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Top 5 Ecommerce & Retail Analytics Use Cases

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Rishabh Rai

The retail landscape is undergoing significant changes that are bringing challenges like complex customer behavior and increased competition. Retailers must leverage e-commerce and retail analytics to cut through noise and offer personalized customer experiences. Analytics use cases like sales and demand forecasting, trend identification for pricing and promotions, supply chain and inventory management, new product planning, and advanced techniques can help retailers address challenges and better understand customers.

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Top 5 Ecommerce & Retail Analytics Use Cases
The retail landscape is undergoing a change unmatched in recent past. This change has brought about a number of challenges for retailers ranging from complex customer behaviour and stiff competition to newer retail channels like e- commerce. Retailers are required to cut through all the noise and offer their customers a unique & personalized experience.
Let us look at some e-commerce & retail analytics use cases and why retailers must leverage them. 1. Sales-Profitability & Demand Forecasting: The BI tools offer the following use cases pertaining to Sales:  Sales Analysis’ draws a complete picture of each transaction starting from mode of purchase till refund status.
 Sales Personnel Performance & Profitability Analysis gives insights on how each salesperson is performing.  ‘Demand Forecasting Modules’ can model scenario analysis to improve the sales goal setting by incorporating market data  Market Basket Analysis uncovers correlation between items frequently bought together & drives subsequent bundling opportunity.
2. Trend identification to drive the Pricing & Promotion Plan: Identifying customer trends is very crucial for retailers. It requires analysis of a huge set of data, having a robust retail data analytics platform boosts the efficiency in planning and rolling out these promotional activities.
‘What-if analysis for costs’ & ‘Analysis of purchase decisions. Price Elasticity Analysis User Behaviour Analysis Promotion Channel Analysis 0 1 0 2 0 3 0 4 Trend identification to drive the Pricing & Promotion Plan
3. Supply Chain Analytics & Inventory Management: 1. With retail analytics systems, retailers/ e- decision-makers can create a repeatable framework Vendor Evaluation. 2. The housed Business Intelligence (BI) systems can gauge the Supplier effectiveness vis-a-vis the Key metrics identified by the retailer.3. Inventory Optimisation with statistical analysis of SKUs data across outlets. 4. Combining Procurement analytics with supplier effectiveness for optimum inventory sourcing decision.
4. Insights driving a New product release plan:The supply chain is ‘pulled’ by the customers demand rather than the traditional ‘push’ strategy driving the trends. Retailers can use ‘Sentiment Analysis’ to capture this demand and accordingly plan the product pipeline in collaboration with their suppliers. Measure & Counter cannibalisation, if any, through ‘Scenario Analysis’ With Competitors’ Analysis, retailers monitor the impact of the competitors’ pricing & promotion events.
5. Some Advanced Retail & E-commerce Analytics Use Cases: 02 03 01 04 Heatmaps: The decision-makers could gauge the behaviour of web/app visitors and A/B test what is working for them. Recommendation Engine: It employs Machine Learning to understand the user behaviour over time and displays ads and promotions to a buyer pertinent to his past explorations. Fraud Detection: With Deep neural networks, they not only identify & flag the fraudulent behaviour but also in predicts them in advance. Customer Lifetime Value Analytics: It identifies the key determinants of a customer churn or a converting someone as a loyalist which helps in benchmarking an ideal customer
THANK YOU! Visit: www.polestarllp.com Email: rishabh.rai@polestarllp.com

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Top 5 Ecommerce & Retail Analytics Use Cases

  • 2. The retail landscape is undergoing a change unmatched in recent past. This change has brought about a number of challenges for retailers ranging from complex customer behaviour and stiff competition to newer retail channels like e- commerce. Retailers are required to cut through all the noise and offer their customers a unique & personalized experience.
  • 3. Let us look at some e-commerce & retail analytics use cases and why retailers must leverage them. 1. Sales-Profitability & Demand Forecasting: The BI tools offer the following use cases pertaining to Sales:  Sales Analysis’ draws a complete picture of each transaction starting from mode of purchase till refund status.
  • 4.  Sales Personnel Performance & Profitability Analysis gives insights on how each salesperson is performing.  ‘Demand Forecasting Modules’ can model scenario analysis to improve the sales goal setting by incorporating market data  Market Basket Analysis uncovers correlation between items frequently bought together & drives subsequent bundling opportunity.
  • 5. 2. Trend identification to drive the Pricing & Promotion Plan: Identifying customer trends is very crucial for retailers. It requires analysis of a huge set of data, having a robust retail data analytics platform boosts the efficiency in planning and rolling out these promotional activities.
  • 6. ‘What-if analysis for costs’ & ‘Analysis of purchase decisions. Price Elasticity Analysis User Behaviour Analysis Promotion Channel Analysis 0 1 0 2 0 3 0 4 Trend identification to drive the Pricing & Promotion Plan
  • 7. 3. Supply Chain Analytics & Inventory Management: 1. With retail analytics systems, retailers/ e- decision-makers can create a repeatable framework Vendor Evaluation. 2. The housed Business Intelligence (BI) systems can gauge the Supplier effectiveness vis-a-vis the Key metrics identified by the retailer.3. Inventory Optimisation with statistical analysis of SKUs data across outlets. 4. Combining Procurement analytics with supplier effectiveness for optimum inventory sourcing decision.
  • 8. 4. Insights driving a New product release plan:The supply chain is ‘pulled’ by the customers demand rather than the traditional ‘push’ strategy driving the trends. Retailers can use ‘Sentiment Analysis’ to capture this demand and accordingly plan the product pipeline in collaboration with their suppliers. Measure & Counter cannibalisation, if any, through ‘Scenario Analysis’ With Competitors’ Analysis, retailers monitor the impact of the competitors’ pricing & promotion events.
  • 9. 5. Some Advanced Retail & E-commerce Analytics Use Cases: 02 03 01 04 Heatmaps: The decision-makers could gauge the behaviour of web/app visitors and A/B test what is working for them. Recommendation Engine: It employs Machine Learning to understand the user behaviour over time and displays ads and promotions to a buyer pertinent to his past explorations. Fraud Detection: With Deep neural networks, they not only identify & flag the fraudulent behaviour but also in predicts them in advance. Customer Lifetime Value Analytics: It identifies the key determinants of a customer churn or a converting someone as a loyalist which helps in benchmarking an ideal customer
  • 10. THANK YOU! Visit: www.polestarllp.com Email: rishabh.rai@polestarllp.com