Experimentation and validation are necessary for risk management in your innovation process. Unfortunately, the methods are not perfect.
We’ve been running validation for multiple businesses, and we’ve put together a list of the most common mistakes and how to avoid them.
Around 80% of startups and corporate innovation projects fail, and what they often have in common is the lack of a market need. Companies spend long periods of time, investments, and workforce on the new initiatives, but they don’t check if the idea is even worth pursuing. The result is one, the project fails just after launch.
Making the right call
Targeted validation of innovation is meant to validate informed assumptions, and provide you with clear future steps. These kinds of experiments fit your solutions to specific market needs. But remember, as perfect as it sounds, these are no flawless. Lack of engagement, sales deficiency, and not achieving the desirable responses can be a consequence of poor validation. What’s even worse, you may not be able to point out what exactly didn’t go well.
- Measurables matter
When outlining a validation of innovation, it’s good to focus on the ‘what’. But it’s just as important as measuring the outcome of the experiment. You want to know where you’re going before you start. A descriptive set of milestones can help you sketch what this metric will look like.
- Clear success criteria
Setting clear success criteria is yet another important step that many innovation teams are known to leave behind. Setting up the right criteria throughout all stages of the experimentation process will help you determine if, and why, validation is considered a success.
Good news! Most information can be reused. Data from past experiments often serves as a benchmark for future validation processes. Just don’t make the data disappear.
- Don’t rush
Control is one of the many advantages of digital validation experiments. These can be conducted quickly, enabling teams to gather consumer data in a matter of hours or days. But we often see, that brands validate wrongly asking for nonsense, or they don’t put enough attention into the answers. To avoid hasty mistakes and irrelevant information, always choose data quality over fast execution.
- Avoid short-term memory
Having clear takeaways and revisiting compiled data can help you avoid the common pitfall of not making the most out of the information that’s already available.
- Don’t fall in love
Successful validation experiments depend on unbiased teams. A good way to keep track of assumptions is listing them and designing an experiment around them. But be willing to be proved wrong. Don’t fall in love with your ideas. Validation experiments are not set to confirm your preferences or reflect your team’s presumptions. They need to give a glimpse of what end-users really want.
- Business rationale and ‘feel good’ metrics
Values such as impressions, reach, or engagement makes you feel good. But these metrics are not where the business is at. Conversion rates, cost per sale, cost per qualified lead, and acquired customers. These are the hard metrics that reflect direct value, so they need to take on the spotlight.
Taking into account these tips to avoid an experiment failure, you’ll have a safety net around your validation processes, and will virtually increase your chances of success.
If you want to talk about your next validating experiment to increase your product’s market-fit, leave your email below. Our consultant will reach out to you.
