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Customer learning for product success

There are a number of reasons why great products fail – it happens all the time. It’s easy to think that success is linked to how much a team knows at the start of a project; however, the potential to develop new skills, learn by doing, and adapt to what is learned is equally, if not more, important. Tackling problems, exploring opportunities and making better decisions comes from a culture that encourages teams to make a learning opportunity out of every action. Even when we try to minimise risk by testing our ideas and running experiments, we don’t always get it right. A common mistake is to confuse a good prototype result with a strong affinity for the problem, or customer demand for the solution. Each of those – problem, solution, demand – are separate concerns that require different learning approaches.

Validating the problem

It might sound obvious, but before we do anything else, we need to ensure that the problem we are trying to solve is indeed one worth solving. There needs to be a significant number of customers who have a genuine pain point, or a process in an organization that is so inefficient that it needs an overhaul.

No matter how confident you are in your solution, it’s critical not to get carried away with your ideas before getting to grips with the real issues facing customers. It’s astounding how many products fail because they solve a 'problem’ that no one really cares about.

Mapping out the customer experience and the processes involved are important steps for deeper learning. These two methods are worth exploring:

  • A great way to create a shared understanding of customer interactions and pinpoint problems along the way is Experience Mapping - a visualisation showing the outside-in view of the complete customer journey. In essence, it’s a map that exposes key insights and allows you to build a more seamless customer experience.
     
  • Value Stream Mapping plots the inside-out view of everything that happens in the organization to deliver value to customers. It’s particularly useful for identifying and eliminating waste and bottlenecks, allowing for a more efficient process and giving customers more value.
When it comes to understanding customers’ needs, behavior and challenges there are many tools and methods to choose from. Designing a statistically accurate and unbiased survey is a skill that, if well executed, has an important place. But more often product teams gain deeper insight and discover real customer needs through direct observations and open conversations.
 
Interviews with customers are a fast way to test initial assumptions. When done well, the insights you gain will give you confidence in the problem to solve, or reveal the flaws and gaps in your thinking. This is never about certainty to make decisions, and always about confidence to continue through understanding things more fully.

Evaluating potential solutions

Once you’ve got a good grasp of the problem and have thought up ideas for the best solution, it’s time to test those ideas to ensure you’re on track to a desirable outcome. The approach you take to evaluate your solution will depend on the time and resources available, your budget and whether you are building a new product or optimising an existing solution. Here’s a summary of some approaches to consider:

  • A concept prototype is a throw-away sketch or mock-up for rapidly exploring concepts with customers. It’s low cost and often gets more honest feedback because the sketches are low effort.
     
  • A high-fidelity prototype is a detailed and interactive mock-up of the product experience and helps validate the interaction design, content, look and feel.  Based on the feedback received it’s easy to iterate and build upon.
     
  • The concierge model is a personalised service provided to a small cohort of early customers to learn what works before building an automated solution. The constraint with this approach is that it’s difficult to scale and there is a risk of building for a niche, because the cohort might not represent the broader market.
     
  • Although high-cost, a working prototype tested on a sample of real customers can be a great way to learn what really works, especially when building new products.
     
  • When it’s an existing product or service, quantitative analytics and split/multivariate testing create feedback loops that help agile product delivery teams decide what to do next.
Prototyping exercise

Testing market demand for a solution

The solution we build must be technically feasible, commercially viable and perfectly timed for market demand. Before spending money to build what we think people want, we aim to measure what solutions they actually want or need. Measuring demand helps determine where to invest or whether to invest at all. It’s the reverse of build it, and they will come. Demand validation says, when they come, build it.
 
One way to test this, is by running a marketing campaign before building anything. Try setting up a search marketing ad to direct target customers to a simple landing page about the product.  This facade ad campaign allows you to experiment with different variants to see what resonates. Pairing this with a waitlist registration or pre-order can tell you a lot about who’s out there, and how interested they are in your solution.

To get an accurate feel for organic market demand, before investing in a complete solution, try a Wizard of Oz Prototype. This gives the appearance and experience of a complete and working service, yet all of the back-of-house processes are done manually, mimicking a real-time automated solution.

In summary, customer learning is critical to product success, but it’s no good if we have the wrong mindset. Don’t fall prey to thinking that we test things just to ‘validate’ them and that it’s simply another check-box in the delivery process. Decision making can’t be codified, and there is no certainty when executing strategy. True customer learning means we have to break through the validation mindset. Be clear about what to learn to support the next decision, design the right experiments to learn the right things, and be open-minded and nimble in response to learning things that are unexpected.