In his recent blog for the Harvard Business Review, Gary Hawkins, CEO, Hawkins Strategic, a US-based consultancy focused on gathering, understanding, and using detailed customer data in retail, says big data has the potential to kill all but the biggest retailers. He speaks to The Strategist on the prospects of big data and challenges before retailers
Will big data indeed kill all but the biggest retailers?
As I called out in the article on HBR, big data is challenging all but the largest retailers. It remains to be seen how smaller and even mid-market retailers can respond when today they do not have access to the sophisticated technologies or expensive human resources and skill sets required to work with big data. That being said, technology is rapidly evolving and there may be solutions that come to market that help smaller retailers.
How about a country like India where retail is largely unorganised?
My view is that in markets like India and others where the retail industry is still splintered and the largest global retailers have not yet established themselves, smaller retailers or locally owned retailers have an opportunity to leapfrog the normal cycle of retail growth. An example is provided by the telephone industry: In the US, telephones evolved from landlines to today’s cellular mobile phone systems. In countries like India, it is possible to pass over landlines and immediately move to mobile phones (which is obviously happening). A similar thought can be applied to retail.
Retailers tend to rely on loyalty programmes to beef up their big data repository. What is the next trend in big data management?
The next generation of ‘loyalty’ will be personalised marketing. Retailers and brand manufacturers directing specific promotions to specific shoppers based on their past purchasing history, interests, and other attributes. In the world of personalised marketing, there must be a way to identify the shopper to the transaction; the same shopper ID used to trigger the shopper-specific discounts at checkout. In this sense personalised marketing generates shopper data without the retailer having to have a traditional loyalty programme.
Big retailers are supplementing shopper data with other data sources such as monitoring Twitter and Facetime, geolocation data from mobile phones, appending other demographic and lifestyle data to their shoppers’ profiles, and other things.
Can big data open up an alternative source of revenue by itself for the marketer or retailer?
Yes, it can create an incremental source of revenue. A great example of this is Kroger (the largest supermarket chain in US) and its partnership with dunnhumby (the analytics consultancy). The Kroger-dunnhumby partnership sells insights and analytics garnered from Kroger’s shopper data to CPG manufacturers (like Kraft, Unilever etc.) for millions of dollars each year. Some in the industry estimate Kroger is generating over $100 million annually in incremental revenue from selling data. I want to make clear that this is aggregated data that is analysed; Kroger never sells or releases individual shopper information.
What are the challenges in managing big data?
Two challenges in particular come to mind. Big Data is characterised by the three Vs: Volume, variety, and velocity. It is the last of these — velocity — that is a real challenge. This refers to constantly monitoring real-time data feeds from things like social media (Twitter, Facebook, etc.) or data such as geolocation from mobiles. Monitoring all these data feeds in real-time is a major challenge. The second is related to the first: Using that high-velocity data. To effectively use real-time volumes of data moving at velocity requires creating complex and highly sophisticated systems that can react in real-time to the data feed. As an example, if a retailer knows from geolocation data that a shopper is within one mile of a store, how will it respond? The retailer cannot have a person constantly monitoring the data and reacting by creating a specific message for that specific shopper; that is not scalable. Creating a system that enables an automated response is the way to go but this is a major undertaking.
Any examples that you could share about how companies have interpreted consumer data and how its application has helped them...
I go back to Kroger as a great example. Kroger has just reported 34 consecutive quarters of same store sales growth. This is very impressive given the economic challenges in the US market over the past few years. Kroger’s CEO David Dillon attributes this growth to the company’s use of shopper data and its efforts in precision targeted (personalised) marketing.
I also know the power of shopper data firsthand: as a retailer, I created and operated one of the first true personalised marketing systems and programme in US supermarket retail starting in 2006. Providing savings to shoppers on products they want to buy drives increased shopping, increased shopping trips, and increased customer retention over time. The proper use of big data, especially shopper data, is incredibly powerful.
