Affinity‘s Co-Founder, Ray Zhou, wants to bring relationship intelligence to the world because every opportunity begins with a relationship.
Finding product-market fit (PMF) is one of the greatest initial challenges that every startup faces. It’s a multistep journey and checklist—from generating demand to closing your first customers to onboarding them to making them manically successful with your product.
One unsavory reality about this journey is there’s no such thing as partial credit. Founders only achieve PMF if and when they succeed at proving all of the steps in the journey. The step that so many startups falter at is the final, often-underestimated hurdle: customer success (CS).
When describing seemingly successful startups running into high churn, Success Venture Partners founder and managing partner John Gleeson said he “noticed a pattern forming, and it quickly became clear: these weren’t capital ‘C’ Customer Success issues—rather, they were product-market fit problems or small ‘c’ customer success issues.”
It’s a common occurrence for venture-backed startups. Sakib Dadi, partner at Stage 2 Capital, told us: “We see a lot of companies with really high growth rates, but when you look under the hood, they have very poor retention—like, not close to 100% with logo or net retention. Usually, that indicates there’s a problem.”
What can founders do to avoid this trap and reach CS—the true measure of PMF? It’s a common misconception that CS is largely qualitative and can’t be measured. My experience as a founder showed me there’s a framework that allows you to quantify and scale. Here’s how to approach CS in finding PMF.
Understanding Customer Pain Points (And How To Measure Them)
In Affinity’s early days, we didn’t have a fully built product, but we did have a hypothesis that we tested over a large number of interactions with our early customers. Every pilot customer had a constant meeting cadence, most commonly weekly but sometimes daily.
For each customer, we started by asking them to clearly articulate all of the reasons and pain points for their signing up. Then, we turned these into metrics. For example, if they told us it was because they hated data entry and avoided it (meaning key details didn’t make it into the CRM), we would devise a metric that supported solving that pain point.
Some success criteria are easy to measure because they naturally fit into a quantitative metric. For us, that might be a metric like the volume of emails and contacts the product has automatically captured or generated. However, the approach we took works even when metrics are tricky to measure. “It’s hard to stay on top of my network” is a qualitative judgment, but when the customer grades it on a 1-10 scale, that feeling turns into a metric.
This is what begins to systematize the CS process. When a founder has a prototype product, they can ask: For that pain point you told us about, how would you rate how well we solved it? The goal is to work with pilot customers to be super specific about each metric and super specific on how to measure it—pulling in product data to back up their criteria where possible. This provides a common language for whether things are working.
With this framework in place, regular conversations with customers can rapidly both validate progress and unearth when the product is going in the wrong direction. All of this input goes into the roadmap to iterate on the product. The faster you can build, ship and iterate, the quicker you’ll get to a product that customers are willing to pay for.
Taking this approach can also help you hone in on a product’s target customer. When criteria are repeatedly not met, it could indicate pain points that a product isn’t a good fit to fix. The process helps startups focus on the cohort that has the sorts of problems they’re trying to fix and removes outliers.
Using Data To Scale Customer Success
The data from these many customer interactions—when combined with the information from qualitative and quantitative metrics—reveals the user actions and thresholds that drive the “aha” moment, which indicates users are finding value in the product. This matters because a user who finds value is more likely to become an engaged repeat customer rather than one who churns.
A team can use these valuable insights to orient its product development and customer onboarding toward driving the “aha” moments. For example, our goal was to become the system of record for customer relationship management in private capital firms. Affinity syncs a firm’s communication data, analyzing it to deliver accurate relationship insights that improve key workflows like deal sourcing, portfolio management and fundraising.
Our early team leaned heavily on product analytics tools to understand our unique “aha” moments (which were customers setting up a list of people, companies or opportunities and habitually searching for information on contacts and companies before meeting them), which then informed our understanding of how to drive that customer success threshold.
Customer success is the final frontier. Get it right, and you’ve truly landed product-market fit. If value is delivered, customers will renew—and if you can make 10 customers happy, you can make 100 happy. This customer obsession was our foundation, and it still shapes how we scale our company and team today.
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