Retailers are ahead of some of the other industries in leveraging Data Science and Insights for increasing customer loyalty (revisit of the customers), maximizing basket size for a visit, optimizing marketing effectiveness and improving cost effectiveness of store operations & inventory management.
DnI Consulting provides broad range of Decision Science & Insights services for retailers in improving customer experience, reducing cost and increasing profitability. We have extensive industry experience and experience of work on broad range of business problems, analytics tools and technologies and statistical & machine leaning techniques.
Decision Science & Insights Offering for Retailers:-
|Vendor & Sourcing
||Planning & Inventory
- Vendor invoice & pricing analytics
- Vendor Lead time analytics
- Supply chain analytics
- Route Optimization
- Demand Forecasting & Planning
- Assortment Planning
- Category Management
- Mark Down Optimization
- Market Basket Analysis
- Market Mix Optimization
- Marketing Effectiveness
- Respond Modeling
- Store Promotion Analytics
- Attribution Modeling
- Customer Segmentation & Need Identification
- Loyalty Analytics
- CLTV Modeling & Strategy
- Customer Retention Modeling & Insights
Decision Science & Insights Approach for Retailers:-
|Define KPIs and design visualization dashboards for the business managers.
- “What” is current state of the business process?
- Define right measures to track performance of the business
- Deliver reports and dashboard for the business managers
|Analyze business performance and provide insights for effective decisions.
- “Why” performance is not align with the expectations?
- Leveraging right analytical frameworks and techniques in answering “key” business questions.
- Provide insights
|Leverage advanced analytics for growing business & revenue cost effectively.
- How” performance can be improved?
- Employ advanced statistical and machine learning techniques to identify patterns and forecast future trends.
- Deliver models and work with technology & business to get it.
Decision Science Examples:-
||Product Run Rate
||Marketing Campaign Response
- For a US luxury retailer, looked at the combination of products bought together.
- Provide insights to drive up the product sales leveraging product affinities
- Understanding product run rate (sales rate) is critical in managing inventory cost but reduce stock out.
- Defining category level expected run rate and identified run rate index for each of the SKUs.
- Forecast is critical in proper planning, vendor management and operation cost management
- Leveraged advanced analytics techniques for effective forecasting at a category level
- Contacting right customers for a product promotion is required in retail industry for improving marketing effectiveness.
- Built a response model using logistic regression techniques in identifying customers who are more likely to respond.