Portland Marketing Analytics (PortMA) | Portland, Maine

Accounting for Shifts in Event Marketing Data

Accounting for Shifts in Event Marketing DataWhen reviewing data for event marketing, we typically look at it in two different formats: tour-to-date and the most recent biweekly period. Tour-to-date gives a good summary of how a program is performing overall. Biweekly provides a snapshot of what is going on right now in the program.

 

Typically, so long as your n sizes are robust, your biweekly data will fluctuate only a moderate amount from your tour-to-date data. Something sitting at 75% in the TTD data may go as high as 85% or low as 65%. Expect to see that type of fluctuation. What you really need to keep an eye out for is when data goes outside of those parameters. If you find that data is well outside of your expectations, there are a few potential reasons to keep in mind.

Consider where your event marketing activations took place. Determine if there is anything unique about the events or locations. We once had a sporting event that was rained out. Field staff reporting indicated that the weather was cold and unpleasant. It was also the source of many of our surveys from the previous two weeks. It’s something people forget, but weather can have a big impact on people’s moods. This can affect how receptive they are to being introduced to a new product.

See if anything has changed about the activations. Were there changes in product sampling or premiums being distributed? Even though premiums may not have much, if any, impact on survey responses, it’s still a good idea to be aware of any potential outside influences. Other potential changes might include who was conducting the event. People who are new to a project may not be as proficient at selling the product. It’s possible that they will simply need some time to get used to how best to present the product. You should see an increase back to normal levels within a few weeks after a personnel. This also may be an indicator that the new Brand Ambassador needs additional training.

Ensure there were no errors on your own part. If you re-code or modify your data in any way before analysis, it’s possible to make a mistake in doing so. You might accidentally swap your coding (i.e. the top of your scale went to the bottom and vice versa). Or, you may accidentally exclude a response option. A careful review of the data at its base level can reveal if any mistakes were made.

These are just a few potential causes of any aberration in consumer opinion at an event marketing venue. It is important to be aware of changes, and determine what the cause(s) may be.

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