Why making sense of customer data is a win-win deal for any business


By Emilio Lapiello, Director, Advanced Analytics and Strategy

What does a butcher in Italy have in common with Walmart? The answer lies in customer data.

Image courtesy of Flickr
Image courtesy of Flickr

When I was young, I played soccer with my neighborhood friends in a typical Italian piazza surrounded by grocery stores.

My mother shopped there every day. If the butcher did not see me playing with my friends, he would ask my mom about me. “Where’s Emilio? I haven’t seen him today.”  My mom would report on the occasional cold or flu that kept me from running around chasing a soccer ball, and the butcher would respond with something like: “Here, this beef cut just got in today. Bring some to him. It will make him feel better.” My mother would buy the meat and cook it from me.

I am quite sure my mother did not buy that beef cut because she thought it would make me recover, but she did think I would feel better knowing that people were thinking about me and sent me something with their best wishes of a prompt recovery. The butcher’s message was relevant to her and to me, and added value to the neighborhood shopping experience.  There’s a lesson learned here that applies beyond the piazza to today’s modern world of strip malls, big box retailers, super-sized grocery stores, and the online e-commerce giants like Amazon and co.  Large companies should also make sure they know their customers just like the butcher in Italy did during my childhood.

That neighborhood butcher was unconsciously doing what large companies worldwide are very consciously trying to replicate: providing relevant messages to each customer.

Why relevant messages? Because relevance increases the value of a product. Products are bundles of benefits, and their actual value depends on the customer needs and wants they satisfy. These needs and wants are dynamic in nature and change with time. Being able to find the right message (it will make him feel better), at the right time (well, I was sick.), to the right person (my mother), through the right channel (at the point of sale, in this case) produces the highest possible value to a customer.

What’s in it for the business side? Sale transactions happen only when the value received is equal or greater than the value provided. When customers perceive the highest value, they are willing to pay the highest cost as well, which then turns into higher profits for the business side.

Now the big difference between the neighborhood grocer and Walmart is that large companies have millions of customers, so how can they possibly get to know each and every person with enough insight to tailor their marketing messages to individuals?  Luckily for them, and what they may not realize, is that they have spent the last 20 years collecting large amounts of data, and they can now focus on using it.

In today’s Big Data era, it is time to introduce Smart Data: data that has value in both its quantity and quality.

Companies today, big or small, cannot afford to be unaware of what is relevant to their customers, and thankfully insights into customer preferences, behavior, purchasing history, and much more are now accessible through the data mining and analytics tools and technologies available today. Actionable insights can be extracted and information about customers can be inferred to ensure value can be generated through relevance. Data mining and clever analytic methods are key to understanding what those customers value and what is relevant to them.

This “learn and serve” process should be the basis of the modern enterprise, and it is essentially data and analytics driven. It is also a dynamic process. People change their mind and their tastes, and without even realizing it, they start valuing products and services differently. What was valuable before will not be valuable anymore.

Companies are realizing their only way to keep up with these ever-changing customer preferences is to use customer data in a more fluid and always on way. The most advanced and successful companies are trying to predict those changes, as they realized they cannot react to their customers’ value perception dynamism. They predict customer behavior and maximize relevance using customer data. Most importantly, successful companies use customer data to their customers’ advantage, providing them with more relevant products, products their customers value the most.

So while Walmart may never be able to predict when I’ll come down with my next cold in order to recommend their best beef cuts, with the right data and analytics tools they can begin to see patterns in my purchase history and preferences that can be used to not only serve me relevant ads and promotions, but also predict how I’m likely to respond to new product offerings and store concepts.  In this way, the local butcher shop on the piazza and Walmart have something in common.