The complexity of using survey data for decision making

Last week I was helping someone dig through self-reported survey data to try to find some interesting metrics to report on.

Oh boy was that complicated...

Even ignoring the self-reporting aspect, the responses were so complex that it took forever just to understand the results of a single question. Even then, we got sidetracked into a whole bunch of other follow-up questions we wanted to ask but couldn't.

In this case that's fine as the survey is compiled only once per year so this is a one-time effort. It did make me think how difficult it would be to use any sort of customer survey to drive short-term decisions.

You could only surface-level questions that you can analyze quickly, anything deep would take so long to sort through that the opportunity could be lost.

I guess that's why so many surveys just end up in a report that no one reads and has no impact on the actual business behavior.

Experiencing that pain reinforced how valuable it is to have behavioral data based on objective data points. Or in non-geek:

Keep track of what people do and measure it.

That also means it's possible to automate the collection and analysis which saves time and can be coded to remove biases.

For Shopify stores, that's how Repeat Customer Insights works.

It'll automatically pull your order and customer data, use its algorithms to organize the customer behavior, and then present you with a clear view of what's actually going on. And it'll incorporate new data as it comes in so you don't have to rely on months-old survey data to plan your decisions.

Eric Davis

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Topics: Customer analysis Customer behavior

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