4 Areas Where Grocery Stores Can Use Data Analytics to Enhance Operations

In this special guest feature, Bagrat Safaryan, Co-Founder and CEO of Local Express, takes a close look at four areas where data analytics can have a significant impact for grocers. Local Express is a SaaS vertical for the Food and Beverage (F&B) industry which specializes in providing eCommerce solutions to independent retailers and enterprises seeking digital transformation. Since starting the company in 2017, Bagrat has been on a mission to bring turnkey e-commerce solutions to grocery stores and food retailers across the country using the Local Express platform

In recent years, an immense digitization trend has taken hold in the grocery industry. Smaller supermarkets and grocery stores looking to get a piece of the online-market pie have often resorted to pre-made, third-party baking mixes, reducing their overall share of the profit-pastry. The result: 59% of grocers agree their third-party delivery partnerships are unprofitable. 

The constricted profitability grocers face when using third-party vendors is due to the loss of their data, which impacts their inventory management, marketing, and customer service.

But thanks to new, easy-to-integrate technologies, if you are a grocer, you can start baking the cake yourself and claim most of it as revenue. The key here is to use the technologies and collect own data, analyze systematic relationships and patterns, and tailor your store’s operations to your and your customers’ needs.

Let’s take a closer look at four areas where data analytics can have a significant impact for grocers.

1. Inventory management

The heart of any retail business is its inventory. A well-managed inventory ensures that consumers can access the products they want at any time. However, what sounds simple at first may be more complex in reality. 

In 2021, two-thirds of in-store shoppers and 51% of online customers experienced out-of-stock products, which resulted in more than $3 billion loss for supermarkets across the US. If, your store stocks too many products, you have to bear the costs of spoilage and overstocked shelves; if you order too few, you risk empty racks and disgruntled customers. 

What makes inventory management so daunting is rapidly fluctuating demand, seasonality, and delays in reordering. The only way to address these issues is using data to gain insight on how to prevent each. With the help of an integrated point-of-sale (POS) system, stores can get and explore the following metrics: 

• Sales velocity of each item category 
• Excessive inventory
• Average spoilage time for each item category
• Seasonality influencing customer demands
• Special event demand

Next, grocers need to drive analytics that help them predict future demand and intelligently manage replenishment. The idea is to reduce costs caused by items taking up unnecessary shelf space or going to waste regularly.

When you are in full control of your inventory, you can better plan for replenishment and send targeted marketing messages to reduce large stockouts or show alternatives if an item is out of stock. For example, if your data tells you that your last order of blueberries will go moldy in the next few days, you can offer a 50% discount and try to sell them all before that happens.

2. Online shopping

The market share of digital grocery shopping is growing: 70% of U.S. shoppers will buy their groceries online by the end of 2022. Today, most stores either already have a hybrid store model in place – meaning they have a physical store and an online catalog – or are flirting with the idea of introducing one. Nevertheless, 86% of grocers indicate they are dissatisfied with their online profitability. While digital ordering will enable even greater data, the digital grocery business needs to have a more positive effect on profitability.

For starters, grocers need to collect sufficient data on the number and volume of digital orders. By analyzing the cost of delivery or curbside pickup (hiring delivery staff), you can determine the most profitable way to organize digital orders (e.g., subscription models). You could test whether you can increase product prices while promoting lower fees and better service through your own e-commerce platforms – without risking lower order volumes or lost sales.

Data can also improve the customer experience by making digital ordering more frictionless, and therefore, faster. Data-driven systems analyze store routes and create a pickup list optimized for efficiency that helps pickers to pick up multiple orders in one go, which also promotes the full utilization of your workforce.

3. Marketing

Personalized marketing is critical for attracting and retaining customers over the long term. Across the whole e-commerce industry, about 99% of marketers say personalization helps advance customer relationships. Imagine browsing an online catalog for your weekend meals, and all the foods you were about to pack are already on a list in the app. But it can also take the form of comparing a shopping basket to that of others, suggesting the most popular items. That’s what customer convenience looks like nowadays – and it’s all empowered by analytics.

To personalize marketing messages, grocers need to collect information on customers’ previous purchase patterns, their cart abandonment, and the geo-location. But before this data can be analyzed, it must first be obtained legally. Therefore, supermarkets can resort to an age-old business tool: Reforming traditional loyalty cards and introducing loyalty programs that offer customers benefits every time they shop online or in-store. Customers who receive a discount on their most favored products are more likely to log in frequently – and feed your systems with tons of valuable data.

4. Rentability

Traditionally, the supermarket industry is a low-margin business, which means that striking a balance between the cost of products and the price at which customers buy them is critical. Here, data analytics taking sales numbers and customer feedback into account help determine at what price customer demand is the highest. For example, grocery stores can test different pricing strategies and analyze the effect on sales data, allowing them to identify products where a price raise won’t necessarily affect buyers’ decisions (e.g. luxury food, fresh produce).

The most exciting aspect of pricing is that many customers are willing to pay higher prices for their groceries – but only if they get a better shopping experience in return. This means that the first priority of stores must be to improve the customer experience. Stores need to understand that cutting corners will hurt their bottom line in the current market situation. Instead, they need to invest in intelligent management systems and big data analytics to offer customers the best possible shopping experience – both online and offline.

Sign up for the free insideAI News newsletter.

Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1