Throughout the DIM course, we have had a strong focus on data analytics. With new kinds of data constantly being harnessed for analysis, like voice recording and facial scans, many businesses are overwhelmed with options on how to best assess the road ahead.
I thought it was very interesting how Kaiser included the gym member “survey versus surveillance” data to better exemplify perception versus reality. With surveys, there is an automatic bias as there are only certain types of people that will actually complete surveys. Their answers are rooted in their unique perception of the gym, based on subjective feelings and emotions. With surveillance data, this type of information is mostly objective, but is biased in the fact that only gym goers will make it into this data set. It also lacks the psychological insight, leading to numerous questions of “why” for analysts. As a best practice, and if budget allows, companies need to leverage both observational and survey data for marketing analysis.
The purpose of marketing is to attract customers and the data should support that theory. The analytics surrounding customer acquisition can be divided up into three segments: past, present and future data. Questions like, “What channels give you the best/most customers?” are historical data questions. What type of messaging is most effective would be relevant as a “present” question. What types of customers are most likely to convert is a “future” data question. All three viewpoints should be considered as you craft your customer driven data analysis.
It also must be said that the accuracy and relevancy of data is best at the very last second. These variables decay with time, so marketers need to be wary of the time sensitivity. In terms of customer acquisition, data is only helpful if it is utilized in the short window to convert customers. For example, if Target had waited too long to market to their possible pregnant customer data base, their targets would have already had their baby and possibly converted to another competitive retailer. This data, only 6 months old, could now be rendered useless.
With all the data resources at our disposal, data will continue to create value for marketers and help guide their decision making process. However, data is not a battle between analytics and intuition; data should compliment intuition. The sensible answer for most companies is a balance of data analytics and human judgement.