Challenges we address
Capturing the reluctantly spent dollars of today’s post-recession consumer requires retailers to be engaging, personal and relevant. As a result, analytics in retail industry is of paramount importance, to help retailers effectively collect, analyze and act on both customer and organization data in near real time across all the channels they function in.
While retailers view retail analytics as an effective tool to increase customer wallet share, gain higher margins, increase complimentary store sales and reduce wasted marketing dollars, many still struggle to prioritize their analytical approaches. While some are overwhelmed by the large number of options, others find it difficult to digest all of the data provided by various point-of-sale systems, websites and internal transaction processes.
Here are questions that retailers need to find answers for:
- How do I target precisely and customize my offerings?
- How do I justify my marketing spends?
- How do I optimally price throughout the product lifecycle?
- How do I decide on my assortment composition so that I can minimize lost sales?
HCL’s Retail Analytics Services
With over 10 years of extensive retail experience, and a client list that accounts for the who’s who in the retail industry, HCL has built a robust practice for analytics in retail that can customize analytics for retailers, enabling them to do business profitably. Our marketing mix modelling, propensity to buy modelling, pre pack optimization, and market basket analysis are just some of the many analytics solutions that have helped us provide the right information, at the right time, to the right decision makers, using the right technology.
What you can expect
Marketing Mix Modeling: The task of measuring returns on the marketing mix has become more complex as media has proliferated. HCL leverages marketing mix modeling to unearth the driving forces in the marketing environment for the allocation of promotion dollars to more hard working marketing buckets, thereby helping retailers become more profitable.
Propensity to Buy Modelling: Retailers today understand that identifying customers with the highest propensity to buy new products and services is imperative for accurate and better customer segmentation. Propensity to buy modeling helps produce a predictive score for each customer or prospect, thereby allowing retailers to target the most likely prospects of a marketing campaign.
Test and Learn for Stores and Customers: HCL’s extensive retail analytics capabilities empower retailers to test new ideas on a sample set of stores or utilize a test laboratory facility for various tests to be carried out.
Pre Pack Optimization: This is a tool which takes a system-wide perspective in identifying and reducing cost, thereby enhancing supply chain profitability. The objective here is to enhance and standardize processes to determine the optimal number of assortment packs, size ratios, and target stores for distribution. A method which we have evolved is to create a lost sales model and incorporate it into the current processes.
Market Basket Analysis: With the amount of data at the disposal of retailers, market basket analysis helps effectively utilize this data to uncover underlying patterns in customer transactions. We take the regular market basket analysis to the next level by identifying the most profitable baskets, differentiating between the natural rules that are inherent to the stores and patterns induced by promotion, and normalize the effect of store attributes (like size, footfalls etc.) to sales and margins, and compare stores. (HCL is currently researching a scientific process to define a general optimized solution to promotion strategy with the help of market basket analysis).