AI Strategies For Improving Personalization At Subscription Companies

Vasudeva Akula, VOZIQ AI cofounder and head of data science. Helps recurring revenue businesses improve customer retention using ML.

According to McKinsey, companies that excel at personalization generate 40% more revenue than those that are only average at their personalization efforts. For subscription businesses, in particular, the more personalized the campaigns, the greater the improvements in customer lifetime value (CLV).

AI can help to achieve this goal by extracting deeper customer-level insights and offering better predictions about what’s driving dissatisfaction. In my conversations with subscription leaders, though, I’ve heard that, despite their best efforts, many are struggling to build accurate, actionable intelligence for operational decision-making. The common challenges they face include:

• Customer data is often fragmented across departments. Even when unified, it lacks the depth needed to fully predict customer behavior.

• Many subscription businesses use a single analytics model that can identify at-risk customers but fails to uncover the reasons behind the risk of cancellation.

• Even those adopting AI for personalization are struggling to make it actionable.

Drawing from my experience helping subscription businesses achieve CLV breakthroughs using AI, here are four AI-powered strategies that can help to achieve better results with personalization efforts:

1. Enable multi-layered analysis of every customer.

The success of AI-driven personalized campaigns relies on the accuracy of the models in predicting individual customer behavior. The more relevant data you provide, the more precise these predictions become.

By combining structured and unstructured internal data (such as billing details, demographics, transactions, agent notes, service history, location and contract information) with third-party enrichments (like income data, demographics, property values, home size data and geographical data about city and neighborhood)‚ you can enable multi-layered analysis, which in turn helps to create accurate customer-level intelligence.

Many businesses already capture these data points but struggle to analyze them to create accurate and actionable customer-level intelligence.

2. Deploy a multiple predictive model approach.

Many subscription leaders rely on a single predictive model that targets a specific metric such as customer retention rate. They believe, for example, that generating risk predictions is sufficient. However, these predictions often lack actionability and explainability, making it challenging to drive the necessary actions.

That’s why I recommend using multiple explainable AI models, each tailored to predict different facets of customer behavior and risk. This approach can improve prediction accuracy while also uncovering the underlying reasons—such as when and why a customer might cancel, why a particular customer is high-value and which customers are ideal for price increases, winbacks, upgrades and referrals.

Additionally, these models can often recommend optimal offers to maximize conversions.

3. Create actionable customer micro-segments.

While a single AI model can also segment customers, using multiple models can help to enhance the actionability of these segments.

By analyzing the customer-level data and risk patterns, models can assign predictive risk scores to the entire customer base and microsegment customers not only by churn risk but also by root causes, hidden CLV growth opportunities, recommended offers and customer sentiment.

This approach can help ensure that, when you review each segment, you clearly understand why customers are placed in specific segments and how to integrate this intelligence with marketing channels to drive hyper-personalization at scale.

4. Operationalize intelligence at multiple customer touchpoints.

By leveraging predictions and microsegments, organizations can create holistic, predictive customer profiles. This enables them to run personalized, automated campaigns across all customer-facing channels and optimize their outreach efforts.

However, I recommend initially deploying AI in a single channel—such as the call center—to tune the models, manage changes and evaluate the ROI of the AI initiative. This strategy can help organizations to build a solid foundation before embedding the intelligence into other customer-facing channels.

Conclusion

AI is a game changer for driving personalization and maintaining a satisfied customer base, particularly for subscription businesses. However, implementing AI is often more challenging than anticipated, which is why many businesses struggle to take their AI projects into production.

By systematically applying these four personalization strategies, subscription businesses will be more likely to successfully kickstart AI-driven transformation by proactively renewing their customers with targeted rate adjustments or service upgrades and, ultimately, improving enterprise value.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?