Vision Critical is already working on leveraging machine learning and artificial intelligence (AI) to transform how companies engage with their community members and how they develop deeper customer relationships. At Summit, Paul Holtzman, SVP of data science, and Catherine Rogers, SVP of customer success, provided an exclusive look at Customer Relationship Intelligence Science, or CRIS—Vision Critical’s proprietary suite of Artificial Intelligence (AI) applications.
We have some highlights below.
Identify your highest-value customers—and learn how to better engage them
You can capture your community members’ Customer Worth, a variant of Lifetime Value, by integrating transactional or sales data to Sparq 3. Based on factors like a member’s engagement level in the community, AI algorithms can then identify the most valuable segments of your community. This type of measurement is important because it allows you to optimize your activities with those customers and, ultimately, raise the value of those segments.
Holtzman said AI algorithms can identify the specific actions you need to take in your community to drive response rates and engagement. This is Relationship MemoryTM at play: Sparq calls upon relevant data and recognizes influential factors that can help you maximize engagement.
Holtzman noted that it’s also possible to identify your lowest-value members—those who are less engaged—and use AI to identify ways of raising the value of those customers.
Listen, learn and adapt
An engaged insight community is key to capturing the data and insight you need to make better business decisions. Without an engaged community to begin with, AI and machine learning can’t do much in boosting the value of your customers.
You need to ensure that you have customers who want to remain a part of your community and be an active participant, said Rogers. Conducting the right activities in your community is critical to doing that.
Rogers recommended a three-step process to building your Relationship Intelligence and nurturing a community that’s highly engaged:
1. Listen by collecting and tracking the data on members’ responses to all activities, including surveys and “sharebacks”—content that you share with your members about their participation.
2. Learn by analyzing these data and using AI and machine learning analysis techniques to learn how to most effectively engage and communicate with your members.
3. Adapt by implementing what both machine learning and AI has told you regarding member engagement.
Improve retention—by looking into churn
Machine learning can pinpoint segments that are more likely to churn. With this information, you adapt engagement tactics targeted at those segments to reduce churn. The opposite works as well: you can identify segments with high retention, learn why they are active in the community and optimize your community activities using that insight.
The bigger point is that the more defined your segments are, the more effective you’ll be in leveraging machine learning modeling as an early-warning system to take actions that make sense for your insight community.
Enhance your survey design
The last example provided by the speakers showcased how AI can provide specific recommendations on how to improve the activities you deploy on Sparq. Your survey paradata—survey content like question types and how frequently you use them—are catalogued on Sparq, so algorithms can be applied to quantify the characteristics of activities that are most likely to resonate with your members. For example, if you’re wondering if grid questions can affect your response rate, AI can provide an answer and recommend an in-depth assessment of combinations of question types you should use. You can also identify the survey characteristics that drive lower response rates and with which segments—important information to optimizing community management.
If you’d like to learn more about CRIS, Vision Critical’s proprietary suite of AI applications, send us a message below.