Why Price Forecasting is a Requirement for Pricing Optimization
We recently conducted a webinar in conjunction with the Professional Pricing Society where we discussed the 8 Myths of Pricing Optimization. The webinar was so popular and highly rated that we decided to expand upon it in eight blog posts over the coming weeks.
If you missed the first or second blog posts on this topic, we offer the links for your convenience.
Myth #3: Why Historical Data is Required – But Not Sufficient – for Pricing Optimization
One common question we often get pertains to whether historical data is good enough for pricing optimization. Customers ask how they can optimize the price that wins in the market tomorrow if they look only at historical data that won yesterday.
Good question.
There are entire industries that have built pricing models around the fact that they will NEVER get a better price tomorrow than they did today. The high-tech and medical device industries are two. Price erosion is a fact of life in these industries, as new and better technology cannibalizes legacy products. What can price optimization do for them?
If the ONLY thing you do is look at historical pricing, then you’d certainly be in trouble in many circumstances. But pricing optimization should focus on where prices are going – or price forecasting – versus price history. By analyzing trends in historical data, weighting more recent transactions more heavily than those that took place a quarter or six months ago, will help you understand where the market price will be tomorrow, not where it was yesterday. Historical pricing information can provide insights based not only on historical averages, but also on an analysis of trends and rates of change, which provides a more accurate picture of where prices are going.
Take, for example, a new server that was thought to be an evolutionary – not revolutionary – improvement upon the previous generation. That new server, however, provides its users with unique benefits in the marketplace, and may not follow historical pricing patterns. If historical pricing analysis shows that pricing has been held longer than previous generations of the product, the price forecasting capabilities will decelerate the rate of decline of pricing in the market place. Ultimately, that can mean millions of dollars of “found money” based on recovering lost-opportunity revenue.
Any pricing technology that claims to do pricing optimization must have a foundation in price forecasting. If not, your pricing is destined to move backward.
Thanks,
Patrick
In high tech, one of the biggest issues is that in an uncertain market, there are irrational vendors that voluntarily take value out of the market to build market share for non-revenue based reasons such as setting up a new funding round or to set up another market. One of the valuable aspects of using historical data is to understand when one of these fundamental shifts has taken place and to adjust, perhaps by bringing either a new release or value-added functionality to market and maintain current margins. Otherwise, you lose market share and may not know why if predictive forecasting is simply being used as a model for expected price erosion.
This is the perfect argument for predictive analytics on top of Business Analytics. Companies that can find historical trend patterns and apply that to current data along with forecasting those variables that drive your sales, you can predict what your price is going to be and what you need to price things to be competitive.
Data is such a wonderful thing when used well….
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