Suppose that someone is searching online for a car engine performance tuning solution or for an engine diagnostics tool. How could the analyst who is looking at the data be in a position to understand what kind of person is typing this search term? Could it be a professional who is searching for new equipment to hire? Or rather a professional who is researching the market for competitive products? Can he/she be a car enthusiast who is looking for a servicing workshop with equivalent capabilities?
The answer to this question could only be provided if the search term is sufficiently specific and the relevant analysis be detailed enough. What if the person is experimenting with versions of his / her own search term in order to end up with a search output that is really useful to him / her? In the process, each one of the searches, probably involving respective clicks, are being counted as independent searches from the search engine.
Let us look at this from the perspective of the service or the product owner who wants to attract new customers. Can the keywords that he / she chooses to employ be detailed enough to match the potential customer’s search term? Keywords that are employed for specific campaigns should strike an optimum balance. They should be aiming to reach a sizeable audience, at the same time avoiding to be too vague resulting in meaningless website traffic.
It is evident from the above considerations that in a digital world where there is a plethora of data, careful analyses of the data sets have to be carried out. This will output to the subsequent data transformation exercise as well as the choice of the metrics to focus on in order to have a good chance to develop powerful insights that have the potential to drive the business forward.
Developing valuable insights takes a lot of effort, using tools and techniques that help to make the leap beyond the obvious and to identify the key to unlock customer behaviour.
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