What You Can Expect from the ‘Big Data Supermarkets’ Article
- Uses of Big Data in the Food and Beverage Industry
Highlights of the ‘Big Data Supermarkets’ Article
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Running a grocery store can be challenging. There are numerous products to manage, many of which spoil quickly, often resulting in a significant amount of waste. With the profit margin for grocery retailers typically ranging between 1% and 2%, careful planning and strong marketing are essential. There are many ways big data can help facilitate this, and strategic use of food and beverage analytics solutions can make a significant difference to profitability.
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Uses of Big Data in the Food and Beverage Industry
1. Inventory Analysis
Big data analytics can provide insights into much more than just inventory levels and the popularity of different products. It can identify the most profitable products (which aren’t necessarily the same as the most expensive or the most popular ones), allowing you to focus your marketing efforts on those, and to reduce the stock of less profitable items or drop them entirely.
Analytics can also determine how quickly promoted products leave the shelf and predict when they will need to be restocked, resulting in fewer empty shelves and dissatisfied customers. This analysis can even be done while a sale is currently in progress: by analyzing the first several hours of sales, it is possible to get a stronger idea of how those items are moving, allowing you to more accurately predict necessary stock levels.
Accurately predicting inventory levels is particularly important when dealing with perishable goods. While there are some charities that will take supermarkets’ unsellable inventory, stores still wind up with a massive amount of waste. Globally, 1.3 billion tonnes of food is wasted annually, and grocery stores are in a position to reduce this number. By using big data analytics to closely monitor inventory levels, it is possible to significantly cut down on overstock without constantly ending up with bare shelves.
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2. Customer Loyalty
Loyalty programs are a good way both to collect data and to use it effectively. They can provide substantial insight into customer preferences and buying behavior, both on an aggregate and individual level. This data can then be used to provide more targeted marketing and promotions to each customer, making it more effective.
Identifying which products a customer buys is not the only way to use customer data and it indeed is better to not use this information in isolation. If a customer buys a long-lasting product like peanut butter or bathroom cleaner, for example, sending them a promotion for that item next week will be useless (and frustrating to the consumer, who will wish they had this deal last week). Analytics makes it possible to determine how frequently a customer buys a particular product, and then offer them a deal for it around the time they will be wanting to purchase it again.
Loyalty program data can also be used to identify when a customer turns to a competitor to buy a specific product. If a person (or, more tellingly, multiple people) used to buy meat every week and suddenly stops, that’s a signal that prices may be too high or quality too low. The store can begin offering the customer coupons for meat or emailing them when meat is on sale in an effort to lure them back. It can also take this behavior as a signal to look into its products and prices and determine if something needs to be changed.
Inventory and marketing are just two major areas where big data can provide a variety of benefits. Many organizations collect large amounts of data but never make proper use of it. By approaching food and beverage analytics with specific goals in mind, it is possible to gain many useful insights and find many ways to make your business more profitable.
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