discount spiral? Let’s build a strategy that scales your profits and positions your brand as the premium choice. Drop a “Yes” in my DMs if you’re ready to level up. (And no, this doesn’t include a 20% off strategy.) But you can Follow me to learn more things about SEO. #EcommerceStrategy #MarketingStrategy #BrandPerception

Forecast accuracy is useful, but it is not the goal. The goal is NOT to predict the future perfectly. The goal IS to make better DECISIONS under uncertainty. The real world is not clean, symmetric, or normally distributed. Demand does not politely follow a bell curve. Lead times are often skewed. Disruptions are lumpy. Supplier delays, port congestion, quality issues, transportation failures, and sudden demand spikes tend to live in the tails. And unfortunately, that is exactly where a lot of the money gets made or lost. A 10% forecast miss is not just a 10% forecast miss. Being 10% too low might mean stockouts, lost sales, expediting costs, broken customer promises, or lost market share. Being 10% too high might mean excess inventory, markdowns, waste, or capital tied up in the wrong place. The error may look similar in a forecast accuracy report, but the economic consequences can be completely different. That is why optimizing around forecast accuracy alone is dangerous. It treats the forecast as the product, when the forecast is only an input. The real product is the decision. How much should we buy? Where should we allocate inventory? Which orders should we prioritize? How much capacity should we reserve? When should we expedite? What service level is actually worth paying for? These are the questions that create value. A good decision system asks, “Given uncertainty, asymmetry, constraints, and economics, what decision should we make now?”, it does not naively ask, “How accurate was the forecast?” Forecasting matters. But decision quality matters more. The goal is the best decision (not the best forecast). The goal is ROI. What would change in your organization if forecast accuracy stopped being the North Star, and decision quality became the measure that mattered?

Everyone wants to win the deal. So they drop the price. Again. And again. It feels like a tactical move. But pricing is never tactical. It’s structural. Every time you discount to close, you’re not just impacting revenue. You’re rewriting your unit economics. - Gross margin compresses - Contribution margin declines - CAC payback extends - Burn increases - Valuation multiples take a hit Let’s put numbers behind it: If you reduce pricing by 20%… You don’t need 20% more customers to compensate. → You often need 30–50% more volume (depending on your cost structure and delivery model) Because your cost base is sticky: - Salaries don’t decrease - Infrastructure doesn’t flex down - Delivery complexity often increases with scale So each new deal contributes less incremental cash. Now zoom out over 6–12 months: - You close more deals → revenue goes up - Margins shrink → profitability declines - You hire to support growth → fixed costs increase And suddenly: → Growth looks strong on paper → Cash flow deteriorates → Runway shortens “We’re growing… so why does it feel harder?” Because growth built on discounting is negative leverage. You’re scaling volume, not value. It also distorts your core metrics: - LTV decreases (lower contract value) - CAC efficiency worsens - Burn multiple increases - Revenue quality declines Which directly impacts: → Fundraising conversations → Investor confidence → Exit optionality And then comes the long-term damage: Market conditioning. Once you anchor yourself as “the cheaper option”: - Pricing power disappears - Discounts become expected - Sales cycles don’t improve, they get harder At that point, pricing isn’t a decision anymore. It’s a dependency. In almost every case I’ve seen, discounting is not the root problem. It’s a symptom of: - Weak positioning - Unclear value articulation - Or lack of conviction in the offer The fix is not “stop discounting.” The fix is: 1. Understand your true contribution margin (not just top-line revenue) 2. Set a pricing floor based on unit economics (and protect it under pressure) 3. Improve value perception, not price competitiveness 4. Disqualify aggressively (bad deals destroy more value than no deals) Because revenue growth can hide a broken economic model. But cash flow never lies. If you’re winning deals by lowering price… You’re not outcompeting. You’re eroding your own business, one contract at a time.

Procurement teams are no strangers to supplier price hikes. But the truth is: Not every price increase is justified. Inflation, tariffs, and labor costs are real, but so is cost softening. And if you're not tracking those shifts down to the commodity and component level, you’re likely leaving savings on the table. This type of insight should be done for every product, component, and direct material. Here’s a simple, repeatable method to push back with facts, not assumptions: Step 1: Identify Commodity Trends ➡️ Track input commodities. The commodities that are part of the products you buy. If commodity/component prices have decreased, that’s your opportunity window. Step 2: Map Commodities to Products ➡️ Connect those commodities to the SKUs and products in your portfolio. How much does the commodity get used in your buy-space? Which goods are exposed? What suppliers are being affected? What products have that commodity? Step 3: Analyze Cost Structures ➡️ Drill into the cost breakdown of every product that uses that commodity. What % of the total cost does that commodity represent? Repeat the analysis for every product that uses that commodity. Step 4: Supplier Attribution ➡️ Now link those products to the suppliers you buy them from. You should know exactly which suppliers are affected. Step 5: Quantify the Opportunity ➡️ Use real market data to calculate what the savings should be based on recent cost declines. For example, if aluminum dropped 15% in the last three quarters and makes up 30% of a product’s cost, that’s meaningful leverage. Step 6: Negotiate with Confidence ➡️ Approach your supplier with the data. Be precise. Be proactive. “We’ve seen a 15% decrease in aluminum prices, which represents X% of your product cost. We’d like to see that reflected in pricing.” This is how you fight inflation without guesswork. 📌 Bonus: Platforms like Kloopify make this process faster, scalable, easier, and defensible. We embed real-time commodity, tariff, and cost intelligence at the SKU level, location, and supplier level, so you’re never negotiating blind. Procurement isn’t just reacting anymore. We’re leading with data. Let’s make sure our suppliers know it. What did I miss? Or what would you add? Let me know!

Stop guessing which discounts to test. Your data already knows. Most brands test discount tiers without looking at what customers already buy naturally. This can potentially be a big mistake. Here’s how to analyze your data so you can test the right discount tiers: Step 1: Pull your data Look at how many customers buy 1 vs 2 vs 3 vs 4+ items. Don't assume it's a smooth dropoff. Step 2: Rate each tier by signal strength • 10%+ buying that quantity = test it • 3-10% = worth testing • 1-3% = probably not worth it (and maybe risky) • Under 1% = skip it Let’s take the example of a wallet brand we helped with analyzing their data: • 88% buy 1 wallet • 4% buy 2 wallets • 7% buy 3 wallets People were skipping 2 and jumping straight to 3. When we dug deeper into those 3-wallet orders: • 50% bought same wallet in 3 colors • 30% bought for family ("dad + 2 sons") • The rest were gifts or small retailers A lot of brands would test a 2-pack discount first. But… Only 4% naturally buy 2 wallets. If you discount 2-packs, you risk pulling those 7% who buy 3-packs down to 2-packs. You might actually lose money. This pattern shows up in a lot of different industries. • Socks: 1 pair or 3+ pairs, rarely 2 • Wine: 1 bottle or 6-pack, not 2-3 • Books: 1 book or vacation stack of 3+ TLDR: Check where demand naturally clusters in YOUR data. Let your actual customer behavior show you which discount tiers deserve tests and which ones are likely not worth testing.

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.