Retail Analytics : Two Side Complexity

This is a continuation to my earlier post on Retail Analytics (Analytics in Retail, CPG and Distribution). I am trying to break the whole of Retail Operations to two parts and suggesting some analytics solutions.

i. Supply Chain Side:
This is the back-end operation. The typical decisions to be taken here are:
a. Location Decision : Where to locate various like the production units, warehouse, retail stores etc.
b. Production Decision : What to produce and how much to produce.
c. Inventory Decision : How much to store and in what phase, either raw or semi-finished or final product.
d. Transportation Decision : How to optimize the transportation cost while maintaining good logistics management.
e. Efficiency Optimization : This includes increasing overall efficiency of employees as well as processes involved.

Few of the Analyzes and Strategy Sciences Management works that I can remember, which will support these decisions are:
a. Network Design Methodology
b. Category Management
c. Inventory Management
d. Operations Research
e. Merchandise Performance Metrics
f. POP Indicator Analysis

I would prefer to call all these analyzes together as Supply Chain Analytics.

ii. Marketing Management Side
This is more operationally complex. To better this side understanding the customer behavior becomes critical. But this is not enough. There are various decisions to be taken as a Retail Chian store. Those decisions can be:
1. Target Customer
2. Brand Preference
3. Operational Efficiency Optimization
4. Cross-sell Focus Decisions
5. Advertisement Selection
6. Profit Maximization
7. Portfolio Maximization
Most of these topics are understandable from the terminology itself. So I don't get into much detail here.
I would rather move fast to suggest some analyzes and strategy consulting activities leveraging these decisions.
1. Brand Loyalty Metrics
2. Yield Management
3. Markdown Analysis
4. Customer Segmentation
5. Demand and Sales Forecasting
6. Competitor Analysis
7. Sensitivity Analysis (Price, Ads etc)
8. Migration Model (likelihood test to find people going to attrite or change behaviour significantly)
9 Market Basket Analysis
10. Marketing Mix Modeling
11. Advertisement and Campaign Management
12. Portfolio Maximization Analysis
13. POS Indicator Analysis

So many topics just thrown at you!!! Hope you enjoyed it. If not, let me know. I will try to address your concern in coming posts.

Analytics in Retail, CPG and Distribution

I keep on getting queries on use of Analytics in Retail Industry. Now I have tried to answer all those queries over here.

Problems and Issues in the Industry:
Let us first see what the common worries exist in the Industry. I have tried to make a list of issues that is constantly taking the head of Business Managers.

• Where to place the Production facility?
• What is the most appropriate stocking point?
• Which merchants should I buy from?
  
• What is the most suitable sourcing point?
• What is the best way to schedule my production?
• How to balance the work load?
• How to control the quality of the product ?
  
• What is the optimal level of Inventories to be held?
• What deployment strategy should be followed?(push vs pull)
  
• What is the best control policy ?
  
• What shipment size would be best (consolidated bulk vs lot for lot)?
• What route to be follow for the shipment?
  
• How shall I schedule my shipment ?
  

• Who are my target customers?
• Which brands should I focus on ?
  
• How shall I beat my competitor?
  
• Are the target customers cost sensitive ?
• How brand loyal are my customers?
  
• How effective are the campaigns and advertisement?
• What is the Point of Sale ?
  
• How to maximize my Portfolio?
• How to maximize the yield?
• How effective are the markdowns?
• Whom can I target to cross-sell the profitable items?
  
• How can I increase the efficiency of my sales force?
• What is the expected demand at a certain point of time?

Analytics Offerings:
After saying all this, I will divide the Analytics Offerings in various categories.

1. Supply Chain Analytics

• Vendor Efficiency Model
• Inventory Turns and stock levels
• Fill Rates and stock outs
• Storage Utilization
• Network Utilization and Zone Routing
• Inbound and outbound truck load Analysis
• SKU Velocity Analysis
  

2. Store Analytics

• Store Layout Analysis
• Sales and Margin Rates
• Shrink Analysis
• Return Rates and Fraud Analysis
• Credit Card Fault Detection
  

3. Campaign Analytics

• Campaign Effectiveness Analysis
• Channel Optimization
  
• RFM Analysis
• Mark Down Elasticity
  

4. Financial Analytics

• Financial Fore-casting and Budgetary Analysis
• ROC and Working Capital Management
  
• Shareholder Metrics

5. Merchandising Analytics

• Assortment Optimization
• Product Pricing
• Seasonal Trends
• Category Contribution
• Hot Items Listing
  

6. Customer Analytics

• Customer Life Time Value
  
• Profitability Analysis
• Customer Segmentation
• Market Basket Analysis
• Cross-sell Options
• Brand Switching and Loyalty Metrics
  

7. E-Business and Web Analytics

• Click Stream Analysis
• Web Sales and Traffic Rates
• Subscription Rates
• Store Cannibalization Analysis
• Web Traffic Segmentation and Target Marketing
• ROI and Web Spend Effectiveness Analysis
  

Result of Analytics Usage:
Analytics can help to get good results but only when there is proper implementation and co-ordination among all stake holders. And most importantly, Analytics comes at much later stage of Vertical Solutions.

I would recommend three steps in process improvement.
1. First get the process right. Automate the basic operations and implement best in class IT Solutions. ERP Software Implementation is best.
2. Create Good Reporting System (MIS). Know everything what is going on in your organization.
3. Analyze how things can be improved to become the best in the market.

Analytics helps in getting closer view of three stakeholders of Business.
1. Customers : Know you customers
2. Competitors : know your competitors and their moves
3. Capital : Know yourself, your strengths and capability.

I will be frank with you here. After doing all this, what you can get from Analytics is the last 10% value add. First 90% lies with the first two steps . ... ERP and MIS.