Business Tip 6
Understanding In-Store Promotional Event Mix

The Basics

Promotional Event Mix is an assessment of a company's portfolio of in-store events over a given period of time. As promotional calendars are often set up anywhere from six months to a year out, reviewing promotional events for the past year is essential for determining which programs should be repeated, modified, or dropped. However, accurately evaluating the results for in-store promotional events is often problematic since syndicated data do not always capture total event sales -- especially if the promotion was regional.

One method for resolving the syndicated (consumer dollar) data gap is tracking shipment sales during the in-store promotion along with consumer sales. That is, there are two levels of promotional dollar sales assessment: increased shipments to the retailer and increased consumer purchases. Since all retailers who participated in the promotion should be captured in shipment data, a combined assessment of syndicated consumer and internal shipment data will provide a more complete and balanced review of past promotional events.

While bar charts are useful, a quadrant analysis (see Business Tip #1) can provide more insightful comparisons based on both share and growth. For promotion mix, reviewing two different quadrant analyses - consumer and shipment incremental dollars - allows for a more comprehensive assessment.

In Action

Incremental Shipment Dollars A promotional event's success is usually based on whether the program drove incremental sales (dollars above expected baseline sales). While syndicated data provides incremental dollar sales based on a calculated baseline, incremental shipment dollars needs to be determined internally. Instead of attempting the complicated process of calculating a shipment baseline by retail customer, it is a common industry assumption that between 10 percent and 20 percent of shipments during a promotional period are incremental.

Example: Total Shipment Dollars x 15% = Incremental Shipment Dollars

Often total incremental retail sales for a category also falls within the 10 percent to 20 percent range.

Once incremental shipment and consumption dollar sales are available, the remaining measures required for a quadrant analysis are as follows:

How does the quadrant analysis work?

While a quadrant analysis can be done in a traditional bubble chart format, in the example below we are listing share and growth numbers without graphic representation. The promotional events for a period of one year are plotted in four quadrants:

1. Winners: High % Share/ High % Growth
2. Sleepers: High % Share/ Low % Growth
3. Opportunities: Low % Share/ High % Growth
4. Questionables: Low % Share/ Low % Growth
   

Using the same quadrant definitions, shipment data by promotional event can be placed on the same grid and compared to the event analysis for consumer sales.

Once both charts have been built and compared, promotional calendar planning can then be based on the appropriate business goals at hand. Note that over half of the Questionable events for the Consumption analysis are not Questionable in the shipment analysis. That is, just because a program did not perform well in the store does not necessarily mean it was unsuccessful.

The Deliverables

With this combined quadrant analysis approach, promotional planning will include more informed decisions and ensure a more balanced in-store promotional event mix. As a result, the supplier's promotional planners gain a bigger picture of both internal shipment cycles as well as consumer behavior at retail.

By making use of a software solution, such as XP3, various syndicated and shipment measures can seamlessly be loaded into one database and quickly added to a PowerPoint presentation. Additionally, the complex analyses of different markets, time periods and products can occur on-the-fly using simple data queries. Refreshing the display from source data occurs automatically, as does the development of actionable recommendations.

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