Engagement & Retention project | EXPLIoT Academy
Note: I don't have actual cohort data of the monthly cohort and their activity [active members] for subsequent months.
However, I managed manually to find monthly signups and monthly active learners for the year 2024.
Retention Calculations are based on the above new signups and monthly active learners data.
Methodology used
- Normalize Active Learners:
Since we don't have cohort-level data, we can normalize active learners by considering them as a percentage of cumulative signups up to each month. This method accounts for the growth in signups while approximating retention. - Assume December (Previous Year):
To address the missing December (previous year) data, calculate the average of new signups and active learners across all months and use these values as a base for December of the previous year: - Average New Signups = 68
- Average Active Learners = 196
- Retention Formula:
For each month:
Retention (%)=Active Learners in a Given MonthCumulative Signups up to That Month×100Retention (%)=Cumulative Signups up to That MonthActive Learners in a Given Month×100 - Cumulative Signups:
Calculate cumulative signups starting from December (previous year) to account for all users.
Based on above methodology, retention calculation is as follows
What type of retention curve above data shows?
Retention Curve Type | Scenario | Explanation |
|---|---|---|
Declining Curve | Partially Fits | Your retention curve declines significantly after the initial months but stabilizes later. |
Flattering Curve | Partially Fits | The curve flattens at lower percentages (20-40%), indicating some long-term user retention. |
Smile Curve | Does Not Fit | There is no evidence of churned users returning or an upward trend in your data. |
So finally what it shows is --> declining-to-flattening curve
EXPLIoT Acadmey's retention graph most closely resembles a declining-to-flattening curve, where user retention drops significantly after onboarding but stabilizes at lower levels over time.
Based on ICPs and user behaviours, key churn reasons are:
Churn Category | Key Churn Reasons |
|---|---|
Lack of Immediate Value | Users don’t experience an “aha” moment early enough, leading to disengagement. |
Perceived High Price vs. Value | Some users (especially students) find the cost unjustified for their needs. |
Completion Drop-Off | Users start courses but fail to complete due to lack of motivation or time constraints. |
Lack of Career Progression Visibility | Users don’t clearly see how the course benefits their career advancement. |
No Community or Hands-on Support | Users struggle with doubts and don’t receive timely guidance, leading to drop-off. |
No Habit Formation | Learning is not integrated into their routine, leading to eventual disengagement. |
Churn Type | Definition | Examples in EXPLIoT Academy |
|---|---|---|
Voluntary Churn | Users deliberately stop engaging due to dissatisfaction or external preferences. | - No clear career ROI after finishing some lessons. |
Involuntary Churn | Users churn due to uncontrollable or accidental reasons. | - Expired payment methods for subscriptions. |
Negative Indicator | Why It Signals Churn? | Potential Solution |
|---|---|---|
Low Course Completion Rates | Users who don’t complete even one module in the first 14 days are at high churn risk. | Automated progress nudges, milestone rewards, & gamification. |
Low Engagement with Hands-On Labs | Users who avoid practical exercises tend to drop off earlier. | Encourage lab-based learning with personalized recommendations. |
Unopened Emails & Notifications | Users not engaging with Academy’s communication likely don’t return. | Behavior-based reactivation campaigns. |
No Community Interaction | Users who never ask questions or engage with peers are more likely to quit. | Promote discussion forums, peer mentoring & Q&A sessions. |
Drop in Login Frequency | If a user doesn’t log in for 2+ weeks, they are at risk of churning. | Automated win-back campaigns, free trial extensions. |
High Refund Requests / Subscription Cancellations | A direct signal of dissatisfaction or mismatch with expectations. | Feedback collection & offer alternative learning paths. |
Now that churn risks are identified, let's move into ICP-based retention solutions😀
Step 1: Refining ICPs Based on Actual Learner Types
To develop a granular, data-backed retention strategy, we must refine ICPs and map them to actual user types (Casual, Core, Power Users).
🔹 Finalized ICPs for EXPLIoT Academy:
- ICP 1: Independent Cybersecurity Professionals (Researchers & Consultants)
- ICP 2: Enterprise IoT Security & Compliance Teams
- ICP 3: IoT Security Students (University-level learners & Early Professionals)
🔹 User Types Across ICPs:
- Casual Users → Engage infrequently, high churn risk
- Core Users → Moderate engagement, at-risk for drop-off
- Power Users → Highly engaged, long-term retention potential
Step 2: Mapping Retention Strategies to ICPs & User Types
ICP & User Type | Casual Users (Low Engagement, High Churn Risk) | Core Users (Moderate Engagement, Active Learners) | Power Users (High Engagement, Long-Term Retention) |
|---|---|---|---|
ICP 1: Independent Cybersecurity Professionals (Researchers & Consultants) | - Fast-Track Intro to IoT Exploitation (7-day challenge to reduce drop-off) | - Monthly deep-dive sessions on niche exploit research | - Exclusive access to pre-release vulnerability research |
ICP 2: Enterprise IoT Security & Compliance Teams | - Guided compliance frameworks (IEC 62443, NIST) | - Team-based dashboards for progress tracking | - Advanced simulations & threat modeling for enterprises |
ICP 3: IoT Security Students (University Learners & Early Professionals) | - Structured beginner’s roadmap (reducing cognitive overload) | - Monthly CTF-style hacking competitions | - Internship/job placement assistance |
Step 3: Aligning Retention Interventions to ICP-Specific Drop-off Points
Retention success depends on identifying where users drop off and applying the right intervention at key drop-off points.
| Drop-off Stage | Casual Users (First 7-30 Days) | Core Users (1-3 Months In) | Power Users (3+ Months, Long-Term) |
|---|---|---|---|
Drop-off Risk | Low activation, unclear value perception | Learning fatigue, unclear career outcomes | Burnout, limited advanced material, need for professional edge |
Intervention | - Automated “Aha” moment emails within 7 days | - Personalized content recommendations (AI-driven learning track suggestions) | - Exclusive research access & high-level IoT exploit simulations |
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