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Big Data: Every Retailer Knows Their Customers

Every retailer knows their customers.  Unless you run a neighborhood fruit stand, it’s very likely that you have so many customers that you don’t actually know them individually.  You think of them collectively and you segment them into groups.

In a smaller business like a coffee shop, you might think of these segments based on a single dimension such as schedule – “the morning rush”.  Those workday commuters have a different set of priorities and a different lifestyle than your “late-morning coffee clutches” or the grad student who settle into your shop as their “evening study hall”.

Online retailers have customer relationships that are, necessarily, much more transactional than face-to-face retail.  We know our customers best by the purchases they make from our stores and, to some extent, the marketing messages they responded to.  Depending on your business, this typically means you have a handful of datapoints each year for your most active customers and even fewer for your casual customers.

Many online retailers have become accustomed to having limited data on which to segment customers.  They resort to very simple segments with limited utility such as loyalty/non-loyalty.

In pursuit of a more sophisticated understanding of your customers, you may have taken the significant step of building segment personas.  This typically requires analysis of buying patterns on your site, supplemented with purchased demographics data.  Raising your game to this level brings powerful customer insights, albeit at a higher investment of money and time.

Thanks to developments in Big Data, the cost/benefit of sophisticated segmentation is at an inflection point.

Big Data is about discovering patterns and truths that can’t be found in smaller data sets.  Organizations who are applying Big Data capabilities to segmentation are discovering behavior patterns across massive data sets.  By keeping the analysis in aggregate and never sharing identifiable data, individual privacy can be protected. 

The first bit of good news is that by applying large-scale data processing and data science, segments can be created in short order.  With a feedback loop, learning models can continuously maintain segments through changing markets, seasonality, etc.

You've spotted the obvious catch by now.  It's likely that your site alone doesn’t generate the volume, variety and velocity of commerce data necessary to support this type of analysis.  The data processing and data science capabilities required are also quite expensive.

The second bit of good news is that Big Data also creates the opportunity for retailers to partner. Since the patterns and insights generated by Big Data are massive aggregations, they can also be individually anonymous.  Today, a retailer can partner with a company with broad commerce visibility and rich data assets, such as eBay to create segments.  Sharing only macro patterns between organizations, retailers can now have sophisticated segments based on a massive set of commerce data.

While you still may not know your customers as personally as the neighborhood fruit stand, leveraging Big Data to develop enriched segments will help you engage your customers much more effectively.

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