"In this article Maanda Tshifularo discusses how to measure what matters for your strategy and business context”
Part of what sets winning businesses apart from those that fail is the leadership’s ability to cut through the noise and excitement of launching a never before seen product or innovative service, to knowing what the real success factors are.
Measure what matters
Any business can get its success factors wrong, but this especially true with startups which haven’t had the benefit of years of usage, or considerable consumer insights.
When you’re in a startup environment, what you measure is different from what you would in an established business. In a startup context, measures like Customer acquisition cost (CAC) are important to have an eye on. This is because if you are in the early stages of your growth, survival relies on gaining users, but the amount you spend to gain each customer is important.
Eric Ries, author of Lean Startup, cautions against ‘vanity metrics’ to describe metrics that make us feel good about ourselves but do not serve us well in terms of an honest reflection of business health or as a guideline for the next actions we should take.
The Lean Startup methodology helps businesses avert the mistake of building products that sound good, but have no actual demand in the market.
Even giants such a Google aren’t immune to costly miscalculations. The spectacular failure of Google glass was attributed to the company’s failure to identify and confirm user enthusiasm and determine the problems the glasses would address.
One of the key components of the Lean methodology is the ‘Build, Measure, Learn’ cycle which helps businesses learn quickly, and make decisions fast. Fans of this model support it because helps them to determine whether they should pivot or persevere and its agile enough to implement across any business model.
Build-Measure-Learn has been a game-changing technique for businesses that otherwise would have blindly developed products without any market feedback throughout the process. Some companies were lucky and got it right, but many have poured their blood, sweat and tears into products that have gathered dust on the shelf.
The Build-Measure-Learn feedback loop has the following steps:
- Plan your experiment: learn, measure and build – including developing a formal hypothesis.
- Build a minimum viable product, and test it.
- Measure the results against your hypothesis to decide whether you can develop a viable business around your product.
- Learn from your results, and decide whether to persevere or pivot. Then, cycle back to the beginning, and keep on going around the loop as you develop your product.
It’s important to know what your critical indicators are and what they mean. Alistair Kroll & Ben Yoskowitz, authors of Lean Analytics, say a good metric is comparative. They suggest businesses try to compare their metric to other time periods, groups of users or competitors to help them to understand the trend and direction in which things are moving.
They also say that a good metric is understandable; people should be able to easily have a discussion about this metric. If they get confused or are unclear about any aspect of it, then the metric won’t be actionable.
Additionally, a good metric is a ratio or rate. For instance, by looking at a daily metric over a few weeks, you’ll see if there is a sudden spike, a general trend or just noise. And finally, a good metric changes behaviour; for example, having a clear threshold in mind ensures that crossing it will trigger action within the company.
Categorize which key business metrics you could pursue, and make a decision
In Harvard Business Review, Ethan Mollick, points to evidence that advocates for startups engaging in measures such the Lean Startup Method. He points to an experiment conducted by a group of Italian academics on 116 startups, where half of them were instructed to perform rigorous experiments on their startup ideas, generating hypotheses and testing them systematically. The others were taught to do experiments but were not shown how to use the scientific method of hypothesis generation. The first group performed better, pivoting more, avoiding problems, and ultimately generating higher revenues than the control group. Rigorous experimentation is clearly important to startup success.
