Grocers have long found the analysis of data helpful in better serving their customers, managing their logistics and remaining profitable.
While we often think of digital data collection as relatively modern, grocery store data is something that food retailers have been collecting for years. Historically, grocers have used transaction logs of register receipts to help inventory and ordering processes for managers. While lacking in the sophistication of credit cards, store shopper cards and buyers’ programs have also been used to get a sense of how certain households and families are shopping.
Today, however, data collection is far more robust. Everything we do online is in some way tracked, from our social media activity to our searches and reviews. A treasure trove of data for companies of all types, this information is an invaluable component of modern marketing. Grocers, like other businesses, can use this information to get a sense of which products are most popular at a given time and increase stock accordingly.
Big Data also assists grocers when they have to reduce stock for a period of time. For example, consider the case of a large-scale product recall. In the past, grocers would have to advertise the recall in as many places as possible, given the impossibility of reaching out to every customer who had purchased it directly. With Big Data, grocers can gain insight into a customer’s long-term buying preferences and identify precisely who is most likely to have the product in their household, even without credit card data showing an actual purchase. This allows grocers to target customers whom the recall would affect more effectively and efficiently.
But making the most of Big Data is not always smooth sailing. Particularly for smaller grocers, the immensity of the data collected can over complicate a traditionally low-tech business. Some grocers lack the resources and training to utilize Big Data at all, even going so far as to cancel loyalty programs and other data-collection sources.
It may be the case that the implementation of Big Data in grocery stores is waiting for a greater sense of its return on investment: Migrating toward data analysis is an expensive, time-consuming proposition for smaller organizations, causing some to hesitate until they are sure that this migration is worth it.
Despite some concern by smaller organizations, in general, grocers are utilizing Big Data to great effect. Some of the areas where we are beginning to see the effect of Big Data are:
- Targeting shoppers: Grocers can form a comprehensive picture of a shopper’s buying preferences, allowing targeted marketing and customized offers that this shopper may find appealing.
- Promotions: Grocery store data can show exactly which items are the most in-demand and which are not selling as well, helping them offload unpopular items with appealing promotions or coupons.
- Marketing campaign management: Data can provide predictive insight into projected customer buying preferences in the future, allowing tailored marketing campaigns and precise budget projections for advertising.
- Inventory: Grocery store data can be used to understand when some items are most likely to sell, so that managers can decide the best time (if any) to discard certain unsold products.
- Pricing: Over time, the information provided can show grocers the precise price point where selling a good is most effective, maximizing profit and reducing the chance of extra unsold inventory.