About the Customer
Industry: Retail & FMCG
The client is a leading FMCG brand serving 6 billion people globally. The company markets its products in over 200 countries and territories worldwide and It is a $15 billion global company serving.
Key Challenges
Objective to analyze the items purchased by the customer and establish an associative relationship between the item purchased. It is used to profile a subset of transactions, identify patterns and to arrive at some business rules. The associative rules can be used to increase customer loyalty, increase sales and increase profits. Analyze the POS data and recommended changes to the usefulness of data for the following:
- Cross-selling – Up-selling of new products to the right set of customers
- Improve sales by identifying sets of product sold together and reorganize the store layout to keep such products close to each other
- Improve the efficiency of promotions by promoting one set of products and have increases sales for the non promoted products
Our Solution
Diagnosing the challenges of the client, SPEC INDIA designed and developed a Business Intelligence and Analytics solution based on Tableau and R Studio, to understand the customer purchase behavior.
After analyzing the historical data, we underwent the following activities, as a part of the solution:
- Explored the customer POS data of multiple retail stores
- Build a dimensional model and consolidate the 5 years of historical data
- Detailed categorization of sales transactions based on relevant data
- Develop ETL Jobs for seamless integration and applied data cleansing
- Build a Model and applied on training data sets
- Aligned observations with business intuitions
- Re-Build a Market Basket for a group based on age, gender, particular family type, time-specific
- Predictive analytics dashboard representing data in a visual and interactive manner to identify customer buying patterns
- Affinity analysis of sales pattern for product categories, subcategories, and items
- Cutting edge machine learning algorithm to optimize marketing strategy in real-time
Tools and Technologies
Business Benefits
- The data-driven decision helped the business to add the recommendation to a customer during purchase for products
- The promotion offered on non-pick days helped to improve the operational efficiency
- Build a sales strategy based on customer type for up-selling and cross-selling
- Optimize the layout to put the frequently purchased items together
- Targeted and optimized marketing campaigns and messages
- Well-defined and augmented promotional events
- Recommendation engines
- New product placements
- Effective cross-selling
- Optimized inventory layout and maintenance
- Store planograms, segmentations