How To Avoid Common Packaging Mistakes in Pricing

March 28, 2022

Author

James D. Wilton

Managing Partner

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As the lead of the pricing service line at McKinsey’s startup practice, I advised close to 50 startups over a 3-year period. Many of them, getting to that stage of growth through a pricing approach developed by a Founder’s hunch, who were tackling pricing in a strategic way for the very first time. And for lots of them, they weren’t looking for an earth-shattering, convention-defying radical approach to pricing – they were just trying to understand how not to mess up.


This article kicked off a very popular series on the common pricing mistakes startups and fast-growing companies make and what can be done to avoid them. This first installment focuses on packaging and tiering. And since I’m still seeing all these mistakes being repeated by startups, I hope that you can read this and manage to produce tiers without tears.**


**I apologise. Couldn’t resist.

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Most startups I encounter have moved beyond a monolithic offering, and have adopted a “Good, Better, Best” (GBB) approach – creating several tiers of packages which vary by price level or quality of features. This is great – simple tiering creates price differentiation that performs way better than “one size fits all,” and while there are many other approaches to packaging that can be more suitable to mature companies, GBB is simple and readily accepted by customers, which is perfect for startups. A GBB model when well designed is a highly-effective packaging strategy.


The hitch is that often they are not well designed, and that lessens the potential impact. Over dozens of engagements with startups, we’ve distilled down the most common GBB packaging mistakes we see most frequently into the 4 below:


#1: Too much base: Product leaders love their (admittedly awesome) products, which is great. But the side-effect is not wanting to deprive any of their customers of their best widgets, and that leads to entry-level products that are feature-heavy. When the base is too good, the climb to the next paid tier is one fewer customers will make. The result is that customers who would pay extra for some features are quite happy hanging out on the base version. That means more customers at low price points, fewer upgrades, and less revenue.


#2: Pitching to the middle: A big benefit of a 3-tier GBB approach is that there is a middle option, which is a natural landing point for many buyers. But having a good middle option is different from deliberately making the value propositions of the base and premium versions, so that it becomes the only logical choice. Companies that do this will still benefit from GBB’s ability to speed up a buying decision, but will miss out on profitable lower tier business, and monetizing customers who would have paid more.


#3: The Grand Canyon: As mentioned, one of benefits of GBB is the element of simple choice, which makes a buying decision more likely. But what if one package is $20 with 2 users and the other is $20,000 with 10,000 users? That’s not really a choice. The customer will feel forced into the option that is obviously meant for them. Here you are not getting the benefits of the choice-based system.


#4: Death by 1,000 features: We’ve all seen the familiar “check mark” GBB pricing pages which communicate what to expect in each package. When the few “extra checks” point to important and differentiated features, they can very helpful in the decision journey. However, when the difference between two packages is based on a laundry list of 100 line items, the customer doesn’t know what to look at or focus on. This results in confusion, and can often lead frustration, as buyers feel they’re being asked to pay a premium for a bunch of features they don’t value. This gets worse as the price differences between packages is also high.


But all is not lost! There are 6 steps you can take to avoid these mistakes:


1. Produce an MVP entry package: You should design a base package with enough features to satisfy only the most price sensitive of customers. These customers should be truly happy with this product, but everyone else should want more.


2. Tier by the Key Value Drivers – Identify the few drivers that truly impact value perception among your highest willingness to pay segment, and then build your premium tiers around these features. Everything else is just noise. You can now build clear value stories for higher-tiered packages for your sales and marketing teams.


3. Consider Upsell Triggers: Placing caps on capacity or usage by tiers can drive upsell when the customer’s needs outgrow the current tier. Just make sure that, having crossed these thresholds, customers will likely want the next tier’s features, otherwise they will feel forced to buy things they don’t want.


4. Scale Prices Separately: If you’re a B2B provider, use a price metric to scale the price of each package to the customer (for example, package A may cost $500 to a customer with 500 employees, but $5,000 to a company with 100,000 employees). This adds to your ability to price differentiate across a set of customers with markedly different willingness-to-pay, without limiting choice.


5. Monetize Niche Functionality as Add-Ons: Certain features or functionality will be highly valued by a small portion of customers. If you include these in your tiers, two things will likely happen: 1) you will either under-monetize them, or 2) create friction in the buying process for these customers who want the premium tiers, but don’t value these features. Break them out separately as add-ons so they can be purchased by just the customers who value them.


6. Link Tiers to Value: Remember – for many buyers, low-priced offerings signal poor product quality. It’s key to price each tier at the appropriate level for the value the customer receives.


Of course, the devil is in the details and it’s always helpful to seek guidance before embarking on any large transformation. If you want a deeper dive into any of the insights above or to speak to an expert, drop us a note here.


Click here for the full article on the Fuel by McKinsey website.

By James D. Wilton May 28, 2025
Outcome-based pricing (OBP) is one of the hottest topics in AI and SaaS monetization today. Instead of charging customers for access or usage, vendors charge based on measurable results. The idea? Customers only pay when they see real value. It sounds like the ultimate pricing model - perfectly aligned incentives, no wasted spend, and a direct link between cost and benefit. So why don’t more companies use it? Because in reality, OBP is much harder to execute than it looks. It’s been around for decades, but few companies truly succeed with it. That’s because OBP introduces complexity, risk, and friction that can make it more challenging than traditional SaaS models. Here are the five biggest pitfalls of OBP - and what to do about them. 1. Defining the Right Metric is Harder Than It Looks The biggest challenge in OBP is choosing a metric that accurately reflects value - without creating unintended consequences. If the vendor defines success too loosely, customers will feel overcharged. If the metric is too restrictive, vendors won’t get paid fairly. Example: Zendesk’s AI Ticket Resolution Pricing Zendesk introduced AI-powered customer service pricing based on resolved tickets. But customers pushed back - because Zendesk’s definition of a "resolution" didn’t always match what customers considered a real resolution. The lesson? A pricing metric must be: Meaningful to the customer (aligned with their definition of success). Tied to the vendor’s real value-add (not just surface-level activity). Difficult to game or manipulate (or customers will optimize against it). 2. Attribution is a Nightmare (Even with AI) Choosing the right metric is only part of the battle - there’s still another problem: Can you prove that YOUR product drove the result? In many cases, multiple factors contribute to an outcome. If revenue grows, was it because of the AI-powered sales tool, better sales reps, or an overall market uptick? Example: IBM Watson & Salesforce Einstein Both were positioned as transformational AI platforms, but customers struggled to isolate the AI’s impact. They could see business improvements, but couldn’t confidently say, “Watson/Einstein was responsible for X% of that success.” Notably, neither IBM nor Salesforce uses OBP for these products. Why? Attribution is too difficult. If vendors can’t prove they caused the outcome, customers won’t want to pay for it. A better approach: Control more of the process (the more your product influences the outcome, the easier it is to claim credit). Use proxy metrics (if direct attribution is hard, find leading indicators that correlate with success). Offer hybrid pricing (mix base fees with OBP so revenue isn’t fully dependent on attribution). 3. Baselining Gets Messy, Fast Even if a vendor picks the right metric AND can prove attribution, there’s yet another challenge: How do you measure improvement? The problem: Many OBP models assume a static baseline - but in reality, customer environments change over time. Example: Fraud Prevention in Financial Services Some AI vendors charge based on the reduction in fraudulent transactions. But this raises tough questions: What’s the starting fraud rate? (Pre-existing fraud levels may fluctuate.) Should the baseline reset each year? (If the vendor permanently reduces fraud, do they still get paid for maintaining it?) The lesson? Customers won’t want to pay for improvements they believe they would have achieved anyway. And vendors need a way to continuously justify their impact. A better approach: Define clear baseline periods (e.g. compare against the 6 months before implementation). Adjust pricing over time (the vendor’s impact might be front-loaded, requiring a different model in later years). Use tiered pricing (higher fees early, lower fees as impact normalizes). 4. Revenue Delays Can Kill a Vendor Even if everything else works - the metric is solid, attribution is clear, and baselining is fair - there’s still one big problem: Vendors often don’t get paid until months (or even years) after delivering value. This creates massive cash flow risks. Many SaaS companies depend on predictable, upfront revenue to fund operations. But OBP means revenue recognition is delayed, making forecasting difficult. Example: Riskified’s Outcome-Based Model Riskified, a fraud prevention platform, only gets paid when transactions are successfully approved without fraud. This aligns incentives - but it also means their revenue is inherently unpredictable. The lesson? While this approach works for Riskified, not every vendor can afford to wait for long-term verification before getting paid. (Note: Investors may not love it either - Riskified trades at just 1.89x EV/Revenue, a very low multiple for a SaaS company.) A better approach: Charge a mix of fixed fees + OBP to ensure steady cash flow. Offer performance tiers (higher base fees for lower-risk customers, full OBP for riskier bets). Use milestone-based payments - instead of waiting for full verification, charge in phases. 5. Customers Prefer Predictability - Even Over Potential Savings Even if an OBP model delivers better value, many customers still choose predictable pricing over variable costs. Why? Most businesses prefer stable, budgetable expenses over a fluctuating fee - even if the predictable price is technically more expensive. Example: Conversational AI in Customer Support A vendor offering an AI chatbot asked customers to choose between: Payment based on how many conversations the AI fully handled (OBP model). A flat subscription fee. Most customers chose the flat subscription. The lesson? Even if OBP is theoretically the best model, buyers often prefer predictability. The existence of an OBP option, however, can signal vendor confidence and reinforce the value of a fixed-price plan. A better approach: Give customers a choice (some will prefer OBP, but many want predictability). Use OBP as an anchor (show the OBP price, but steer customers toward a fixed option). Cap OBP costs to reduce buyer anxiety. Final Thoughts: OBP Works - But It’s Not for Everyone Outcome-based pricing sounds great in theory, but it’s tough to get right. When structured poorly, it leads to: Customer friction (over unclear metrics or unfair pricing). Revenue instability (due to attribution and baseline issues). Delayed payments (which can crush cash flow). The best OBP models: Pick the right metric - aligned to value and hard to manipulate. Solve the attribution problem - proving the vendor’s role in success. Balance cash flow - with a mix of fixed fees and variable components. OBP isn’t broken - but it’s not a magic bullet. Companies that embrace it need to go in with open eyes and a clear strategy. What’s your take? Have you seen OBP succeed or fail? Let’s discuss.
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