Earlier this year, I was consulting at an early stage startup where I worked on an interesting growth challenge. I was working on two problems that were not entirely orthogonal but at the same time complex to tackle together — defining the product market fit to fuel the next stage of growth and simultaneously define a pricing strategy that captures the value created by the product.

It is a complex problem because pricing informs the product and product informs the pricing i.e. the value that the product delivers will help define how much value the firm can capture and alternatively the value that the firm can capture will determine how much value they can invest on the product that can create more than the value captured.

This can be best understood by what is referred to as the pricing thermometer

It is a simple and powerful idea that guides firms to price the product somewhere between COGS and Perceived value of the product for the user. The pricing should be such that it has incentive for the startup to sell by capturing the producer surplus (Price — Cost of producing the product) and user has an incentive to buy to capture the consumer surplus (Perceived value — Price). The marketing $ is spent trying to move the perceived value to the True economic value. TEV = Value of the next best alternative + Positive differential of the product — Negative differential of the product

However, when it comes to practice, especially for a startup, it seems a complex problem to solve. Most of the startups handle this by not tackling the problem at all i.e. they focus on creating a product and do not focus on pricing as much. Most often these startups start with some pricing strategy and adjust once they prove the product market fit. However, the true product market fit is an intersection of product, market, and pricing. Without pricing, finding a product market fit is like rolling a square wheel up a hill

Thus by focusing only on product and market, these startups fail to capture the value that they create. They eventually either fail to prove the value to the investors or run out of seed money even before they can pitch to them. Worse, at times frustration creeps in and startups reduce pricing further to capture the market share to prove the product market fit to investors. Especially, in an age where silicon valley is trying to stay away from the growth story and focusing more on the profit story, it becomes even more important to bring pricing into the equation early on.

So how do we price the product to find the true product market fit? There are a few techniques that I recommend in the early stage of the company:

1. Quantitative and Qualitative data: Startups will not have data. Especially not when it is a completely new concept and you are trying to define, develop, and prove that there exists a market for the product category. Sometimes, we do not need huge datasets to learn pricing signals. When the company matures, it makes sense to let a deluge of data feed the ML model and spew out the real time pricing. But initially, use the Quantitative (Conjoint analysis, factor analysis) and qualitative analysis (Interviews, desk research) to get early signals. Even before creating a product, go out and actually interview users. Steve jobs was so right when he said it is not the customer’s job to know what they want, it is our (PMs, founders) job to know what they want even before they do. So instead of focusing on what they want, focus on what pain points they have, how do they solve it currently, how much value would they associate to a solution if there is a product that solves it either faster (a product feature) or with less cost of ownership or with less side effects (in case of medicine). Asking customers and taking detailed notes will help in the next stage.
2. Lean proof of concept (POC): Use that to create a proof of concept. Idea is not to create a product or a feature with 100% fidelity but to create enough that user can actually start using and provide feedback. In design thinking world, this could even be just a drawing or a mock. Let user experience it, capture their reactions, and use that to inform the next phase of POC.
3. Iterate: Use the learning from Quantitative and qualitative analysis to inform the next iteration. Do not take user’s feedback too seriously or as a sign of commitment. You would be surprised how little sometimes we all understand our own preference let alone being able to vocalize it. Non-transitive preferences exist much more than we would think. So it is important to use the early cues as a signal to create another prototype.

Repeat steps 1 to 3 and go slow with adding more features. At every step use the learning to define your pricing strategy. Do not get married to a strategy early on. Be flexible, communicate that to your early customers so that they are ready when you bring a pricing change. They will still try to resist it but they will be more willing if startups have done the level setting early on. “Pricing thus is in motion not set in stone”. I recently was discussing the pricing strategy of a startup with its founder. They had an extremely complex pricing structure. When I asked “Why did you choose this particular pricing”, he said “This is what competitors are pricing at”. Pricing may end up being a battle field, choose your battle field as per your strength not your competitor’s strength. Case in point metromile disrupted the $200 billion dollar car insurance industry with their innovative pay-per-mile model.

Simplifying the pricing structure will help smoothen the sales process, increase the stickiness of the customers, and may give a long term competitive moat. Getting pricing right is as important, if not more, as it is to get the product right. Pricing separates a self funded firm from an investor dependent firm. The startups that are able to get their pricing strategy at the center stage early on will end up surviving this new age of value investing in silicon valley.

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Read our other articles on Product Leadership, Product Growth, Pricing & Monetization strategy, and AI/ML here.