Sunday, September 13, 2009

Point of sales data - What other uses can consumer goods companies get out of it?

Sep 13, 2009

You buy a product at a store. The scanner collects the transaction. What happens to that data? How is it used to serve you better? Many of the large retailers are sharing their data with consumer goods companies. The usage of the data by the Consumer Goods (CG) companies varies a lot. Some of them use it as part of their daily or weekly process. Many don't know what to do with it. Frequently CG companies use it as part of monthly or quarterly processes. Using the data only for monthly or quarterly processes, diminishes the value that comes with the freshness of POS data. Here is my attempt at ranking the usage of POS data (most common usage first) assuming it is available for the channel at a day, store, SKU level. Where do you disagree with this ranking? What other uses would you add to this and where would you rank them?
  1. Sell More Distribution Points / Shelf Space: Dedicated retail account teams at CG companies are analyzing POS data (together with syndicated data) to identify potential new opportunities in terms of additional stores selling a product and getting more shelf space for a product at existing stores. In some retail channels including some in the top 10, syndicated data is not available, and in those cases the retailer provided POS data is the only option for CG companies.
  2. Trade Promotion Execution Perfection: CG companies can monitor trade promotion and new product cut-in execution through POS data monitoring. New product cut-in is simple - the product needs to scan in the few days before and after the introduction date. Display type promotions are slightly tougher if the promotions are for products that gets sold at the store even when there is no promotion; but through lift and pattern recognition algorithms, this is being monitored successfully.
  3. Sense and Correct Shelf Outs / Lost Sales / Phantom Inventory / Distribution Voids: Automatically monitor sales pattern at by store by SKU and check for anomalies. Some of it can be explained by competitor promotions, pre-promotion and after promotion lulls. But other instances, validated with cleaned up version of the store inventory shared by retailers, are instances that needs corrections. The corrections can be done through collaboration with the retailer or with personnel going into the stores.
  4. Downstream data to improve service level at lower inventory and transportation cost (DSD and warehouse replenishment): POS data and channel inventory can help improve accuracy of short term forecast. This is particularly important for heavily promoted items, new items, and seasonal items. Also this can be used to reduce the effect of "bull whip" due to ordering rules at the retailer. This increase in short term forecast helps improve "perfect order" performance of CG companies with much lower finished goods inventory. This also reduces the inefficient inventory re-deployments / cross shipments when the customer demand is at a different distribution center / geographic area from the forecasted demand.
  5. Sensing Retailer Price & Distribution Point Changes & Reacting to that: Retailers are more getting more aggressive in changing their price by store groups. Retailers are also increasing their usage of culling or increasing product distribution points between store sets / modular changes. Sometimes the manufacturers are the last one to know about these changes. POS gives the CG company the ability to sense significant changes in price and react to it - the reaction could be to negotiate with the retailers to change the price, adjust their expectation of their future demand based on the new price or a combination of the two.
  6. More Efficient Usage of Merchandiser Store Visit: Significant percentage of the large CG companies that rely on traditional store presence, have personnel visiting the stores on their behalf. These could be their own employees or could be third party merchandiser. The person visiting the store has no way to check each and every one of the items. POS data can help narrowing down the items to check and improve the accuracy of the tasks. This helps them be more efficient in fixing problems and spend their time selling and collecting data that could help the CG company.
  7. Reduction in Price Protection / Inventory Write-offs / Charge backs: Avoiding over-inventorying the stores through detailed analysis of POS and store inventory data and correcting anomalies early helps avoid having to pay the retailer when the inventory becomes obsolete or reduces in value.
  8. Production and Procurement Getting Early Visibility to Demand Spikes: Monitoring aggregate POS data and comparing it with channel inventory helps production get an early read on demand changes by product by channel whether it is increase or drop-off. That helps adjust production schedule and procurement, avoiding inventory build-ups.
I will continue editing this as the feedback comes in. Karthik

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