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Olga Spaic

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Customer-Centricity and Big Data: Marketing Basics Still Apply

Olga Spaic  |  29 Jun 2012, 10:25 PM
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How can big data help companies become more customer-centric?  At the Wharton Leadership Forum yesterday in Seattle, Wharton School Professor Peter Fader and Jarvis Bowers from Microsoft’s IEB unit discussed the various ways companies are using data to segment their customers and provide unique, value-added experiences.  Though many companies collect data on customers, very few know what to do with it – and when they think they do, they tend to oversimplify it by slotting customers into generic segments.  Does it matter that a 20 year old and an 80 year old belong to different demographic groups if their observed behavior is the same?  This is just one of the questions brought up during the discussion. 

The conversation was hopping and covered a wide range of topics, but I jotted down a few takeaways.  No statistics background required – this is marketing common-sense.

Customer-centricity is not about doing everything for every customer.  It’s about knowing which customer is more valuable and perhaps treating those customers just slightly better than the rest.  Professor Fader pointed out that Starbucks and Nordstrom’s, known for their great customer service, roll out a “generic red carpet” and make everyone feel good for stopping into their locales. But he says, being “nice” is not a competitive advantage – it can be easily copied.  Instead, Fader suggests trying to attain a clear understanding of the customer and their value to the company as a way to build its competitive advantage.  In that vein, he mentioned a new Neiman Marcus app that alerts store staff when customers come into the store – presumably, high-value customers would sound the alarm quickly and get immediate attention from staff.  So much for anonymous browsing! 

On the other hand, Fader thought that Amazon.com does a great job of maximizing revenue based on customer behavior and is much more selective with its customer perks.  Bowers, however, countered by saying that Amazon doesn’t always get a 1-to-1 relationship – in the instance of their household for example, all four family members use the same Amazon Prime membership.  The parents and kids have individual preferences that are not necessarily reflected in this household-to-Amazon relationship.  I would contend that Amazon can still parse that data to determine the specific preference of the household members – a children’s toy or book is obviously something that is a preference of a child in the household, just like a business book is likely to be a preferred item of Bowers himself.

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If you ask customers for data, do something with it.  Bowers noted that many companies start loyalty programs but don’t actually provide any valuable information or perks to those “loyal” customers.  Customers expect a company to engage with them after they have provided an email address.  One of the more interesting examples of a clear value proposition early in the Internet era was ESPN.com – avid sports fans had to create online profiles to participate in the site’s fantasy sports leagues.  ESPN.com provided a forum for engagement, all the while collecting reams of data on their customers – both the information they volunteered in their profiles as well as clickstream data on their behavior.  This was a win-win for both the users of the site and ESPN.  Fader pointed out that loyalty programs can’t be “if you build it, they will come” – they really need to expand upon your company’s value proposition and look for ways to engage customers so they can be repeat customers, influencers of others in their purchases, and so on.  In today’s Internet era, loyalty programs may not have as many rules and a very rigid structure but they still need to follow this concept of “engagement” – if you put up a Facebook page, don’t just focus on getting Likes, but also spend time on engaging with those fans.

You don’t need all imaginable data, just the data you will use to make smarter business decisions.  Big data  presents a unique problem for many companies – instead of thinking strategically about the data they need, many companies try to collect as much data as they can and then try to figure out what to do with it.  Instead, both Fader and Bowers recommend thinking about data more strategically – what do you really need to do know to make smarter decisions? This is especially important in the context of privacy and personally identifiable information (PII) – customers expect something in return in exchange for giving their PII, and they expect you to use their data judiciously.  So the less you ask for, the easier it is to be a good privacy steward too!

Figuring out customer lifetime value (CLV) is worth the effort.  Knowing the CLV and the aggregate customer equity (sum of all of the CLVs of your various customers) can help you prioritize your company’s investments over the long term.  Many companies tend to talk a lot about CRM, 1-to-1 marketing, and using data to determine customer lifetime value, but very few actually take on the hard work of creating the right models to truly measure CLV.  Bowers said that this is paramount for companies in ultra-competitive spaces as they need to extract value from every customer interaction – it is not a “nice to have” for them.

There was no shortage of examples from the real-world business community – the  discussion touched on a number of other different brands including American Express, Apple, Orbitz.com, Tesco, Paris Casinos, and Bonobos.  Both the academic Peter Fader and the practitioner Jarvis Bowers had a similar parting thought – focus on blockbuster customers not blockbuster products.  Use the data you have on customer behaviors to figure out how to both maximize and enhance the relationship with your customers and extract more value from those relationships. 

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