Product-Market Fit: Is It Necessary?

Product-market fit is a fundamental marketing concept that is often misunderstood, especially in startups and early-stage companies still testing the waters and are in the pre-product stage. Back in 2020, we wrote a blog called 7 Steps to Determine Product-Market-Fit. In our most recent Spotlight on Marketing, Udi Ledergor, CMO at Gong and Shane Murphy-Reuter, CMO at ZoomInfo, discussed how product market fit was (quite literally) the one thing marketing cannot fix

We’re all aware that achieving the right product-market fit is about solving people’s pain points and is often associated with reaching the desired metrics for scaling, but in reality, it takes more than that. Pulled from a decade-old definition, many believe the description needs a refresh. Dan Olsen, the author of The Lean Product Playbook, defines PMF as meeting the underserved needs of target customers better than the competition

Some businesses make the mistake of rushing through a product launch without first establishing a consumer base and with the belief that the right product-market fit will magically happen overnight. A dedicated but small customer base isn’t enough to know whether your product solves a problem large enough to generate high demand. 

Marketers need to understand that the product-market fit doesn’t take shape on its own, and as with product development in general, needs to be constantly studied, nurtured, and developed. For instance, Netflix’s early product addressed the pain points of DVD rental late fees and brick and mortar stores. Suppose Netflix wasn’t diligent about keeping their pulse on where the market, they would have suffered the same fate as Blockbuster. Instead, they saw an opportunity to meet a market need by offering streaming while phasing out their physical DVD mailers.

Does your business need a good product-market fit? The answer is both yes and no. 

A Binary Mindset vs. A Full Spectrum – Which One is Better?

There certainly are massive advantages to having a solid product-market fit, but there are alternatives to it that could provide you with more points of view when deciding on what to prioritize.

An example of this is a mental model developed by Rand Fishkin of software company SparkToro, called the Customer Adoption Spectrum. Its goal is to improve and challenge the traditional binary mindset of “fit or non-fit.” The spectrum relies on more than just the product for results. “It helps us understand what kinds of customers resonate with various parts of the product, lets us estimate how much opportunity we have in pursuing different paths and making hard-to-figure-out tradeoffs,” Rand explains in his piece. It gives you more opportunity to be more creative in terms of making your product profitable, as opposed to focusing solely on whether you have a product-market fit or not.

Identifying Your Niche Market

Shane Murphy-Reuter, CMO at ZoomInfo, discussed in one of their sessions why it’s essential for startups to tap into niche markets within a broader segment—something that can help them spot trends in terms of how their product resonates with their audiences. “Identify who the customers are that truly love your product,” Shane advises. “Your average impact will be quite low, or you might find that there is like a niche within there that loves you.” Focus on collecting market feedback and incorporating it into the product. In short: identify what’s working or not for these segments.  

Case in point: Spotify saw the market desire for music streaming but was able to learn from Napster’s legal fallouts by betting that people would pay a small fee for legal access to the music they loved. Spotify now has 34% of the market share.   

The Takeaway

Companies out there have successfully found the right product-market fit and are now thriving in their respective spaces. However, it is not a one-size-fits-all concept and may not necessarily work for all businesses. There’s no need to force it onto your framework if it repeatedly doesn’t drive results—after all, product development is a continuous process and isn’t as fixed as the binary “fit vs. non-fit” model.

 

Image by Austin Distel via Unsplash.

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