This is the 2nd article in a series of B2B SaaS pricing articles. The first article in the series introduced a new lens for pricing decisions where price moves to the center of decision-making. It then covered Price-Product fit a la how your price should lead to your product and how your pricing strategy should evolve with your product. You can read the article here

Here’s a recap of why it is important for founders to shift from a product-market-model-channel fit to price-product-persona-packaging-channel fit.

A new lens to think about pricing

Most advice founders get on pricing is to consider it an integral part of the product and iterate on it as part of product development. While this advice is right, in many cases, pricing decisions tend to get obfuscated and overshadowed by other product-related decisions. This happens because new features are added to the product every month/quarter, but prices aren’t changed at that frequency. Also, unlike product management, sales, and marketing, pricing has no functional owner in startups. Thus, there’s no forcing function to keep the organization focused on pricing decisions. 

One of the ways to better index on pricing to drive revenue/org growth is to move it to the center of decision-making. This means shifting the lens from product-market-model-channel fit to price-product-persona-packaging-channel fit. This shift is illustrated below. 

In this lens, ‘fit’ highlights the interdependencies between price and the other four key elements - product, persona, packaging, and channel. Here’s what each of the nodes means:

  • Persona: Who is the customer you are selling to?
  • Packaging: How is your product bundled to solve your customer’s pain point?
  • Channel: Where are you selling to your customer?

Let's look at the price-persona fit.

Price-Persona fit

If you’ve spent enough time on Twitter, you would’ve come across this meme highlighting the futility of making buyer personas. But in my view, it’s not so much a criticism of creating buyer personas but of creating non-quantified personas. If you are also doing this, it’s time to shift to a Quantified Buyer Persona (QFB).

What’s a Quantified Buyer Persona

Admittedly, this is not the first time someone is talking about QFB, but we have a reason for doubling down on it. You will find customers willing to pay you $50k/year and customers who will only want to pay you $10k/year. While you need to have pricing to sell to both, more importantly, you want to ensure that accidentally you don’t sell a $10k contract to someone willing to pay $50k. Knowing who is willing to pay more unlocks your ARR.

Here’s a real-life example of this. One of Blume’s portfolio companies sells to both marketing leaders and analytics leaders in enterprises. By building quantified buyer personas, they understood that the marketing leader had a greater say in decision-making and a higher preference for their product's core features. So they tweaked their GTM to target marketing leaders, which bumped up their LTV by $30,000 to $40,000 on average.

Find the right way to segment your customer base

Before getting deep into buyer personas, it is important to segment your customer base the right way, as different segments have different cost structures, perceive the value they get from software differently, and hence you will need to charge them in different ways- will they pay monthly or yearly? Will they prefer to be charged per seat or by teams? 

Geo, ARR, and industry are usually used to segment markets, but you should feel free to be creative. For instance, a Blume portfolio company segmented its customers by ‘source of budget’ to determine how it should charge them. If the source of the budget is central (say, coming from the HQ in the US), they charged the same dollar amount for every seat, whether the end-user was in the US or India. But, if the budget came from each country’s P&L, they offered country-specific pricing to offer customers geo price parity.

How to build a Quantified Buyer Persona

With that out of the way, let's look at what’s a Quantified Buyer Persona (QFB). While it may mean different things to different people, a good QFB typically tells you four things about your buyer:

  • What features do they value the most
  • What is their willingness to pay for those features
  • What is their LTV/payback period
  • How much it costs you to sell to them

Here’s a battle-tested template to get you started.


While a company at any stage can build QFB, it is most relevant for companies at the expansion stage and beyond. (To understand how the pricing priorities evolve through the early, expansion and growth stages, check the first article in this series here). Companies at the expansion stage need to replicate their successful customers at scale, and without a quantified buyer persona, they’ll either be marketing to the wrong customers, which leads to fewer conversions or won’t communicate the benefits correctly to existing customers, leading to high churn.

Finding information to build your Quantified Buyer Persona

The first step is to stop being cute with your buyer personas. Instead of creating Startup Sally or Tech Terry-type personas based on demographics, pull out your spreadsheet and fill in information that affects how your customers use your product. 

Going back to the buyer persona template, columns are the buyer personas you are targeting, and rows are the data points about these personas. If you're just starting or don't have some of this data, it’s fine. Fill it out with your hypotheses of what you know about your customers and then refine it.

The two most critical aspects for which you’ll need to do customer interviews are - feature preferences and pricing.

Feature preferences

Different personas in your segment will prioritize your product’s features differently. A Blume portfolio company selling market intelligence SaaS shares that, for them, feature preferences change even within an industry as they move from one sub-vertical to the next. In the retail industry, for instance, a brand like Apple indexes on the frequency of market intelligence updates rather than the number of brands covered as their market has a small set of players and Apple has a dominant market share. On the other hand, in a fragmented market like fashion, a fashion retailer in Singapore wants a much wider selection of competitors in the market information update, even if the updates come once a day to them.

A sub-optimal way of finding feature preferences is to show a list of features to your customers and ask them to rate them from 1 to 10. If you do this, you’ll end up with unactionable garbage data, as your customers will most likely rank every feature a nine or a 10. Instead, ask them this - “Given our current pricing, which is the one feature if we remove, you’ll stop using our product? On the other hand, which is that one feature, if removed, will make no difference to you?

When you can do this at scale and get responses from 40-50 prospective customers/existing customers, you’ll start seeing the relative preference scores for each feature for different buyer personas, which tells you their feature preferences. To make it easier, you can pick up this template and start filling it out today from your customer conversation notes. (link)


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Willingness to pay

It is well understood that you shouldn’t ask your customer how much to charge, but you still need to understand what they are willing to pay. At an early stage, you can assess your prospect’s willingness to pay by increasing the price in every subsequent deal till enough people say it’s too expensive for them, which is the threshold where you stabilize your pricing. While this may seem a very brave (or foolish) thing to do, Peter Reinhardt (founder of Segment) did exactly this in 2012, when he doubled the price for every new customer and scaled their price from $120/year to $240k/year in just six months (link to the full story at the end of the article).

There’s also a scientific way to test the willingness to pay. First, talk to prospective customers, pitch them your product’s benefits and value, and go through a demo call. Even if your product is not ready, showing them a prototype, video, or Figma concept will also work.

Then ask them what they think is an acceptable/good bargain price for this product. When answering this question, most people will typically lowball themselves. Tag it as ‘bargain.’ Then ask them what price is so high, they will never think of talking to you again - tag it as ‘too expensive.’ Next, ask them at what price the product is expensive for them, but they will still consider it - tag it as ‘good value.’ Lastly, ask them at what price point they’ll think it’s too cheap and worry about the quality of the product. Tag it as ‘too cheap.’ When you do these conversations at scale and map the data in a sheet, you’ll get a chart like the one below.

The price range for your product starts at where the ‘too cheap’ line intersects with the ‘good value’ line - anything lower than that, and you are leaving too much money on the table. The upper end of the price range is where the ‘too expensive’ line cuts the ‘bargain’ line. You’ll find it difficult to convert prospects if you go beyond that range.

Tony Beltramelli, the co-founder of Uizard (Series A startup with $19M raised, backed by Insight Partners and LDV Capital), spent months conducting the willingness to pay study for his product, and here are some things he learned:

  • Work hard to reduce the mismatch between what people think they’ll be paying for and what the actual product does else any pricing feedback you’ll collect will be pointless. Spend time educating them about the product on your calls rather than just collecting data points.
  • Most of your calls will be with prospects or beta-stage customers, who have not used your product at all or used it for a very short time. This will create noise in your pricing feedback as they aren’t fully educated about the product’s value. You should factor this into your decisions.
  • One way to reduce noise if you have a beta product is to map the feedback on pricing with how your customers are using the product and discard red flags. Examples of red flags - someone hits the low willingness to pay bracket but is using your product a lot, or someone says they prefer certain features but use others a lot more.

Pro tips:

  • It’s much better to make willingness-to-pay conversations at the prototype or beta phase because if your customers are not willing to pay for the prototype, it’s unlikely they’ll pay for a fancier version.
  • A simple heuristic to watch out for to know if your product is overpriced is when a prospect asks you for a commercial proposal and then goes into radio silence. If this happens too often, it means that they like your product, but you are selling it at a way higher price than the value it offers.
  • As a founder with a technical background, you may believe the willingness to pay exercise to be an exact science, but it isn’t. Apart from building the charts, you also build a mental model of customers’ feature preferences and willingness to pay. Price sensitivity analysis helps you set your first price as the willingness to pay is relevant during the beta or launch phase. Once you have customers, they’ll tell you if your product is expensive or value for money. 

The future articles in this series will cover price-packaging fit and price-channel fit. You can find links to all the third-party references in this article below.