Wine prices and BI

There is an interesting piece in Advertising Age by Jack Neff discussing the increasing desire of companies to move to premier pricing, even at the cost of losing some customers.  It raises the critical question – how good is the information that companies have on the performance of their brands?  For a multi-national company in particular even tracking the profit margin of a particular product or brand can be a thorny issue – do the French and the Italian subsidiaries allocate all their costs in the same way as the Brazilians or the Japanese?  Chances are they do not, and plenty of internal meeting time is spent debating whose country’s operations are more effective.  Yet pricing is a powerful weapon. I recall when I worked on a project at Shell Retail that we had just put in a data warehouse that tracked every single (non fuel) transaction at all Shell’s shops in Germany.  For the first time the category managers were able to see comprehensive information about sales by store, by SKU etc each morning.  In this way they could track the behaviour of promotions, for example, and rapidly see the effect of other changes they made.  For example in one cluster of stores they experimented by raising the price of wine to see what effect it had on volume sold.  Interestingly, it had no effect at all, so they kept nudging up the price until an impact could be seen.  This single decision, when multiplied across all the stores in Germany, had a significant impact on profitability.

Pricing is a complex animal and hard to predict its effects in advance.  Hence it is critical that you have in place an information infrastructure that allows you to see the true operating margins of products and brands, and also the margin by customer and channel.  This may require a major investment, since if the cost allocation rules are not uniform in a corporation then you will need a system that can cope, and be able to continue coping when changes occur.  You may not need “real time” information, but as in the above example you at least want to be able to see the effect of decisions the next day, perhaps within hours.

The article mentions Unilever as a case study amongst others, and indeed its comprehensive network of linked data warehouses around the world give it precisely the ability to see true gross margin across all their brands.  This enables them to decide which brands to invest in further, and which to pare back, as well as enabling them to track more operational aspects of business performance.

This ability, sometimes called “3D marketing” i.e. to be track performance across multiple business dimensions like customer, channel and product, is a powerful enabler to more subtle and profitable pricing decisions. In some projects I am aware of serious pricing discrepancies causing very real gross margin issues, which were buried away in a maze of confusing systems and so went unnoticed, in some cases for years.  Clever pricing can be highly profitable just as inadvertently poor pricing can cost you money.  The common denominator is having the ability to track profitability at the detailed level, in any business dimension, across the globe. Companies like Unilever have shown that this can be done.

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