The more a company knows about its customers, the better it can serve them. We talk a lot about the potential for big data to pinpoint efficiency opportunities from an operations standpoint, but we’re missing out on the customer service opportunity.
Data used in a customer interaction can be played out in different ways and scenarios.
With food and beverage products, for example, customers have proven to have an insatiable appetite for more information about where and how their food was produced. Paul Chang, worldwide consumer products lead at IBM, wrote on the Smarter Planet blog about how one meat marketer in Germany is using smartphones and QR codes to deliver information about how the meat they’re purchasing was raised and slaughtered.
Westfleisch, the third largest meat marketer in Germany and the fifth largest in Europe, is embracing this trend with a new smart phone application that uses QR codes on meat packages to enable shoppers to find information about the animal’s origin.
Through the app, called fTrace, information about things like where the animal was slaughtered and when it was packed is as readily available as recipes and tips on storing the meat safely. The app also enables consumers to access specific information on carbon emissions. fTrace has already been rolled out in the large European discount supermarket chains Aldi Süd and Netto; and the supermarket chain Lidl is set to begin using it this month.
Westfleisch believes that giving smarter customers additional information that was either difficult or impossible to find out heretofore will help build brand loyalty. Greater visibility into products, supply chains and practices can only improve customer confidence. Equally important is the carbon emissions information, which is stored with all the other data that supports the application.
But there’s a delicate balance that retailers need to be aware of as they turn to customer data to make improvements. Data can be very personal, so it’s important to use customer information in a way that makes the interaction reassuring and not unsettling.
Sean Madden, creative director at Ziba, wrote about his amazing customer service experience with Barbara, an Amazon customer service representative, in an article for Fast Company’s Co.Design website. The fact that Barbara had access to lots of specific data on him was helpful, but the fact that she knew when and how to use that data was even more critical.
When I meet an old acquaintance at a party, she remembers my name and asks one or two questions about things we discussed last time we spoke. The fact that she remembers establishes rapport; the fact that she doesn’t list out every bit of information she possesses makes me feel comfortable. Without even thinking about it, humans are very good at conveying just the right amount of information in personal conversation.
Companies need to do the same. When I spoke with Barbara at Amazon, she had access to plenty of data, but only referenced what was necessary, starting with my name and the problem I was trying to solve. It quickly disarmed my self-defense instinct and made me comfortable referencing facts we knew in common but hadn’t explicitly stated. "Can you send it to the Northeast Ninth Avenue address?" I asked when we got to shipping options, even though I hadn’t asked if she had it on file. "Sure," she said, and I smiled.
Another thing companies need to be aware of when they tap into data for customer service: Not all data is created equal. Just because you have some data on a customer’s behavior doesn’t mean it’s accurate or reflective of the customer overall.
Monica Ricci, director of product marketing for CSG International, wrote about a woman named Pat on the Quaero blog. Based on some data culled from Pat’s online behavior, Ricci painted one portrait of Pat the customer, but by the end of the piece, she revealed a completely different portrait of Pat by cross-checking the customer data on various channels, such as social media.
But a much more accurate view of Pat can be derived not just from the morning spent with the search engine, but coupled with information from other sources like service provider geographical and physical locations, banks, and social networks. By aggregating information from the biggest data set, we can know Pat all the better. Look closely now—what do you notice? Pat is a man!
Lesson learned: Sometimes big data doesn’t always reveal the big picture. So make sure to cross-reference and incorporate multiple data points when building a customer profile.