When a particular measure is used as an indicator of the performance of a system, people may choose to target that measure, improving its value at the cost of other aspects of the system. The chosen measure then improves disproportionately, and becomes useless as a measure of performance of the system.
I just paraphrased Charles Goodhart’s phenomenon about data, reacting to results and increasing of performance based on statistics.
This kind of paradoxical challenges are faced by most startups and entrepreneurs – and unfortunately it’s the case even with established organizations.
Hence it is so important for entrepreneurs to have near term and long term strategies constantly aligned in everything they do – product, go to market, channel development, marketing, customers and partners.
Agreed that you might not be able to do this for every decision – but – as long as you take time frequently to realign the objectives and priorities for your startup, you will still come ahead of many other startups.
Not seeing the forest for the trees is a common behavioral problem we all face in every walk of our life and entrepreneurs end up paying a high price for this – even their startup existence.
So, how could one become better at this? There is no magic “seven step” process to change this. Most of the lean startups – that focus on measure-learn-iterate cycle become victims of this behavior.
Some of the ways I have dealt with are -
- In your data analysis, start measuring it from various view points as well as different combinations. For example – evaluate the data from view points of end users, customers, partners, employees, etc. Similarly, instead of just focusing on one data point as an individual entity, try looking at it in combination with other data points. This results in amazingly different conclusions – which could lead you to different iterations.
- When you want to iterate based on a customer data point or user feedback or site traffic or sales figures, make sure you evaluate the impact of your proposed solution on that particular data point as well as the overall platform or product or site or business or what so ever.