What's Wrong With Taking Survey Data at Face Value?

I’ve been designing survey questionnaires and analyzing the data for so long that I often forget that some people may not be doing the deep dive and asking the hard questions of the data they’ve collected and for which our clients hire us (thank you, you know who you are). Maybe a little exploratory analysis, a tad of correlation, a glance at the verbatim comments and we’re done until the next time. Did we do a survey? Check. Did we do anything with it? Sure, sort of. Do we have a deep understanding of what the data means? Well….

What’s worse than not gathering intelligence from customers and employees?

Coming to the wrong conclusions!

I’m reminded of this fact by two articles I read last week: In This Case, Let’s Examine Dissatisfaction in the February issue of Survey magazine and Guest Perceptions on Factors Influencing Customer Loyalty in the current issue of the International Journal of Contemporary Hospitality Management.

In the case of customer dissatisfaction, the article suggests several calls to action:

Understand whether you have a category problem rather than a brand problem. In other words, your competitive space may allow easy switching with or without loyalty programs so make sure you know what your issue is before investing in programs that will not alleviate it.

Your market strategy will drive a customer’s perception of satisfaction. If you are a low cost provider, you have accepted that a lower level of quality and service is part of the equation. The danger zone you could find yourself in is in trying to be low cost while also attracting a customer who looks for a different level of product and service.

Benchmarking. I’ve never been a fan but lots of companies do it and the swirling vortex that you get sucked into is that you compare your performance to companies who target different customer segments.

Dissatisfaction may not arise from what you do but rather what other, similar companies do that you don’t do. Customers constantly evaluate decisions based on alternatives; some amount of dissatisfaction arises with your product and service even if you are executing your strategy perfectly.

My suggestions for arriving at the best conclusions possible from your data analysis:

• Keep your strategy upper most in mind when designing the project and return to it often when analyzing data. This means knowing who your competition is; who the ideal customer is and what your competitive advantages are.

• Design survey questions to be particular rather than general. The more generic the question, the less likely it is that you have actionable data and the more likely you potentially are arriving at the wrong conclusions.

• Don’t confuse happy with satisfied. If you want to meet a customer’s needs, you are aiming for satisfaction. If you want happy, that’s a whole different level of expectations.

• Perform data analysis from several different perspectives. Not all survey questions should be treated equally in reaching conclusions.

I’ll write more about the last topic next week.