The world of supermarket retail is changing rapidly. Thanks to the increased digital competition, the retail economy is showing signs of fundamental change and increased activity. We are witnessing an increase in the number of new products being launched and customers appear to be more informed of their product choices than ever before.
As that happens, the expectations of the customers are also changing. Retail customers expect personalized services in this age. They look forward to brands reaching out to them across different channels and on their preferred touchpoints.
Many of these changing market dynamics can be tackled using data analytics tools, especially predictive analytics.
What is predictive analytics?
Predictive Analytics has been defined as a set of technologies and approaches are deployed to work with retail data to make future predictions and detect hidden patterns.
Predictive analytics helps retailers foresee customers’ purchase behavior, market trends, future activity, and provide answers to business problems. Supermarkets have begun using data at every stage of the retail process and this is driving their technology ecosystem choices too. For instance, Walmart’s data strategy includes building one of the world’s largest private cloud systems, with the capacity to manage 2.5 petabytes of data every hour.
Every time a customer shops from a supermarket store, uber-relevant data is collected. This data presents a massive opportunity for the application of careful data analytics.
So, how can data and predictive analytics take Supermarket retail to another level? Let us discuss this in detail.
1. Managing pricing and margins.
Pricing decisions are critically important for any retail store, as they are directly linked to consumer sales and profits. Even slightly suboptimal pricing can lead to retail losses. Pricing even marginally higher than desirable can reduce sales and drive customers to the competition.
Predictive analytics and Artificial Intelligence (AI) can process historical data based on price optimization and sales dynamics and establish quantitative relationships and recommend optimal pricing strategies for retailers.
When a retail store has thousands of products, it becomes time-consuming to use manual practices to set prices and determine the ‘best’ margins. With predictive analytics, 60% of retailers can increase their profit margins by 60%.
For instance, a multinational food and beverage company seeking to overhaul its revenue management and pricing capability benefited from predictive analytics and reported an incremental annual revenue and profit of 1.5% and 3%, respectively.
2. Helps in forecasting demand
As the adage goes, “Retail is detail at large scale.” Effective retail is all about meeting customer and market demands.
Typically, supermarkets have tens of millions of SKUs to sell every day. What is needed is an effective plan to ensure smooth operations and maintain profitability. An accurate demand forecast is necessary for retailers to get an accurate picture of which goods are needed for different store locations and channels on any given day. Accurate demand forecasts ensure the high availability of items for customers while maintaining minimal stock risk.
Global retail brands such as Amazon and Walmart use predictive analytics (powered by AI & machine learning) for building data-backed supply chain strategies. For example, Walmart ran demand-based predictions for more than 100,000 different products over 4,700 stores in the US. The company’s new GPU-based demand forecasting model achieved a 1.7% increase in forecast accuracy compared to the previous approach.
3. Gauging the effectiveness of marketing campaigns
Marketing campaigns are an important part of any supermarket retail business. Inefficiencies in marketing campaigns can lead to poor ROI due to budget inefficiencies and reduced conversions.
On the other hand, analytics can be used to inform decisions and actions for individual marketing campaigns that target a specific consumer base, with the right message delivered across the right channel at just the right time. This helps increase the relevance of the messaging and its impact, thus making marketing more efficient. AI and machine learning tools enable marketers to act on insights derived from real-time data.
Effectively, customer insights and behavior allow retail marketers to identify the audience segments that are closer to conversion and devote more attention to those consumers by giving them just the nudge they need.
4. Personalizing customer experience (CX)
In today’s digital age, retailers can gain a competitive advantage by knowing what customers want to do or buy next. Predictive analytics can help to boost personalized services and improve the customer experience (CX). This helps improve customer engagement, loyalty, and usage.
Consumers are using digital channels to interact with brands more than ever. McKinsey reported that 75% of U.S. consumers tried a new brand or way of shopping in 2020. This means more channels of communication are opening up every day and brands have an opportunity to increase engagement by talking specifically to individuals.
For instance, when an online store like Amazon recommends “products you might like”, that’s driven by powerful analytics. Personalized recommendations have helped Amazon generate up to 35% of its sales.
5. Maintaining real-time inventory
At the time of supply chain disruptions, supermarkets need to have the right stock of products at the right place at the right time. A poorly managed inventory leads to a direct loss in sales and revenues. Over-provisioning leads to wasted stock and losses. Under-provisioning leads to unfulfilled demand and lost customer loyalty.
52% of online shoppers abandon all the items in their shopping carts on learning that some items are not in stock. Further, only 17% of customers return to the same retailer after such an experience.
How does predictive analytics help in inventory management? Here are a few pointers:
- It enables quick decision-making and even automates the inventory management process.
- It answers questions like what items to stock (or discard) and the right time to do that.
- It eliminates the need to add (or remove) product stocks (on a hunch) or those that are not yielding sales revenues.
For instance, retail giant Amazon also uses predictive analytics in its logistics management, moving stock between depots in response to real-time demand predictions to guarantee rapid fulfillment of customers.
Conclusion
Predictive data analytics can be used by both brick-and-mortar and online retail stores to improve decision-making and overall customer experience. Retailers who want to thrive in this competitive environment can’t afford not to incorporate data analytics into their business decision-making process. As it happens, the opportunity for data-driven growth is now available to more retailers than ever before. Data analytics used to be the sole purview of the big retailers such as Amazon and Walmart. Now the emergence of rich and functional cloud options, specialist partners with technology and domain knowledge, and well-established use-cases is opening possibilities for small-to-medium retailers too. Groupsoft can help you revolutionize your retail processes with its data management and analytics services.
Reinvent the way you run your retail supermarkets with us. Contact us right away.
