Throughout this past year, we have investigated how different kinds of data can be used to enhance audience segmentation practices. We first discussed the importance of audience segmentation for arts organizations. We then focused on CRM data and compared the difference between the two main kinds of systems.
The real value comes in once arts managers take the time to analyze this data that the traditional system generates. They can then begin to categorize the customers into “profitability tiers” by correlating the purchasing and consumption frequency alongside other transaction records. Behavior patterns can then be predicted by using “regression" analysis.
Meanwhile, the data extracted from social CRM systems presents an overview of each customer’s communications by analyzing media usage frequency, duration, content and utilized channels, as well as interactions among social accounts.
After arts mangers acquire the appropriate information from CRM reports, they can obtain a better idea of their current audience’s behavioral features. The key question then arises: how can arts mangers apply this CRM data analysis to properly segmentation their audience? The answer is combining this CRM data with qualitative information from conducted surveys and interviews. Once you properly segment your current audience according to psychographics and behaviors, you can then apply demographic features to different audience clusters. To find out more about how to do this, click here.
In conclusion, arts managers will find most audience segmentation success by taking the following measures:
1. Collect more developed CRM data by using both traditional and social systems. Perform sentiment analysis whenever possible on social data.
2. Pair different data sources together to obtain the most complete picture possible of your patrons.
3. Properly segmentation your current audience based on both psychographic information and behavioral records
4. Develop customized communication, marketing, and programming strategies to acquire new audiences that share similarities to your current audience segments.
Have you found audience segmentation success in pairing different kinds of data together? We would love to hear about it in the comments below!