Avoid these 4 common B2B SaaS Pricing Mistakes to better capture your value

Santan Katragadda • February 14, 2024

Santan Katragadda

Associate

Read Bio

With the advent of the new year, many SaaS companies have been scrambling to revamp their pricing strategies. Unfortunately, while these companies are usually great at building innovative products that create tons of value for their customers, they often lack the expertise to build a pricing strategy that effectively captures that value.


At Monevate, we’ve worked with over 30 SaaS companies to transform ineffective pricing strategies over the past ~3 years, and we see a lot of commonalities in the errors these types of companies make when designing their pricing. In this article, I’ll walk through 4 of the most pervasive pricing mistakes we’ve seen that hold B2B SaaS products back from achieving their potential.

 

Mistake #1: Failing to align your pricing to your business objectives


Too often, we hear of companies choosing their pricing strategies because it’s the “norm” in their industry, and all their competitors use it. The implication being that it is the “right” way to price for that segment.


The truth is, there isn’t a correct way to price for an industry or a vertical. While there may be a typical way of pricing in the industry that customers are accustomed to, your ideal pricing strategy is highly dependent on your business objectives. Given your objectives probably differ to those of your competitors, if you are copying your competitors’ pricing strategies, your pricing is not optimized for you.


Let’s consider 2 companies that have the exact same product and market, but different objectives. Company A’s objective is to increase NRR, while Company B’s objective is to maximize profit margins. 


To meet their NRR objectives, Company A will likely need lower entry prices and simple upsell pathways based on features or usage to see consistent growth. Company B, on the other hand, will maximize profit margins by setting higher price levels with more granular packaging to monetize every possible source of value (and cost!).


As you can see, while these companies are almost identical, their optimized pricing strategies are VERY different. Therefore, blindly following the pricing conventions in your industry without considering your company’s objectives will likely prevent you from achieving them. 


A recent example of a company that used specific pricing decisions to fuel their broader strategic goals is Tesla. I outlined the strategy and decision-making inside Tesla in this article. 



Mistake #2: Designing your packaging by what you THINK customers care about 


When we interview clients at the start of an engagement, we frequently ask them what features customers want, need, and will be willing to pay more for. Product teams are usually very confident in their answers. However, we then validate these beliefs directly with the customers, and I can’t tell you how many times these conversations paint a very different picture of what drives value for the customer. 


Your packaging should be driven by the needs of your customers. Since most companies don’t know exactly what their customers care about most, you’ll likely need to conduct proper market research to determine this. If you design your packaging based purely on assumptions, there’s a good chance your packaging won’t be optimized and will be ineffective at upselling or price differentiating across your customer base. 


Simple survey questions can be used to get deep insights on the real purchasing drivers, including categorizing your features as:


  • Core – features that many customers value but consider standard. These should be included in base packages.
  • Premium – features that a lot of customers will pay extra to get access to. These should be included in higher tiers.
  • Niche – features that not many customers value, but those who do would be willing to pay extra for. These make ideal add-ons.
  • Ancillary – features that customers don’t see the value in, and shouldn’t be discussed. Using this categorization will help you design an extremely value-aligned packaging approach that will likely increase both adoption and upsell rates.


You can read more about this and some other common packaging mistakes here!

 


Mistake #3: Building the pricing for NOW, not for TOMORROW


SaaS pricing strategies should hopefully have a lifespan of at least 2-3 years. However, most growing SaaS companies typically launch new features and improve their products all the time. Therefore, if your pricing strategy is only built to monetize the product you have at the time of launch, chances are that it won’t work well for the product 3 years down the line.  Pricing strategies should be built sustainably, and that means considering the development roadmap to make sure they are able to capture additional value as new functionality is released.


When redesigning your pricing, you should consider the major features / capabilities you’re likely to release over the next few years and plan for where these features will fit in your new model. Some features may get absorbed into existing tiers, while others may target a new customer segment and require an additional module. Having this level of clarity in advance helps you avoid either under monetizing new functionality or requiring an early packaging redesign that can drain time and resources as well as frustrate your customers.

   


Mistake #4: Setting list prices too low


Startups, especially in earlier stages, tend to set their prices low when they feel their product isn’t “fully baked”, and/or to gain early traction and build their user base. That’s fine in principle, but it can cause problems when these low prices are achieved by setting the “list” or “sticker” prices at a very low level.


Your list prices are not just prices – they’re a signal of the value you provide. As you get started, low list prices can actually decrease adoption as prospective buyers may assume the product has low value since the list price is so low. Customers that end up adopting get anchored around low initial prices levels and feel that it’s the “fair” amount to pay for the product’s value, even as it grows. Consequently, they may become resistant to the large price increases that the SaaS company may feel are necessary as the product is built out more. 


These issues can be avoided if companies set list prices higher to reflect the expected value when more critical functionality is included. In the early stages, discounts can be used to lower the prices and increase adoption without having as large of an impact on value perception. During renewals, customer success teams will have an easier conversation explaining a reduction in the discount than justifying large increases in list prices.


---


Designing a pricing strategy is a daunting task, especially knowing how much is at stake. Avoiding these 4 mistakes (among others) can help innovative SaaS companies build a more effective pricing strategy that captures a fair portion of the value they create. As you explore new pricing and packaging designs in the new year, let your strategy be as carefully crafted as the product it supports.


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.
By Annika Li March 24, 2025
Customers love integrations but not paying for them. How to structure API pricing to align with perceived value while maintaining profitability?
By James Wilton March 18, 2025
AI has changed everything—from how we work to how we interact with technology.
By James Wilton February 11, 2025
The following is an edited excerpt from Capturing Value: The Definitive Guide to Transforming SaaS Pricing and Unshackling Growth, the new book by Monevate founder James D. Wilton.
By James Wilton October 1, 2024
James argues that Canva's 300% price increase for Teams is reasonable, but poor messaging, lacking empathy and fairness, fueled customer backlash, underscoring the need for better communication in SaaS pricing changes.
September 6, 2024
While GenAI remains the hotbed of innovation in tech, the level of innovation is not as high for AI’s pricing strategies - dive deep into GenAI pricing insights and strategies!
By James Wilton August 19, 2024
James D. Wilton's 7 B2B PLG Commandments guide effective pricing strategies for SMBs. After covering packaging in his first article, he now explores critical aspects of PLG pricing through commandments four to seven.
By James Wilton August 19, 2024
Product-led growth (PLG) is effective for targeting SMBs, but requires a carefully crafted B2B pricing strategy. James D. Wilton outlines 7 commandments of B2B PLG pricing, focusing on the first three that deal with packaging.
By Max Baughman August 14, 2024
Behind the scenes pricing is where the real money hides in plain sight and implicit price metrics are the unsung heroes of B2B SaaS revenue optimization. Learn all about them and how to ethically implement this powerful strategy.
SHOW MORE