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Retail POS Data Analytics Getting Easier

Posted in Trends & Technologies and tagged , , , , , , , , .

Photo: Decisions Decisions; Andrew Stawarz, CC license

Companies making products sold at retail stores long ago realized they had minimal visibility of how their products are displayed at the retail shelf and how well their products ‘turned’ and produced revenue for them and the retailer. But today they can have visibility, and investments in software, services, analytics, or both are likely to produce good returns, if done correctly.

Where to get solutions? Retailers themselves will sometimes provide the data, but you may not have the infrastructure and tools to make sense of it in its raw form.  Third party data providers such as Nielsen and RSI, Orchestro, and Relational Solutions all provide the data in a much more usable form, accompanied by a whole range of reports and metrics.

The tools (software) and the POS and customer warehouse data are not expensive; you don’t need to build a colossal on-premise database and create a huge infrastructure to maintain it. Plenty of vendors have tools on a try-and-buy basis, and the data can be obtained on an a la carte basis (one retailer only, for example) from several different firms. Experimentation should be encouraged; there is no one right way to do this type of analytics.

The challenge, however, is what to do with the data and the findings gained, and how to engage your retail partners for the best collaboration? Start with your own products and determine metrics against which you want to measure retail performance, such as in-stock rates, % distribution in the store network, velocity, average days of supply in the warehouse, and any irregular peaks or valleys in inventory. You can also measure promotion and merchandising effectiveness.  These findings are the beginnings of the conversations you start to have with your retail customers.

From an IT standpoint, you need three things: 1) if you want the data on-premise, inside your firewall, you’ll need storage and ETL (Extract, Transform, Load) tools to load retailer and other transactional data; 2) a translation and user interface tool to build your analytics (many to choose from – see Tableau, Information Builders, Tibco, Microstrategy); and 3) some way to “govern” the data, which is how each data element will be defined and how it will be used in different analytical formats.

Keep in mind that setting up POS analytics is a means, not an end.  And that’s why experimentation is good — you want to find the best combination of data and analysis that yields the best results for you and your enterprise.  In this case experimenting is not expensive, so do lots of it.  What gets measured usually gets managed, and that is where the true opportunity is.

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