Friday, October 21, 2011

Blog: Night of the Living Net Promoter Score

It’s baaaaaack!

Even though I promised that I’d stop talking about Net Promoter Score (NPS), I just can’t help myself.  In this post, I’d like to outline an approach for NPS that really has less to do with the value of the score than how a marketing organization can make the most of single data point when integrated with other data points.

OK, that sounds boooooorrrrringggggg, so let me put it another way.  You can use NPS to find out who your true friends are.

In my last post on NPS, I described how marketers can use the score as an early warning tool to identify and please disgruntled new customers.  To save you the hard work of clicking a link, I’ll sum it up thusly: by surveying new customers with the single NPS question (“would you recommend us?”) via email, a marketer can create an automated system that follows up with appropriate actions.  Those actions may include member-get-member offers for promoters, discounts for neutrals and offers of support services  for detractors.

However, marketers can also learn more about their customers as a whole by using the same NPS data.  If the marketer can add the NPS scores to the customers’ data files, she can then conduct a simple analysis of what other attributes correspond to high or low NPS scores.  In other words, what other characteristics (demographics, purchase behavior, etc.) are associated with specific NPS scores?

Obviously, this approach rewards marketers with serious analytics chops, but just about anyone can get the basics using a simple pivot table in Microsoft Excel (disclosure: I always need a 24-year-old to help me set up simple pivot tables).  From there, the marketer can create a set of tables or graphs that show how promoters, neutrals and detractors differ from the customer base as a whole.  For you old fart marketers out there, it’s just like banners from a quantitative survey.

For instance, promoters might tend to buy one set of products while detractors might buy another set.  This finding would suggest that the marketer has underperforming products or that the detractors haven’t gotten the right product education.  Or, promoters may come from one section of the country, skew more male or female, have a specific family composition or favor any particular attribute in the customer database.  In turn, each of these findings may suggest another opportunity for overall improvement to the marketer.

As I mentioned up top, this approach has less to do with NPS than it does with employing simple tactics to get the most use out of one’s data.  And this time, I swear, I’m done talking about NPS, unless a comet comes too close to Earth and passes through a sunspot, triggering giant spreadsheets to lay waste to downtown Hartford...

...Sorry.  Halloween is near.

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