Customer feedback is not created equal: Using qualitative and quantitative research to identify feedback that truly drives products forward
Imagine that one of your customers posts on social media that your company should make a change to your sign-up process.
What do you do?
Now, imagine that a second and a third customer call you with the same request.
What should you do now? Well, as it does so often: it depends. From my experience, it depends on these three considerations:
- Representative sample: Is this the view of a few or of many desired customers?
- Desired customers: Are these customers desired or undesired customers?
- Impact assessment: Could implementing this insight significantly contribute to a business objective?
1.Representative sample: The few or the many?
Social media, app reviews or call center feedback can be a great source for customer insights. However, it is vital to confirm that the provided feedback is not just the view of a loud minority — even if we assume that these customers are all part of a desired customer cohort. I’ve selected these feedback channels as examples as they are prone to selection bias. Selection bias occurs when only certain customers share their point of view — in this case only those customers who proactively elect to provide feedback. This group of proactive customers may or may not represent the view of the entire customer base. This is why validating such customer feedback — before acting on it — is of core importance. Typically, this means validating the feedback with a representative sample through customer surveys and cross-validating the feedback with data insights from the sign-up process itself.
2. Desired customers: What makes a customer desirable?
Just like feedback, not all customer are created equal. Often, certain customers are ‘better’ compared to other customers. The crux is in defining what makes a customer desirable. From a business perspective, desired customers are likely profitable at the current price. The other perspective is around customer-brand fit. Here, the question is what type of customer does the business define as its target customer. Frequently, companies use demographic data to define their core customer cluster. In addition and to get a more refined understanding of the customers, these clusters should also include persona archetypes (behaviors, desires, and motivations).
In practice, an argument can be made for having the persona archetypes follow the profitability assessment and vice versa. Either way, defining desired customers can be as important as defining undesired customers. Having a shared understanding of both customers sets can really help your organization separate the insights from the noise.
3. Impact assessment: Does it even matter to the business?
Great question. Let’s consider a few examples. Having a sub-optimal sign-up process that prevents desired customers from buying a service is likely something that a company with growth targets will take very seriously. However, if the feedback was regarding a font size in an app’s subpage, it’ll be more difficult to articulate the need to implement any change at all. So the core question becomes: What are the required resources to get it done, what is the expected impact, and how is the impact measured?
At large scale, mature companies might be able to observe a significant impact on retention in both cases.
Achieving product/market fit: The benefit of getting it done right!
Product/market fit is achieved when around 40% of your customers would be ‘very disappointed’ if your product stopped to exist. Achieving product/market fit is not only a milestone for most start-ups but important for companies of all shapes and size. Achieving or maintaining it is made easier when you:
- distinguish valuable customer feedback from white noise
- build your product primarily for desired customers
- focus on insights that impact key metrics
Failing to do this, can not only reduce product-market fit, but jeopardize the future of the business altogether.
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