Once you have identified your accelerator ideas and performed a high-level discovery, you’re ready to start confirming your hypothesis with validation experiments. Below are four types of experiments which are excellent sources of customer insights to build confidence in your value proposition. In many cases, rapidly using these techniques can lead to critical early course corrections based on customer reactions.
Clickable Prototype
There are many tools and templates that can be used to quickly stand-up convincing prototypes (e.g. Figma). When combined with a script, these can be used to gather feedback from customers. Walking the customer through the story flow can help provide hints into where customers get confused, or where they fail to see the value proposition.
Single Feature MVP
A single feature minimum viable product (MVP) provides the minimum capability to test the assumption for your accelerator. If this fundamental feature isn’t well received or isn’t demonstrating value, it’s time to reexamine your strategy. Avoid the temptation to build anything more than is absolutely necessary to show how you solve for a high impact pain or gain. Although this is a relatively higher cost experiment, it provides very strong evidence about the potential success of your accelerator (especially if your MVP was paid for).
Mash-up
Use a mash-up to demonstrate your accelerator idea using a combination of existing services. While functional, only the minimum levels of integration to tie the solutions together are made. Although this is a higher effort experiment, it provides strong evidence from customer feedback. If customer feedback is good, and they are seeing value being delivered, you’ll have greater confidence for more experiments, and potentially hardening the solution.
Wizard of Oz
Technically minded entrepreneurs and engineering background professionals often rush to automate solutions. Resist this temptation by creating a customer experience manually to confirm value. Although the solution does not scale, it is ideal for learning what steps are required to earn gains for the customer and address pain points. You will need to consider both lead time (order time to delivery time) and cycle time (time working on the request) when building an experiment, as well as what kind of “digital curtain” will work best.
Explainer Video
Not ready to commit to any of the above more involved experiments? Consider starting with an explainer video, which can have a reduced cost to build. The video can focus on the narrative that explains how and where value is achieved, as opposed to visualizing a high-fidelity end-user experience for a tool. It’s important to not over invest in this type of discovery experiment, as a high-production quality video can be costly. While a high-quality video is helpful for driving traffic, it is more important to gather evidence about accelerator risks before committing.