Big data has become a powerful theme over the past year. A Google search on “big data” generates 7,730,000 hits. A useful survey post by Julie Hunt on Big Data, Intelligence and Multi-Faceted Innovation gives the big picture, but what does this mean for the pricing world? And where does big data for pricing come from?
Traditional pricing systems have been focused on internal data. There is much that can be learned by slicing and dicing historical transactional data. One can infer elasticity curves and use these to set target prices. Advanced systems like those from Pros Pricing can even infer the “willingness to pay” of different types of customers. When combined with information from invoices and other transactional systems, one can even build the classic price waterfall and understand the on and off invoice elements that lead from the list price (what we state the price is) to the pocket price (the amount of money that actually ends up in our pocket after discounts and on and off invoices expenses like shipping and financing costs).
But is internal transactional data enough to supply the data needed to execute a world-class pricing program? Prices are shaped by many things other than our internal history and pricing policies. In B2B the established best-practice is value-based pricing (see for example this paper by Stephan Liozu or the many resources available on the LeveragePoint website). Value-based pricing is based on the economic impact (its effect on revenue, costs, operating capital and capital expenditure) of one offer compared to the next best competitive alternative for a specific customer or segment. Estimating value requires information about customers, competitors and external market conditions. The data feeding value models must come from multiple sources (see Figure 1: Sources of Pricing Data).
There is value in transactional pricing data, but it is not enough to rely on this data to build and execute an effective pricing strategy. One also needs data from third-party sources. This can be market research and other studies provided by consulting firms, or data captured by analysts and other information collectors. LeveragePoint has partnered with IDC to provide pricing data for the high-technology sector and additional partnerships will be announced in 2012. There is also a great deal of value to be found in what the intelligence community refers to as ‘open source data.’ This is anything publicly available, but these days it generally means the Internet. Value models often include variables for things such as labor costs, commodities prices, plant utilization rates and so on. One can often find this data, or at least reference points, on the Internet. One can even build live data feeds into value models so that as the model is constantly updated as the environment changes.
Perhaps the most important source of pricing data comes from conversations with customers. This data is sometimes undervalued by people in pricing, who can discount what sales people are hearing in the field, but in fact it is often sales that has the most relevant and recent data about customers and competitors and this data needs to be constantly collected and analyzed. Any serious pricing program needs to build in the feedback loop from sales.
At LeveragePoint we have designed our SaaS platform for value-based pricing to collect all these types of data, to organize them and to make them searchable, and to integrate them into value models and value propositions See Figure 2: Data Search in LeveragePoint).
Leveraging big data for pricing requires more than just data. It also requires a framework to organize the data and make it meaningful and actionable. LeveragePoint provides a software platform where the many types of data relevant to pricing can be gathered and kept current. By bringing the sales team into the loop it helps to keep the data current and, more importantly, relevant to the field sales people who actually go out and negotiate the price.