5 Critical Questions to Ask Your Customers When Developing a Pricing Strategy

Annika Li • June 21, 2024

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Annika Li

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In the dynamic landscape of B2B SaaS, understanding customer needs and preferences is paramount to success. To highlight this, we’ve compiled the top 5 questions that every B2B SaaS company should ask their customers to develop an effective pricing and monetization strategy. While new, prospective customers are certainly important, some of the best insight can come from current customers that know your products inside out. These are the pivotal questions we ask in customer interviews during our engagements with high-growth companies at Monevate. 



Question 1: What drove your organization to seek a solution? 


Understanding the motivations behind a customer's decision to seek out a solution identifies the specific problems customers are trying to solve and reveals what they value most. This ensures that your pricing strategy reflects the benefits customers derive from your solution, increasing its perceived value. By identifying specific pain points or challenges, SaaS companies can tailor offerings and marketing to directly address customer needs, deliver maximum value, and command a higher willingness-to-pay. 


Question 2: What key attributes do you look for and how do vendors compare? 


Buying attributes are the criteria that customers use to evaluate solutions when assessing vendors. Commonly cited attributes include ease of use, scalability, and quality. Customers want their solution to be user friendly, able to grow with their needs, and reliable. These examples can seem vague because key attributes are typically industry specific and nuanced based on the use case. 


By asking customers what key attributes they look for, you gain insight into what features they prioritize and value most. This understanding allows you to align your pricing strategy with the features that customers are most willing to pay for. Understanding how customers perceive your products relative to others in the market allows you to leverage your strengths and potentially justify higher pricing in those areas. Whether it is specific product features, pricing flexibility, or level of support, aligning offerings with customer expectations allows you to craft your value proposition around what matters most and make informed decisions that resonate with your customers. 


Question 3: What do you like or dislike about your solution’s packaging and bundling options? 


Packaging structure refers to how the product is broken up into separate purchasable units and reflects how customers value distinct functionality within the product. Options include a monolithic, tiered, and modular structure. Monolithic offerings suit startups that have one simple product. As a product evolves and becomes more feature differentiated, a tiered structure may help new customers grow into the product. As you and your customers grow, and you build new products to address their additional needs, a modular structure offers greater flexibility to meet diverse customer requirements. This packaging structure describes how products are organized on the back end. 


Customer purchasing preferences are also important to consider in packaging. Some buyers want choice, while others prefer to be told what they need. If you have customers at both ends of the spectrum, an a-la-carte packaging structure can appeal to both. Customers can choose the specific modules they need, or sales reps can present a bundled solution. 


For multi-product companies, creating bundles on the front-end with products that are most commonly purchased together provides a simple way to sell to customers that prefer solutions. Bundles can be tailored to different customer personas and use cases to streamline selling, while the back-end packaging maintains efficient SKU management. Packaging and bundling enable SaaS companies to create solutions for different customer segments, satisfy various buyer preferences, and establish the foundation for price architecture. 


Question 4: What do you like or dislike about your solution’s price architecture? 


Price architecture consists of two components: a price metric and the method by which price scales. The metric is used to scale the price of packages. Customers seek a metric that is easy to understand, perceived as fair, and linked to value. It is important that the value customers associate with the metric increases as customers purchase more. Predictability is also important – customers want to be able to gauge their expenditure based on this metric and have control over adjusting their usage accordingly. In B2B SaaS, we commonly see user-based pricing and consumption-based pricing. 


This metric can scale in a variety of ways, from a linear model that offers the most variability to a flat fee that offers the most predictability. Additional options include a fixed + linear model, where a portion of the fee is fixed while a smaller per user amount—if the metric is users—is also charged, and a sliding scale, where the price per additional user decreases at set breakage points. Diving into customer perspectives on price architecture shines light on their willingness-to-pay and how they perceive value. The goal is to best meet your customers’ preferences while scaling in a way that makes sense for your business. 

 

Question 5: What Are Your Price Sensitivities and Willingness-to-Pay Thresholds? 


Exploring customers' price sensitivities and willingness-to-pay thresholds provides valuable insights into pricing elasticity and market positioning. Willingness-to-pay gauges what price ranges are acceptable for customers, and we utilize the Van Westendorp method that asks customers to provide four price levels: 


  1. Too Cheap: What is the price below which you would question the quality of the product? 
  2. Inexpensive: What is the price at which you would consider the product to be a “bargain”? 
  3. Expensive: What is the price at which you would consider the product to be expensive, but you would still consider purchasing the product? 
  4. Too expensive: What is the price above which you would no longer consider buying the product because it is too expensive? 

 

By asking this set of questions, you obtain not just one price, but a range of what customers are willing to pay, helping you identify your optimal price. While there is a risk that existing customers might “low ball” their responses, the information remains valuable, especially if the bias is considered. Low balled numbers are also not guaranteed—in some cases, we have seen customers love the product, view it as underpriced, and respond with higher than current price levels. These responses are just one of several data sources used to curate prices. By gauging customers’ perception of value and price thresholds, SaaS companies can fine-tune pricing strategies to strike the right balance between affordability and profitability. 

 

At Monevate, we ask these five questions during the market research phase of our engagements to test out hypotheses before building the pricing strategy. Using these questions as a framework to run your own market study on new or existing products can provide invaluable insight for your monetization strategy. 


By asking the right questions and actively listening to customer feedback, you can tailor to customer preferences in an evolving market, optimize your pricing strategy to capture maximum value, and drive short and long-term growth in a sustainable manner. 


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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