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Article Posted: January 14, 2014 By: Evan Wood
The New Year is here and as a marketer (like many others), you’ve made the resolution to get friendly with data and familiarize yourself with analytics.
Good for you!
And you’re not alone. A recent ExactTarget survey suggests that investment in data and analytics tops the lists of priorities in 2014, with 61% of respondents saying they will increase their investment in this area.
What the survey doesn’t mention is exactly where and how those investments will be made, which from our experience with clients, is precisely the information marketers want.
In this first part of a three-part series, we start off with explaining the customer analytics spectrum, understanding which types of analytics make sense and the challenges that lie behind them.
What Are Analytics Anyway?
For the past several years, we have observed the frenzy of activity around Big Data. Though in the author’s opinion, this feverish pitch is somewhat overblown, we have to give credit to Big Data for bringing analytics into the marketing lexicon.
Before Big Data, most marketing departments looked at metrics and reports (at best). Now they want ‘analytics’, which definitely sound much cooler and more strategic. However, in the absence of a clear definition, customer analytics have also become something of a catch-all for the process of generating insight and business intelligence.
So let’s set the stage with what we believe is a comprehensive, well-rounded definition of analytics:
Customer analytics is the process of critically examining data
generated by both passive and active customer behaviours,
and generating insights that can be practically applied
to create or accelerate incremental value for stakeholders.
It’s important to note that our definition refers to both the practical application of insights (we like to keep things actionable), and the creation of incremental value for stakeholders (meaning not just corporate stakeholders, but customer stakeholders too given the nature of marketing as a value exchange).
The Analytics Spectrum
Like most things in life, customer analytics fall on a value spectrum – the more valuable the analytics, the more difficult they are to execute.
However, the reality is that there aren’t shortcuts to the top. Organizations really do need to work their way up the value chain, given that each successive step along the spectrum builds on the insights generated previously.
The spectrum can be illustrated as follows:
Most organizations today are still grappling with Descriptive and Diagnostic analytics – that is, the ‘what’ and ‘why’ of customer behavior.
Understandably, the real buzz is around Predictive analytics and being able to forecast what customers will be most likely to do in the future based on past behaviours. Think ‘next-best-product’ or ‘time-to-purchase’ models as popular examples of predictive analytics (but we’ll talk about many more examples in the next post).
True analytics Nirvana? When you can actually influence customer behavior in a direction to make a particular action happen. That is Prescriptive analytics, something which companies like Amazon, IKEA (think of those impulse buys at the checkout), and BMW (car options and accessories) are very good at.
Once you have an understanding of what analytics are and the range of possibilities, the key is to determine how analytics fit within your organization and your specific mandate.
Where do you fit on the spectrum?
What behaviours are you trying to analyze?
How do these behaviours relate to my business objectives?
What will I do with the insight to drive incremental value?
These are critical planning questions and key success factors in any analytics initiative.
In next week’s post, the second in this series, we’ll tackle a range of common, practical customer analytics that directly tie in to revenue generation. Stay tuned.