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New Use Cases, Executive Buy-In, Non-POS Data Integration and More

11/29/2012
Members of the CGT Downstream Data Share Group met in Orlando, Fla., during the Consumer Goods Business and Technology Leadership Conference on October 23, 2012. In addition to continuing work on the use case matrix, the group tacked various topics including integration and use of non-POS data. 
 
Jennifer Beckett of Vendor Managed Technologies led the discussion. Here is a recap of the best practices shared by share group members:
 
·       In addition to POS data, many companies are hoping to leverage additional data, including internal transactional data, and integrated syndicated data.
·       Senior leadership buy-in is a must and visibility is key to achieving it; teams need to show value quickly; store level alerts (out of stocks) make it easy to quantify results. For some participants, gaining buy-in was unfortunately not as simple or easy as expected.
·       Sometimes, executives have a bias toward one type of data over another. Therefore, proven use cases are critical to gaining acceptance.
·       Successful use cases to garner support include: out of stocks, new product introduction, store assortment management, store alerts
·       Try different granularity – weekly versus daily, for example.
·       Balance an all-compassing DSR versus specific account level scenarios to gain buy-in
·       Identify high-performance stores, set thresholds. 
·       Don’t analyze the same thing every day; rather, select certain days to test and see if something systemic is happening and train people to identify anomalies. 
·       Get the basics down before getting too sophisticated. As you get more sophisticated, the need for a DSR expands and sales-only initiatives will start to involve the supply chain.
·       Identify internal versus external use cases; show how retailers will benefit from sharing data
 
Join us on December 11, 2012, when we’ll explore these topics further and encourage a lively debate about downstream data versus big data.

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