Major consumer-package-goods firms (CPG), like Kraft, Nestle, Coca-Cola and PepsiCo have always operated with a blind spot about customers. This has always made marketing challenging. Although their products were often ingested by consumers, they rarely if ever met customers. CPG firms rarely knew their identify of customers and even at times had a poor understanding of why customer preferred their product over other competitor products. For years, the stores that sold the products also had the same blind spot.
This blind stop in a critical realm of marketing gave rise to the likes of Nielsen. Sampling and surveying were the best tools that could bring measurement and identification to the consumer of CPG products. It was and remains imprecise. It makes use of averages and extrapolates observations to others in a zip code or neighborhood that are often incorrect. It is biased and contaminated by the willingness of participation in many structural ways. Still, it was helpful, because so little other data was available.
In the 1990s grocery stores launched loyalty cards and data mining the transactional data became enormously beneficial for learning about customer behaviors and insights. It spawned a new host of firms that could measure the uptake of a product and to some extend who bought it and what else they bought in the store.
A growing form of sales of CGP goods is through Amazon Pantry. Through Amazon Pantry, customers can order many food items and household goods, delivered straight to their homes. In doing so, Amazon captures customer behavior, preferences and can relate that to many other categories of purchases. The data benefits from a network effect. The data, by the way, is quite precise in that it involves shipping data and billing data that must be current. The role of Amazon in the fulfillment is beyond that of a logistics firms, in a powerful way, Amazon is a data broker. It can tell CPG firms more about the purchasing of products that others in retail have ever been able to do.
With this in mind, it is fascinating that Coca-Cola recently decided to re-launch Surge (a sweet and highly caffeinated soda product) exclusively through Amazon. Surge was always a niche product and its placement challenged Coca-Cola, even though loyal fans called for its return. Where did the customers live? Who were they? How much product should really be made? All questions that Coca-Cola distributors had struggled to answer when Surge was distributed conventionally at stores. These questions are now answered.
Coca-Cola’s launching of Surge via Amazon was a success. It was sold out in days! It also allowed Coca-Cola to know the customer demand function with a precision that was always lacking. This creation of Big Data about CPG products will change how products are introduced, produced, marketed, and the role of distributors and the value of data on our day-to-day purchases. Even more interesting are the “replenishment” models available on Amazon Pantry that schedule regular orders of products. It brings convenience and it is beneficial to Amazon, the customer, and now creates a data signal that helps the CPG firm finally know who enjoys their product on a regular basis.
These important implications of Big Data use in developing a linkage between buyer and product seller in the digital economy, and more, are developed in greater detail in my recent book, From Big Data to Big Profits: Success with Data and Analytics. The book examines the evolving nature of Big Data and how businesses can leverage it to create new monetization opportunities. Using case studies on Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leading-edge users of Big Data, the book also explores how digital platforms, including mobile apps and social networks, are changing customer interactions and expectations, as well as the way Big Data is created and managed by companies. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which assesses companies for Big Data readiness and provides direction on the steps necessary to get the most from Big Data.
Amazon, Analytics, Big Data, Big Data Analytics, Big Data to Big Profits, Coco Cola, CPG, Data Monetization, Data Products, Digital Platforms, Economic Sciences, Kraft, Nestle, Nielsen, PepsiCo, Surge