An important application of big data is on customer relationship management. You would think that companies are already utilizing the data they have, but I see a lot of companies paralyzed with the amount of data they possess because they don’t know what to do with it. When you think about a company’s set of customers, you will find customers with a variety of behaviors. Some buy more at every visit, some buy less but have more frequent visits, some buy only once in a while, while still some others buy once and have yet to return for a second purchase.
For example, I do grocery shopping every week at Whole Foods. I spend anywhere from $20-30 and buy enough to last me a week. There are weeks where I’m left with more food than usual, usually because of a free lunch or dinner from a work or school event. When I have a lot of food left at home, I don’t do grocery shopping that week, which means that later in the week I might run out of food. I go back to the grocery store and reset back to my weekly purchase routine.
At home, my mother buys grocery for the family every 1.5-2 weeks. She buys in bulk and buys more things that can be kept for a longer time than a week (ie. meats, etc). We have different purchasing habits because of the nature of our situation: I live on my own in Chicago, while she is the primary shopper in the family. She buys at Whole Foods, Trader Joes, Safeway, Costco, and a variety of Asian markets.
If Whole Foods analyzes their data, they will find that I’m a loyal customer, albeit I don’t spend a lot. I should be given different marketing messages that will entice me to buy more at every visit, such as a coupon to try food items related to what I buy often– such as dried figs or cranberries. While for my mother, Whole Foods should give her incentives to go more often to the store and do her shopping there– these includes exclusive products that can’t be bought at the grocery stores (ie. La Brea Bakery bread or local apple pie).
Understanding the consumer behavior of your top customers is very important. They are your most avid ambassadors, dedicated defenders, and loyal legion. Aside from behavior, learning about their buying attitudes could provide you with insight into your business. Are people shopping at your store because they like shopping there (good, it means they are here to stay), or only because you have products on sale? (bad, it means they are ready to jump ship)
How else can Whole Foods use big data to understand their customers? Here are some thoughts:
– Analyze customer transactions and roll them up to unique customer IDs. Cut them by decile and understand which sections customers are buying the most (ie. produce, health and beauty, bakery, etc)
– Understand which channel customers are using the most (ie. website, social media, offline, etc) to get to the store. Are they using digital coupons or coupons from the weekly flier? Do they have a list in mind or do they just chance upon products on sale and pick them up?
– Determine how frequently people buy products. Which items do they buy more often? Which ones do they buy less? This could help advise manufacturers to adjust product packaging accordingly– pack foods smaller so people buy more, or pack them bigger so people buy less but consume more.