What is Retail Analytics?

Retail analytics is the analysis of data generated by retail operations with a goal of making business decisions that drive—or hinder—profitability. The use of retail analytics developed as a response to the retail transformation being driven by unprecedented changes in consumer behavior, intensified pressure on margins, the changing role of stores, and intensified competition for both on and offline channels.
To survive and thrive in this challenging environment, retailers must improve efficiency and automation across multichannel processes. They must stay ahead of customer demands, and optimize customer journeys to create a frictionless, seamless online and offline customer experience. Many retailers are turning to retail analytics--a broad set of powerful analytical tools such as pricing and assortment optimization, location analytics, and customer-driven marketing--to enhance their competitive advantage and drive sales and profits.
Retailers must capture vast amounts of data (customer, store, financials, product, inventory, employees, and call center) and be able to deliver an integrated view of the business, trends, and insights into customer behavior—across the enterprise. That transformation process begins by implementing an advanced data and analytics environment—consisting of an integrated data warehouse (IDW).
Retail analytics helps identify new opportunities, create personalized marketing and communications programs, improve revenue streams with profitable customers, and provide the right products and services that meet and exceed consumer expectations. By seeing how all aspects of a business relate to one another, retailers can discover answers to critical questions such as:
Which products are my best customers buying and from which channels?
Am I selling those products at the right price, at the right place in the store, and in the right color, size, and quantity combinations?
Which products drive the highest baskets in terms of sales volume, revenue, or profitability?
What is vendor performance relative to other vendors in category in terms of sales, profitability, and service level?
What is the in-stock percent of my top-selling products?
What would be the optimal price for this item on promotion?
What promotions should we offer to each customer segment, when, for how long, and in which channels?
What is a product’s viability and substitution value based on customer browsing versus purchase behavior?
Is my labor being planned and scheduled efficiently to maximize customer service and sales, while minimizing labor costs?
How can I improve customer service and the products that I offer based on customer feedback?
What is the preferred interaction channel for a given type of customer for different shopping interactions and item categories?
What is the company’s total associate benefits liability, and how has it increased or decreased from the prior year by plan and by associate demographics?
Key retail business process areas that cab be optimized using retail analytics include:
Assortment/Category Management/Product Mix (PMIX)
Item Pricing and Cost Detail
Inventory Management
RFID/Serialized Item Track and Trace
Shipment, Freight Billing, and Claims
Transportation Logistics (Distribution and Logistics)
Agreements (Terms and Conditions)
Promotion Management and Marketing
Point-of-Sale Transactions
Detail and Fulfillment
Catalog Sales and
Content Management
Recall Management
Customer Value, Shopping, and Product Purchase Behavior
Quality Feedback
Loyalty and Gift Card
Usage Behavior
Store Labor and Operations
Human Capital Management (Human Resources)
Privacy and Vendor Management
Call Center Productivity
Omnichannel Commerce and Interactions
Forecasting and Scoring
Financial Management
Retail Pharmacy
Table Dining Servicing
Kitchen and Wait
Time Management
Service Tips
Reporting Compliancy
Sales Tax and Fee Compliance
Increasingly, data and analytics are the lifeblood of retail organizations, necessities for both survival and success. The challenge for retailers is to cost-effectively capture and store exploding data volumes and types, quickly and reliably analyze the data, and then operationalize insights across every channel where retailers touch a customer or supplier.