{"id":288,"date":"2015-07-22T13:07:26","date_gmt":"2015-07-22T01:07:26","guid":{"rendered":"http:\/\/blog.mirrorwave.com\/?p=288"},"modified":"2017-01-26T10:52:28","modified_gmt":"2017-01-25T22:52:28","slug":"put-some-hair-on-your-bald-metrics","status":"publish","type":"post","link":"https:\/\/blog.mirrorwave.com\/blog\/put-some-hair-on-your-bald-metrics\/","title":{"rendered":"Put some hair on your bald metrics"},"content":{"rendered":"

The aim of many employee and customer feedback systems is to generate metrics so that it is clear what is going on. Trouble is, these metrics are pretty bald and of limited use unless you know WHY things are changing. As they say – something that is interesting is OK – but something that is valuable is so much better because you can do something with it.<\/p>\n

The Net Promoter Score system would be the prime example. At the time it seemed like a breakthrough, but frustration soon built up. Companies realised these metrics with industry comparative benchmarks, whilst initially pretty interesting, were hard to do anything with because they either bounce around without any obvious explanation or stay static, no matter what you do.<\/p>\n

To explain why metrics are changing, a fundamental shift is required in the way data-like feedback is collected and structured. The cross-sectional approach typical of market research tracking – where a sample is taken in one survey and then another different sample is taken in the next – has severe limitations when it comes to explaining change.<\/p>\n

To really understand what is changing and why, you have to join up data (that is longitudinally), so that you are ‘following<\/a>’ individuals. So, now you can understand what’s really going on behind a bald summary statistic like an average, without resorting to esoteric multi-variate analytics.<\/p>\n

For instance, it’s natural to think that if the average for one survey is the same as the previous, that not much has changed and therefore nothing needs doing – but nothing could be further from the truth. We routinely show that there is a lot of previously undetectable churn in sentiment going on.<\/p>\n

For example, the average might be the same, but if you follow the people and see how the score has changed for each of them from last time, a third of the participants may have scored higher and a third lower.<\/span><\/p>\n

Every time clients see this churn in sentiment broken down into individual detail, there’s great interest in seeing the ‘train crashes’ who have scored much lower than last time and they’re pretty excited about learning who has scored them much higher. You can really do something with these joined up individual change stories.<\/p>\n

So isn’t it worth putting some hair on your bald metrics?<\/strong><\/p>\n