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When you partner with StellarAlgo, you gain access to a growing library of custom Plays, backed by our own industry research and data asset made up of more than 200M fans, to help you activate target segments of your fan universe.
Here you will find a small sample of our Data Assessment Play, designed to give you a small taste of the targeted guidance and step-by-step approach outlined in a typical Stellar Play.
To unlock the complete Play — including proprietary industry trends, Platform-specific directions, individualized support from a dedicated Account Manager, and much more — contact us to learn more about becoming a StellarAlgo partner.
The Data Assessment Play represents an opportunity for you to understand the breadth of your data capture across your fan universe in almost real-time by leveraging StellarAlgo’s data refreshes. By determining data capture rates, you can identify segmentation strategies that can be executed based on the current data available, as well as inform paid data append efforts. This will enable you to execute on strategies that are best supported by your data and allow you to become more strategic with your data append efforts.
This Play is highly flexible as it can apply to your entire fan universe or key segments of interest.
A single data integration is all that’s required, though this Play scales very well with additional integrations.
For added value, include multiple data integrations, such as:
The Data Assessment Play can be executed on an annual or one-off basis. It will typically be most useful to you when:
At a minimum, we recommend conducting the Data Assessment Play on an annual basis during the post-season or off-season.
The StellarAlgo Platform makes it easy for you to execute the Data Assessment Play quickly and with low effort.
A single analysis will take less than one week.
Opportunity identification is where you will assess whether undertaking the strategy outlined in this Play is a worthwhile investment of your time and resources. The Data Assessment Play spans your entire fan universe and is an opportunity to better inform the rest of your seasonal strategies – this Play drives high value with relatively little input required.
A rich fan data asset bridges the gap separating ambitious fan strategies and what is supported by your fan data, such as:
In the Opportunity Identification phase, identify what fan segmentation strategies your team would like to run during the season, if this information is already available to you. You can then focus on segments of fans or defining characteristics to determine whether your fan data can in fact support these segmentation strategies.
Match rates is the key metric in the Data Assessment. In the Baseline phase, you will focus on determining data match rates for your entire fan universe. You can then use this baseline compare data match rates of specific sub-segments of your fan universe against.
Identify the match rates for your overall fan universe – in the Fan Universe Dashboard, your match rates are automatically and conveniently displayed for you. Select the data type of interest in the Fan Universe Dashboard to view the match rate (e.g., age, gender, in-market, etc.).
The planning stage is where you identify what segments of fans within your fan universe you want to identify data match rates for.
We recommend investigating segments of fans that are of interest across the various departments within your organization. Often, we see partners investigate data match rates based on the products their fans are buying (e.g., full season buyers, half-season buyers, group buyers, single game buyers, etc.). The results of the Data Assessment Play can then be served to the appropriate departments to assist them in informing your segmentation strategies for the season.
In this stage, you will leverage the Segment Builder to calculate your match rates for the specific segments of fans that you identified in the Planning stage.
Identify the match rates for specific segments of fans:
Calculate your match rates for each data type of interest. Divide the count of fans by the total number of fans in the segment and multiply by 100%.
Once match rates have been calculated, strengths and deficiencies of the data should be identified and captured.
Some suggested criteria to help with assessing your match rates include:
For the specific segments you investigated, compare the data match rates for those segments against your overall fan universe to identify where match rates are above or below your fan universe average.
Develop a deep understanding of your fan universe.