WARNING: Bad business analytics may be hazardous to your wealth !

You paid handsomely for the software, perhaps for consulting too and have had some bright sparks working on it for months: the results of your analytics project are in and the answer is ... useless without some understanding of how good the models are it's built on.  If the analyst cannot give you detail on how 'good" the model is for its purpose, all results should come with a wealth warning. 

BAD BUSINESS ANALYTICS MAY BE HAZARDOUS TO YOUR WEALTH.

Point of Sale Data – Supply Chain Analytics

I’ve spent a large part of my career working in Analytics for Supply Chain.  It’s an area blessed with a lot of data and I’ve been able to use predictive analytics and optimization very successfully to drive cost out of the system.  Much of what I learned in managing CPG supply chains translates directly to Retailer supply chains it’s just that there is much more data to deal with.  

Point of Sale Data – Category Analytics

If you haven’t already read the previous entries in this series, you may want to go back and check out [Point of Sale Data – the basics] to see why you really need a DSR to handle this data, and  [Point of Sale Data – Sales Analytics]  for some thoughts on analyzing sales drivers that are equally relevant to Category Management,

 As Category Manager you’re working with the retailer to help drive sales for the entire category.  You hopefully have access to the full data for your category (which could be substantially more than your account manager colleagues).  Let’s see how predictive analytics and modeling could help address some of your challenges:  How well are current planograms performing?  What is the best product assortment for each store?  How can you best balance customization of assortment by store with the work required to create that detail?

Point of Sale Data – Sales Analytics

I’m assuming that you now have a DSR (see [Point Of Sale Data - Basic Analytics] ) so you can manipulate the large quantities of data necessary to do this work, you have your routine reports automated and use the DSR for ad-hoc queries against the POS data. 

The DSR provides a great foundation for analytic work: use it to integrate multiple data sources, clean the data, handle very large data volumes as though it was all sat on your desktop and it will help you build reports that summarize that history with ease. Typically, the DSR does not provide much help for you with predictive-analytics. 

Let’s look at an example related to what really drives sales.   Do you know?  Can you quantify it?  Knowing these answers with quantified detail can help you better explain your sales history and plan for the future.  Better promotions, better pricing, supply chains that anticipate peaks in demand and make sure the product is on the shelf when it’s needed.  Here are some of the things that could drive your sales:

Point of Sale Data – Basic Analytics

You've got access to Point of Sale Data…now, what are you going to do with it?

For the purpose of this blog entry, I’m assuming that we have daily aggregated data by product and by store.  We will certainly get measures of sales (both units sold and currency received).  We may also get other useful measures like inventory on-hand, inventory in-transit, store-receipts, mark-downs taken at the store and perhaps some data around warehouse activity too.

Is the juice worth the squeeze?

I have heard this phrase a lot in recent months in a business context.  It’s so visual, I love it! 

It’s not quite enough though.  It’s pretty simple to understand that every project must be able to pay for itself and deliver a return.  Is the juice worth the squeeze?

It’s also true though that no organization has infinite resources of time or money.  If you have 10 projects that you could do but only enough resources to handle 3, you must prioritize those projects that help you meet your objectives (growth, profitability, market share).  What has the most bang for the buck? 

So with these 2 phrases in mind, it should now be easy…right? (Can you hear the sarcasm)?