How to Identify and Activate Dormant Users in PLG With AI?

Businesses come up with excellent user onboarding and retention strategies for their apps. So, they should be able to retain and engage their users effectively. Surprisingly, 90 percent of app users become inactive before day 30.

For iOS devices alone, the average churn rate by day 30 is 96.3 percent. Meanwhile, 97.9 percent of Android app users become inactive in a month. But why do businesses fail to engage these users even with so much technology available?

If you are concerned about your app's inactive user base, then you are at the right place. Quest Labs explores how to use AI technology to identify and activate dormant users in PLG.

Understanding Dormant Users in PLG

Dormant users are not essentially the users who are inactive with a product from the first day. Initially, they might have been active with your product, but as days passed, they became inactive. Moreover, they are not the customers that you have lost entirely. You can reengage them through the introduction of the right strategies.

The Impact of Dormant Users on Growth Metrics and Product Development

Dormant users have a multifaceted impact on PLG strategies. From a growth perspective, they represent a direct loss in potential revenue and a decrease in active user metrics, which are vital for sustaining the momentum of product-led initiatives.

Moreover, their dormancy can skew analytics and data-driven insights, leading to potentially misguided strategies that don't accurately show the behaviors of the active user base. On the product development front, dormant users are a missed opportunity for feedback and iteration.

Active users typically provide a continuous stream of insights through their interactions, which can be invaluable for refining and enhancing the product. Dormant users, having disengaged, do not contribute to this feedback and improvement loop, slowing the pace at which a product can evolve to meet market demands.

Identifying Common Reasons Behind User Dormancy

Understanding the root causes of user dormancy is crucial for addressing and mitigating its effects. Common reasons behind this phenomenon include:

• Lack of Engagement:

  When a product fails to engage users, they become inactive.

• Product Complexity:

  A complex user interface can discourage continued use and lead users to disengage.

• Changing Needs:

  Users' needs evolve, and if the product does not adapt accordingly, they may find it less relevant to their current situation.

• Competitive Options:

  The emergence of superior or more appealing alternatives can draw users away, pushing them into dormancy.

• Technical Issues:

  Businesses must ensure bugs and performance issues don't frustrate users. Also, 90 percent of app users abandon apps with high loading times.

AI Techniques for Identifying Dormant Users in PLG

Artificial intelligence (AI) serves as a linchpin in the strategic toolkit for combatting user dormancy within Product-Led Growth (PLG) models, offering sophisticated techniques for early identification and segmentation of dormant users.

Businesses can use AI to segment their users to understand them better. AI user segmentation draws out user activity patterns and predicts potential dormancy. It also categorizes users based on their likelihood of re-engagement.

With a high level of accuracy in user segmentation, AI reshapes how companies identify and understand dormant users.

Data-driven Identification

Compared to humans, AI can analyze a vast dataset and study patterns better. Therefore, identifying specific behaviors that cause user dormancy can be easily identified with the help of AI.

Therefore, AI helps businesses recognize when a user drifts toward inactivity. Also, it points out the contributing factors that cause this lack of engagement.

Predictive Analytics

AI can predict the future dormancy of users in PLG through predictive Analytics. The ML models use historical data and user behavior to detect early signs of potential disengagement.

These models are trained to recognize the precursors of dormancy, such as decreased login frequency, reduced interaction with key features, or patterns of erratic activity. By forecasting dormancy, businesses gain a valuable lead time to deploy re-engagement strategies, turning potential attrition into an opportunity for reinvigoration.

Segmenting Dormant Users

The causes of user disengagement can be different for different user segments. Similarly, the strategies for engaging them can also vary significantly. That is why businesses need the help of AI to segment users based on their activity levels. It facilitates coming up with more accurate and personalized engagement efforts.

For instance, if a business has a segment of users who gradually reduce their activity, they can engage them through reminders of product value. At the same time, if they abruptly stop using the product, it's essential to approach them with a solution for potential dissatisfaction. Only AI user segmentation can help businesses find the underlying reason for each user's lack of engagement.

Strategies For Activating Dormant Users in PLG with AI

Engaging dormant users is crucial for improving product-led growth strategies. AI is one of the most beneficial tools for reactivating users and offering personalized solutions to their dissatisfaction.

Personalized Re-engagement Campaigns

AI can efficiently analyze extensive datasets and create personalized engagement campaigns. By understanding a user's preferences and previous interactions with the product, AI can also help customize messages.

If a user frequently interacts with a specific feature before becoming dormant, the engagement message should highlight updates to that feature. Moreover, it should offer tips on using it more effectively. AI can determine the optimal timing and channel for these messages to ensure that the engagement effort is effective.

Product Experience Optimization

Dormancy often stems from a gap between the product experience and the user's expectations or needs. AI can pinpoint where these gaps might exist by analyzing patterns in user behavior that precede dormancy.

Perhaps users find certain features too complex or are missing functionalities they need. Leveraging AI insights, businesses can iteratively improve the product experience, addressing common pain points and enhancing usability.

Incentivization Strategies

Incentives can be a powerful tool in reactivating dormant users, but their effectiveness varies widely among different user segments. AI excels in identifying the types of incentives that are most likely to motivate specific segments of dormant users to re-engage with the product.

For some, access to premium features might be the key; for others, a discount or a promotional offer might prove more enticing. AI can also help in determining the appropriate scale and timing of these incentives, ensuring that they are both cost-effective for the business and impactful enough to motivate users to return.

Best Practices For AI-Powered Dormant User Re-engagement in PLG: Overcoming Challenges With Quest Labs

While Artificial Intelligence (AI) offers transformative potential for identifying and re-engaging dormant users within Product-Led Growth (PLG) strategies, navigating the complexities of its application requires careful consideration.

Addressing Data Privacy and Ethical Considerations

The use of AI to analyze user behavior and engagement patterns necessitates the handling of vast amounts of personal data.

To navigate these concerns, Quest Labs offers :

• Ensure Transparency:

  It‘s important to clearly communicate with users about collecting data and its usage. With transparency, you can help users feel more comfortable with AI analyzing their data.

• Adhere to Regulations:

  Ensure data usage complies with data laws such as GDPR and CCPA in California. These laws allow ethical data use and commit to user privacy.

• Implement Ethical AI Practices:

  Develop and follow guidelines for ethical AI use that go beyond mere legal compliance. Ensure the process is free from biases that could lead to unfair treatment of user groups.

Ensuring the Accuracy of AI Predictions

The effectiveness of using AI to engage dormant users depends on the accuracy of its predictions. If the predictions are irrelevant, it can lead to further disengagement.

To mitigate these risks, Quest Labs ensures:

• Continuously Train AI Models:

  AI algorithms thrive on data. Updating these models with new data ensures that the predictions remain accurate. Moreover, it reflects the behaviors and preferences of current users.

• Test Engagement Strategies:

  AI can predict the most effective engagement strategies, but testing is invaluable. Testing various approaches allows businesses to refine their strategy efficiently.

• Human Involvement:

  Involving human agents in creating engagement strategies ensures better understanding.

Conclusion

By harnessing AI, businesses can achieve a nuanced understanding of user behavior, predict dormancy before it fully takes root, and tailor re-engagement strategies with unparalleled precision. We've seen how AI-driven techniques easily identify dormant users in PLG. It also enables the crafting of personalized re-engagement campaigns.

Encouraging the adoption of AI in PLG strategies with Quest Labs is not merely a suggestion—it is a call to action for businesses aiming to thrive in the digital age. The journey of integrating AI into PLG is one of exploration, innovation, and continuous learning with us. By embracing AI, Quest Labs help unlock new levels of user understanding, engagement, and satisfaction, propelling their growth and ensuring their place at the forefront of their industries.

FAQs

1. How to increase active users?

You can increase app users through these strategies.

• Personalize user experience
• Offer relevant offers
• Gamification to maximize engagement
• Simplify onboarding

2. Who are dormant users?

Dormant users are the users of your app who were active at first but are not using it anymore. They might be inactive due to your app's bad user experience or technical issues.

3. How do I find an inactive user?

AI user segmentation is an effective solution for identifying and categorizing dormant users based on engagement levels.

4. How do I reactivate dormant users?

You can effectively reactivate dormant users by understanding and fixing the causes for their lack of engagement. It's also essential to develop engagement strategies for each segment.