The Story behind IMPACTfox^TM

IMPACTfox was created to solve a marketing engineering problem that has taken shape over the past several years. The more that browser and smartphone privacy have taken hold, the harder it has become to discern how much benefit a business receives as a result of the time & money it spends posting on social media.

Marketing engineering, which requires gauging the return from each marketing channel a business uses, utilizes whichever types of measurements are available within each channel. In the case of “organic” social media (meaning not paid ads or boosted posts), the return is primarily in the form of future revenue that social media can bring via top of mind awareness and brand affinity.

This type of future benefit from posting on social media is almost entirely immeasurable. So for a marketing engineer to determine whether a business’s social media posts are having the desired effect, the future benefit has to be inferred. Using measurements of current behavior, such as the number of likes, comments, and shares reported in social media posts, and data from Google Analytics, estimates can be made of how well a business is benefiting from its social media effort.

Pre privacy, Google Analytics gave information that was very helpful in generating such estimates:

• Google analytics was great at separating social media visits to a website for analysis, making it possible to monitor how well those visitors reached preset goals in a website. For ecommerce sites, revenue from online purchases could be measured and attributed to a particular social media channel
• Posts with links to a website could be tagged with UTM parameters to indicate success on a per-post basis. This capability is still used heavily, but the UTM parameters themselves don’t always make it the whole way into Google Analytics
• The Google Analytics feature known as Attribution Modeling provides a way to help determine the contribution of social media visitors to revenue goals, but it brings with it uncertainty because its results depend on which attribution model gets applied.

Besides the data in Google Analytics, the per-post user activity measurements available from the within social media networks including the number of likes, comments, clicks, and other user behavior is key to on a per-post basis. When taken alongside Google Analytics data, patterns could be extracted to indicate how well a business was conducting its social media activity.

Of course, the actual dollar return on the money invested in paying someone for posting on social media was and remains immeasurable, due to the dependence on revenue in the future.

Fast forwarding to the new era of heavy privacy restrictions, Online Impact Group realized that a completely new approach is required in order to let a business owner know how successful they are in their social media activity.

Several working assumptions were part of the new approach:

• Future revenue from social media posts is highly dependent on how well each post is crafted for generating top of mind awareness and brand affinity
• Since top of mind awareness and brand affinity attributable to social media posts are almost completely unmeasurable, a proxy for these values is needed
• The proxy would have to come from measurements available within social media networks and not depend on input from Google Analytics data.

With the above as a guide, the new approach to gauging the business benefit from investment in social media activity was to use social media data to generate proxy values for top of mind awareness and brand affinity, and determine the ranking of a business within a large set of the same values from businesses in the same industry.

Using this approach, it also becomes possible to compare the top of mind awareness and brand affinity rankings of one business with the same rankings of direct competitors. Comparing the rankings lets a business owner know if they or the competitors are doing a better job of gaining awareness in the minds of a target audience that overlaps among each competitor.

The data chosen for this ranking approach and the calculations for determining the rankings are described in this page. In summary, the rankings are in the form of percentile scores, which allow easy determination of whether a business’s investment in social media posting is performing as good as or lower than can be achieved.

As an example, if a business’s social media ranking is 30, it indicates that a business is only doing better than 30 percent of other businesses in the same industry or niche. It also means that 70 percent of the other businesses are having better results from their social media posting effort, and that the business should up their social media game in order to get closer to what can be achieved within their industry.

It’s not entirely sufficient for a business to only know whether social media results are ranking low or high. Equally important is information showing what needs to change in order to bring the results higher. Fortunately, the components used in the formulas that generate rankings can be used to determine where to focus the effort needed to improve. These components are:

• Interest Level Rank: A percentile ranking of likes, comments, and shares relative to other businesses in the same niche
• Activity Rank: A percentile ranking of how many posts have been published compared to the other businesses
• Followers Rank: The percentile ranking of the number of fans or followers in a given social network, compared to the other businesses

By focusing effort on whichever of the 3 component scores above is most in need of improvement, a business can achieve results that lead to better top of mind awareness and brand affinity, ultimately affecting future revenue.

Each component above has its own methods for being optimized, a portion of which are given in the resources here.

The new approach comes with several additional benefits baked in:

• Percentile rankings are a very easy way to indicate performance
• Valuable time doesn’t have to be spent on generating and evaluating complex performance reports
• A business owner who outsources social media management can use the data when judging whether their provider is the right one to use for staying ahead of the competition in social media.

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