Actionable Analytics Practical Analytics for Practical People

Retail Analytics: Plenty of Opportunities

Traditionally, Retail industry has been characterized by intense competitive pressures and lower margins. This is probably due to a variety of factors - such as advent of new formats, blurring of category boundaries, growth of specialist retailers, and reducing consumer loyalty with better access to information. In this post, we will talk about what retailers need to do to attract and keep their best customers, reduce costs and improve profitability.

Typically, modern retail involves huge investments in IT infrastructure - point-of-sale, inventory, and supply chain management systems. Such transactional systems automate business processes, thereby improving the efficiency, productivity and agility of the business. In addition to these systems, retailers also invest in Business Intelligence capabilities - tools and process to extract, cleanse, transform and summarize the data into data-warehouses, and querying and reporting capabilities.

While these investments are critical to enabling their business processes, BI helps retailers "look back", rather than "look forward". For instance, using BI, a merchandizing manager can evaluate the impact of promotions or pricing, comparing it against what happened in the previous year or month, or against a forecast. As we will see soon, this is simply a superficial way of looking at promotions.

However, predictive analytics enables a retailer look forward, rather than simply look back, or look deeper, rather than just apparent

phenomena. To give an example of the latter, predictive analytics is used by leading retailers to unravel complex relationships in the promotions, so that they can understand the true impact of promotions after accounting for cannibalization, forward buying, switching behavior, competitor activity, brand pull, etc.), and make optimal decisions based on these insights, rather than simply relying on apparent lift.

Visionary retailers use predictive analytics to differentiate themselves in their chosen areas (distinctive capabilities) - whether it is in maintaining cost advantage, or in providing a rich shopper experience (depth & breadth of assortment, in-store service, etc.), or in attracting and retaining loyal customers (through loyalty programs and targeted offers). Some of the key processes where predictive analytics and optimization can help a retailer include:

• Consumer Insights - Understand your consumers and shoppers, their motivations, and behavior
• Assortment Optimization and Shelf Space Allocation - You probably know that 20% of your SKU's is driving 80% of your volumes (or a variant of this theme). However, how can you keep those SKU's that contribute to your value or price image for a given segment
• Promotions Optimization - Maximize the effectiveness of your promotions
• Demand Planning - Predict your demand and keep only what would sell
• Store localization - Customize your store assortment to local tastes and preferences
• Pricing Strategies - Extract the maximum price that  customers are willing to pay