Posted by Ed Kahn on May 16, 2011
I had the pleasure of speaking with a number of Forrester clients at our Consumer Forum last October. Nothing is more invigorating than discussing the needs of the day and applying our research to solve business problems. I presented a session on the use of forecast data and thought I’d share some of that material with you.
The first lesson I’d like to share is about definitions. Before going very far with any forecast data, make sure you know what it is that’s being forecast. This may sound simple, but definitions can be challenging sometimes; local conventions may create wrinkles in understanding, and each company makes a decision on where to draw lines around a category or behavior. For example, the Forrester Research Online Paid Content Forecast, 2010 To 2015 (US) refers to a video category. When we define the audience for online video, we include people who watch user-generated content as well as films and TV because we see all of these people as a potential paying audience for online video. This is important to know because that distinction expands the audience by more than 20 million online users.
Another variety of this apples versus oranges problem is exemplified by the definition of an “online buyer” in our online retail forecasts. In the Forrester Research Online Retail Forecast, 2010 To 2015 (US), payment is completed online; in the Forrester Research Online Retail Forecast, 2010 To 2015 (Western Europe) and Forrester Research Online Retail Forecast, 2010 To 2015 (Asia Pacific) forecasts, however, there are other, offline ways of paying for products. The logistical implications are considerable and, as a forecast user, you should take note of the local market conventions as you assess market opportunities and expected costs.
Have you encountered any issues with comparing and managing multiple forecasts that you thought were the same until you looked at the definitions? Tell me about them in the comments below. Also, please let me know which issues you’d like me to address in the upcoming Forecasting Best Practices posts.