Retailers are getting ready for a new year amidst the looming shadows of an approaching recession and the recursion of Covid. Businesses that have already embraced a fully digital process along with robust data analytics and also mastered an omnichannel marketing strategy are set to gain a competitive edge in 2023. Insights mined from their rich data sources may be the much-needed ‘Brahmāstra’ for them to move ahead of the curve, replanning and repurposing their business with agility, optimizing their operations, improving customer experiences, and thus, effecting an increase in sales and profits.

Recessionary pressures have heightened the push for retailers to increase operational and marketing efficiency. Retail analytics present CXOs with several opportunities for improvement in both areas. Changing economic and social dynamics require continuous monitoring and understanding of shifts in customer spending, habits, and preferences. Analysis of the supply chain, inventory, and POS data provides retailers with a pre-emptive prospect of quick re-arrangement of merchandise, stores, and logistics to stay aligned with the changing market dynamics.  

Let’s look at areas that would be crucial for retailers to effectively manage to get the edge in the new year, and how data and analytics could be their leverage. 

Inventory Predictions, Planning, and Optimization: Retailers need real-time information and analysis of what is selling and what is not. Pre-emptive information can help maintain the flow of goods that customers want and delist stock items that are holding up cash flow. Agility in decision-making could free up resources like cash and space to stock items that have higher inventory turns. Managed poorly, customers may be turned away when they don’t find items of their choice while stores have a stale assortment of unsold SKUs.

Retailers are pushing the envelope by using advanced analytics tools to forecast demand-supply changes, identify stock-out situations and improve the efficiency of the supply chain. Retail analytics can significantly improve inventory management through inventory replenishments, location/store optimization, and lowering transportation costs.

A wide assortment of merchandise, a large number of sourcing channels, and logistics options pose another challenge with the volume and variety of data collected by retailers from all transaction points. A robust retail analytics platform that works with Big Data helps them spot trends and patterns early and predict future demand accurately to plan to the source.

Basket Analysis: Intelligence based on Market Basket Analysis (MBA) has emerged as a powerful tool for retailers to action revenue uplifts. MBA uses powerful algorithms to mine consumer affinity towards purchasing products together in bundles - like milk, bread, and butter - and increase cross-selling opportunities. Analytics have unearthed surprising combinations that are often bought together but may not be that obvious- like beer and diapers on weekends prior to a road trip. This analytical intelligence is used in several ways:

-    Recommendation Engines: Prompts like “People who bought this also bought…” “You may also like…” trigger basket additions along with upsell and cross-sell. This maximizes revenue by making powerful recommendations of products within or outside the category, based upon strong predictive analytics. 

-    Store Layouts: Shelving product categories that have a strong affinity result in basket value enhancement by providing visual buying cues. 

-    Product Combos: Creating product combos of items often bought together. 

-    Marketing Promos: Promotions on one item are likely to increase the sale of other affinity items. This can be a powerful intelligence for marketing as well as inventory, for example, promotions that run on cereals will also boost the sale of milk.

-    Cross-Category Planning: Basket analysis helps in planning and forecasting sales and demand across affinity categories.

-    Marketing Campaigns: Retailers can segment and target audiences created using similar purchase histories as they are likely to have similar requirements and preferences. For example, people who bought a high-end laptop may also be the right target audience for an e-mail campaign for a newly launched smartphone or a LED TV.

Mapping Customer Journeys: Analysis of customer journeys provides potent insights that can be utilized by retailers in several ways: 

-    Targeted Influencing: Automated delivery of marketing messages tailored to customer’s needs at all touchpoints within the journey. For example, product features and differentiations in the consideration stage and discounts or deals during the decision stage through an automated e-mail campaign, built at scale and targeted to individual customers.  

-    Remarketing: Identifying the high drop-off points within the journey and building campaigns to mitigate them. Remarketing campaigns can be run to bring back customers who abandoned carts before checkout. A high drop-off at the payment stage could be an alert that something is amiss –like the absence of a preferred payment option.

-    Video Analytics:  It is an effective tool to build heatmaps within physical stores to identify areas frequented by customers, paths, and wait times at checkouts during various times or days of the week. This can be used to re-plan store layouts and personnel requirements during lean and busy hours. 

Like cookies are used online, video analytics are also used to identify returning customers and new customers, creating a potent opportunity of targeting them using appropriate marketing campaigns and promotions.  

-    Web Analytics: Web analytics that runs on retailers’ online commerce websites provide compelling insights on consumer behavior, product choice, and demographics. They yield precise metrics around site views, product views, cart additions, conversions, search interests along with purchase history. This authoritative data can be used to build an affinity audience profile for each product category and for invoking focused and targeted marketing campaigns directed at them.  Analytics also helps in measuring campaign outcomes and optimizing marketing spending. 

Customer Churn: Analytics help retailers in identifying actions and behavior that predict the risk of a customer leaving or canceling. This is an important alert used to trigger marketing action aimed at retention. A commonly used metric that tracks churn is the Net Promoter Score, which is also a measure of customer satisfaction and positive reviews that dovetails directly into business growth and product sales. 

Retail Analytics is also useful for customer retention and a good predictor of repeat visits and purchases based on historical data. Marketers use this insight to roll out campaigns that bring back customers to stores or websites with timely offers and recommendations, or simply send out reminders like “It is time to reorder…”

Risk and Fraud Detection: Another major issue faced by retailers is a fraud, which causes both financial losses and a decline in consumer trust. Fraud can involve shrinkage, damaged items, inaccurate inventory, credit card data theft, fraudulent returns, and a number of other problems. It might come from clients, employees, or outside sources like service providers. Retailers should be watchful for spending that is not justified financially or not properly documented. Retail businesses may also suffer if service provider contracts are not closely watched.

Analytics can be used to detect and prevent fraud, as well as in risk management. Analytics can be used to identify sources of fraud by analyzing unusual patterns and correlating certain behaviors using a combination of statistical techniques. Data and analytics can then be used to detect anomalies and pinpoint the location of a problem. Here are a few examples:
●    Analytics can assist retailers and banks in detecting fraudulent activity before it becomes a major issue.
●    Unusual patterns of product and inventory movement can be used to identify shrink and shop associate theft.
●    Product types and categories can be profiled and stock take variances and adjustments in various stores can be investigated.

Retail analytics has now been revolutionized by the proliferation of big data, artificial intelligence, and machine learning. These technologies have made it possible for retailers to process and analyze vast amounts of data in real time, providing insights and recommendations instantaneously. Retail analytics empowers retail CXOs with the ability to track and measure the effectiveness of their various strategies and initiatives. For example, they use analytics to identify which marketing campaigns are most successful at driving sales or to optimize their inventory management to reduce waste and increase efficiency. Retail CXOs can thus make informed decisions about how to allocate resources and adjust their operations to achieve their desired outcomes.