Multi-year, experiential marketing programs have an interesting, inherent problem. The benchmark data from previous years can be incredibly useful when scheduling your program for the next year or when predicting the outcome of the year to come. By looking at averages year-to-year, you can gain valuable information about how you can expect your program to perform. However, when looking at historical, experiential marketing data, it’s important to understand when it loses its value.
How does historical data lose its value?
The first thing to understand is that your data may lose value due to changes in your activation. The change could be something as simple as a change in venue type (e.g., at bars and restaurants as opposed to liquor and retail stores). If year one data is all bar activations, but your experiential marketing program incorporates liquor stores in year two, the year one data may be only partially applicable. You can still use the data to accurately predict the performance of year two bar events, but it will not be valid for retail.
Which historical data is most important?
You should define which metrics are most important (event type, state, etc.), then use that to determine whether the experiential marketing data is applicable across multiple years.
There are a number of ways you might want to do this. For example, with data organized by state, you might decide that state data may not be applicable from one state to another.
Alternatively, you might decide that states in the same region can be used interchangeably (Louisiana and Mississippi for example). This type of regional grouping may be slightly less accurate, but can still offer relevant insight. You could decide which states contain similar markets (Myrtle Beach, SC, and Miami, FL for example). This can allow a market analysis based on what you consider common traits other than geographic location.
Setting limits on historical data
Using experiential marketing data must take into account the value of your data as time passes. While data from last year is certainly relevant, and two years is similar, what about data from three or four years ago?
As data ages, it loses relevance to your program. It’s important to determine a cutoff point at which experiential marketing data should no longer be considered because it has lost its relevance to the current state of the program. Market changes over five years can be startling. Trying to apply lessons learned from old data may not work. By setting a five-year limit, you can help to ensure that your analysis of a market is not negatively influenced by out-of-date information.
Ensuring you are applying historical data in a way that is valuable to your program now is as important as having the historical data in the first place. Establish your standards early on in the program, and you will ensure that you apply the best of your program data in the most valuable way.