Business Tip 5
Understanding Product Development Index

The Basics
What it means and why you need to know.
Product Development Index (PDI) is a method to benchmark sales per capita in a given geography against sales per capita in a larger area, such as the total United States. The goal of this measure is to quickly identify geographic areas that are opportunities to develop the product in question.

For instance, if sales per capita of rice in Georgia are much lower than sales per capita of rice across the entire country, it stands to reason that there is an opportunity to grow that category in Georgia.

Product Development indices are generally interpreted by this rule of thumb:

In Action:
How is a Product Development Index Calculated?

Chart A

PDI is typically calculated as a ratio (or index) of the sales per capita in a given geography to the sales per capita in a larger geography (Total US in the equation above). In order to perform this analysis, you do need access to demographic data as well as sales data. A good, free source of US demographic data is: www.census.gov

What does a PDI analysis look like?

Chart B

In this example, PDI is graphically displayed at the county level for product X. You can quickly identify opportunities (in red) by simply looking at this map. This information by itself, though, is seldom useful. A good PDI opportunity analysis generally has this quick-reference information, along with some additional detail that can help validate that opportunity.

Chart C

For example, the slide above identifies opportunities at a retail specific level across six chains. In addition to visually mapping opportunities, this analysis quantifies the importance of each retail trading area in terms of both population and ACV. Furthermore, it provides the viewer with year-on-year growth numbers. With this more encompassing view, the user can quickly identify the target areas for growth and determine if the opportunity is significant enough to warrant further investigation.

Considerations for Effectively using PDI In practice, people tend to use the total population of an area to perform PDI. This is probably due to the difficulty of aligning syndicated data with demographic data. Using a tool like XP3 and data like that provided by the US Census, there is no reason not to go into a little more depth. For instance, why not create ethnicity- and education-specific development indices?

The results of complex measures such as PDI cannot easily be aggregated by an end-user. Consider building these measures in an OLAP system at a granular level to afford end-users the ability to roll items together on the fly.

The data, products and accounts depicted in this example are fictitious. Any resemblance to actual data, products or accounts is purely coincidental.