Webinar with Aplusify: Predicting Association’s Growth w/ AI-powered AMS & LMS Integration #MapleLMS

Key Concepts

• AMS (Association Management System): A software system used to manage member data, events, and other association-related activities.
• LMS (Learning Management System): A software system used to deliver and track online learning content.
• AI (Artificial Intelligence): The simulation of human intelligence processes by computer systems.
• Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
• Data Silos: Isolated data sets that are difficult to access or combine.
• Member Engagement: The level of interaction and involvement members have with an association.
• Member Retention: The ability of an association to keep its members renewing their memberships.
• Non-Dues Revenue: Revenue generated from sources other than membership dues, such as events, courses, and sponsorships.
• Churn Risk Score: A metric that predicts the likelihood of a member not renewing their membership.
• Personalized Learning Paths: Customized learning experiences tailored to individual member needs and interests.

Challenges Associations Face Without Integrated Data

• Fragmented Member Journeys: Lack of a unified view of member interactions across different systems.
• Low Engagement and Retention: Members may disengage due to a lack of personalized communication and experiences.
• Revenue Growth Barriers: Underutilization of non-dues revenue opportunities due to a lack of data insights.
• Data Silos: Prevent effective decision-making and member personalization.

Benefits of Combining LMS Learning Data with AMS Data

• Enhanced Member Engagement:
  • Creating personalized learning paths based on member interests and past interactions.
  • Leveraging LMS and AMS data combined to tailor learning experiences.
  • Sending personalized course recommendations and event invites.
• Improved Member Retention:
  • Gaining a holistic view of members' learning progress and engagement.
  • Providing timely support to members based on their learning activities.
  • Automated reminders for renewals, certifications, and courses.
• Data-Driven Decisions:
  • Analyzing how education impacts membership renewals and event participation.
  • Using Predictive Analytics to identify trends that could boost engagement and retention proactively.
• Increased Non-Dues Revenue:
  • Targeting offers based on member data to upsell courses, announce new courses, and offer premium memberships and event registrations.
  • Aligning learning and membership packages based on specific behaviors and interests.
• Simplified Administrative Tasks:
  • Eliminating redundancies by avoiding data entry into two different systems.
  • Saving staff time and providing a great member experience.

How AI Enhances AMS and LMS Data

• Personalized Learning Journeys: AI creates personalized learning pathways within the LMS, tailoring content to the unique interests and learning styles of members.
• Predictive Analytics for Membership Retention: AI analyzes member behaviors, giving the power to identify at-risk members before they churn.
• Automated Engagement Workflows: Automation powered by AI streamlines workflows, freeing up valuable time for the team.
• AI analyzes AMS data to forecast retention and engagement Trends: AI can predict member renewals based on past Behavior patterns.
• AI-powered Segmentation: AI-powered segmentation helps associations Target the right members with the right messaging by analyzing member data behaviors and engagement patterns.
• AI Data-Driven Insights to Increase Non-Dues Revenue: AI can help uncover new non-dues Revenue opportunities by analyzing Trends in member Behavior event participation and purchasing patterns.

Predictive Scores with AI

• Definition: A data-driven metric generated by AI that forecasts a member's likelihood to renew, engage, or interact with the association in the future.
• Importance: Essential for proactive data-driven decisions for improved member engagement and retention.
• AI Enhancement: AI evaluates multiple factors such as event participation, course completion, email interaction, surveys completed, and more.
• Key Metrics Tracked:
  • Engagement Trends (event attendance, content interaction, email open and click rates).
  • Learning Participation Rates (course enrollment and completion rates, certification achievements, frequency of logins).
  • Member Satisfaction Scores (from surveys).
  • Membership Renewal Behaviors.
  • Membership Engagement with Peer Networking (community participation, mentorships, volunteer involvement).
  • Social Media Engagement.

Live Dashboard Demo (Maple LMS)

• Revenue Generated from Courses Widget: Tracks revenue generated from courses with member count, providing a quick overview of the total number of members engaged with a specific course and the revenue generated from them.
  • Filters: Membership type, time range, member name, course.
• Total Renewed Members with Respect to LMS Engagement Bar Chart: Represents the renewed member count alongside their LMS engagement data, helping track how LMS usage impacts membership renewals.
  • KPIs: Total revenue from LMS participants, number of LMS participants, hours spent in a month, churn risk score.
• Revenue Comparison of Members with Respect to LMS Engagement Pie Charts: Segments revenue based on LMS engagement, showing revenue from members engaged with the LMS versus those who are not.
• Renewals Versus Revenue Comparison of LMS Participants and Non-LMS Participants Combo Chart: Provides insights into revenue trends and renewable behavior.
• Actual Versus Predicted LMS Course Completion Dashboard: Tracks actual versus AI predicted LMS course completion percentage.
• Renewable Prediction Score Comparison: Shows how likely members engaged with the AMS are to renew and how likely non-LMS users are to renew.
• Predicted Versus Actual LMS Revenue Widget: Tracks predicted versus actual LMS Revenue.
• Members Not Currently Engaged in LMS Courses Highlight: Identifies members who are not currently engaged in any LMS courses but should be, with AI-suggested courses based on each member's interest and behavior.

Steps to Implement AI and Predictive Analytics within Your AMS

1. Assess Your Current AMS and Data Infrastructure: Evaluate your AMS and review your data.
2. Define Your Objectives or Goals: Use including predicting renewals, personalize engagement, maybe you want to forecast your revenue or maybe it's a combination of of goals.
3. Choose the Tools That You Want to Use: Choose those tools that you think will be most effective and maybe you want to see demos about those different tools.
4. Data Integration and Setup: Integrate your data essentially link your AMS with your data sources.
5. Implement AI Features: Use AI to personalize emails and event invites based on your members behavior.
6. Train and Support Your Staff: Train your teams make sure they understand how to use AI insights for decision making.
7. Monitor It and Improve It: Evaluate the performance track kpis like renewal dates engagement revenue.
8. Scale AI Use: Explore new AI applications such as content creation and chat Bots may help you and integrate more systems as you see that there's a need.

Key Insights Recap

• AI-powered AMS and LMS integration boosts engagement, retention, and revenue growth.
• Predictive analytics for data-driven decisions.
• Personalized learning journeys for increased LMS adoption.

Special Offers

• Maple LMS:
  • Free demo of their system integrated with an AMS.
  • 15-day free trial to Maple LMS.
• Aplify:
  • Free AI assessment of your system.

Conclusion

Integrating AI with AMS and LMS systems offers associations a powerful way to enhance member engagement, improve retention, and drive revenue growth. By leveraging predictive analytics and personalized learning paths, associations can create more meaningful experiences for their members and optimize their internal processes. The live dashboard demo showcased the potential of AI-driven insights, and the outlined implementation steps provide a roadmap for associations looking to adopt these technologies.