Advanced analytics techniques are now a must for all players in the grocery business. They help make shelf/inventory efficient and provide more effective consumer targeted offerings, which lead to better loyalty management
Big data, advanced analytics and machine learning have revolutionised the decision-making process, shifting the focus from traditional methodologies — that depended on just experience and subjective gut-feel — to objective data-driven decisions. In a world where omni-channel retailing is gaining momentum, traditional grocery stores face the challenge of co-existing profitability in the market. This challenge can be comprehended by understanding buyer behaviour and data-driven methods adopted by competitors.
The lines between various channels of retail vis-à-vis mobile, online and in-store continue to blur, providing the new age tech-savvy buyer with benefits that come not only in the form of price, but also having unlimited access to competitors’ products/prices. Before every purchase, the buyer can determine the channel and grocer providing the most economic offer and decide accordingly. Competitors are also incorporating advanced analytics in their customer life cycle for better customer acquisition, retention and loyalty.
In such a scenario, grocery chains that fail to adapt their in-store strategies would be at a disadvantage. Imagine the day when your personal refrigerator could predict a probable order and eventually place it in the geographically closest grocery store, equipped with an efficient order management process. With
the growth in customer intelligence for grocery chains, it is now mandatory to adopt advanced analytical methods in order to survive the competitive landscape.
Advanced analytics techniques provide numerous advantages to the grocery business. Some of these are effective product bundling, optimised shelf/inventory management, gaining a competitive edge, as well as efficient consumer targeted offerings, which leads to better loyalty management.
Types of analyses
There are various types of analyses that grocery brands can opt for. Market basket analysis uses a multitude of factors that affect transactions such as package size, product category, content and price. It also offers retailers an insight on products being purchased together, enabling product bundling and combo offers to boost sales and revenue. Supply chain and inventory optimisation is another option that gives retailers an insight into improving returns from their existing resources or investments. With increased accuracy in forecasting techniques, supply chain analytics helps grocers with improved route optimisation and increased vehicle and driver utilisation. On the other hand, with advanced inventory optimisation techniques, wastage of resources and time is reduced. This will then maximise performance on key metrics like return on investment, percentage utilisation and inventory turnover. The benefits of this include a minimisation of the overall cycle time and inventory days of supply.
Through advances in customer analytics, retailers can produce more relevant advertisements based on a deeper understanding of customer segments and purchase behaviour. For example, real-time face recognition leads to better targeted offerings for customers. Big data analytics also provides an edge to grocers using competition benchmarking. In fact, an understanding of competitor pricing and strategy leads to an improved service quality for the retailer.
Considering the fact that online giants, like Amazon and Google, have entered the grocery business, it is more than likely they will utilise advanced analytics methods to gauge their impact/strategy. Based on the frequency of purchases and buying behaviour, loyal customers can be identified across the customer base. This makes it easier for retailers to design relevant campaigns and segment specific loyalty programmes. It also leads to reduced costs and a better Net Promoter Score for stores.
A Bloomberg Businessweek report predicts that “By 2023 the share of online grocery chains could be as much as 11 percent.” This may be true but grocery chains must work on enhancing the in-store experience. With numerous channels at their disposal, it is only the experience which would drive a customer to visit a grocery store and make a purchase. Here, advanced analytics solutions will optimise that shelf space/inventory so that product bundles are in close vicinity to the consumer to trigger product purchase. The implementation of advanced analytics solutions, coupled with operations optimisation for omni-channel buyers, will help grocers achieve growth in this new competitive environment.
The writer is senior V P—analytics, Blueocean Market Intelligence