Why propensity modeling is dead: Embracing AI optimization for superior customer acquisition & retention

Understanding customer behavior is the key to successful acquisition and retention strategies. Traditionally, businesses have relied on propensity modeling — a statistical approach to predict customer behavior based on historical data. However, with the advent of advanced technologies, propensity modeling is quickly becoming obsolete. Today, AI-powered optimization has emerged as the superior tool for customer acquisition and retention, offering unparalleled accuracy and adaptability. 

According to McKinsey’s 2021 global survey on the state of AI, 27% of respondents reported at least 5% of earnings before interest and taxes (EBIT) attributable to AI. More importantly, the survey found that AI adoption is highest in the business functions of service operations, product and service development, and marketing and sales. This demonstrates the growing importance and effectiveness of AI in customer-centric business areas, highlighting the need for more advanced analytical tools like AI optimization.

With that in mind, this article will explore why propensity modeling is dead and how AI optimization is revolutionizing customer behavior analysis and analytics.

The limitations of propensity modeling

Propensity modeling has long been the go-to method for predicting customer behavior. By analyzing historical data, such as past purchases and interaction history, businesses could estimate the likelihood of a customer engaging in a specific action, like making a purchase or churning. While this approach has its merits, it is increasingly falling short in today’s dynamic and data-rich environment.

• Static nature of propensity models: Propensity models are inherently static, relying on historical data to make predictions. This means they often fail to account for the fluid and evolving nature of customer behavior. In a world where customer preferences and behaviors can change rapidly due to various external factors, static models can lead to inaccurate predictions and suboptimal marketing strategies.
• Limited data integration: Propensity models traditionally rely on a limited set of data points, primarily focusing on transactional and demographic data. However, today’s customer behavior is influenced by a broader range of factors, including social media interactions, real-time browsing behavior, and even sentiment analysis. Propensity models struggle to integrate these diverse data sources, resulting in an incomplete picture of customer behavior.
• Inflexibility in real-time scenarios: Customers expect personalized experiences in real time. Propensity models, which are typically recalculated periodically, cannot adapt quickly to real-time data, making them less effective in scenarios that require immediate action. This lack of real-time adaptability can lead to missed opportunities in engaging customers at critical moments.

The rise of AI optimization in customer behavior analytics

As businesses seek to overcome the limitations of propensity modeling, AI optimization has emerged as a powerful alternative. AI-powered tools are transforming customer behavior analysis by leveraging machine learning algorithms to analyze vast amounts of data in real time. Here’s how AI optimization is changing the game for customer acquisition and retention:

• Dynamic and adaptive modeling: Unlike propensity models, AI optimization is dynamic and continuously learns from new data inputs. Machine learning algorithms can adjust to changing customer behaviors and preferences, providing businesses with real-time insights. This adaptability ensures that predictions are always up-to-date, allowing for more accurate and effective marketing strategies.
• Comprehensive data integration: AI-powered optimization tools can analyze data from a wide range of sources, including transactional data, social media interactions, real-time browsing behavior, and even unstructured data like customer reviews and sentiment analysis. By integrating these diverse data sources, AI provides a holistic view of customer behavior, enabling more precise targeting and personalization.
• Real-time decision making: One of the most significant advantages of AI optimization is its ability to operate in real time. AI algorithms can process vast amounts of data and make predictions on the fly, allowing businesses to engage customers at the right moment with the right message. This real-time capability is particularly crucial in today’s fast-paced digital environment, where customer expectations are higher than ever.

How AI optimization enhances customer acquisition

Customer acquisition has always been a challenge for businesses, particularly in competitive markets. AI optimization enhances acquisition strategies by providing deeper insights into potential customers and enabling more effective targeting.

• Predictive customer profiling: AI-powered analytics can create detailed profiles of potential customers by analyzing a wide array of data points, from browsing history to social media activity. These profiles go beyond traditional demographic data, offering insights into individual preferences, interests, and even predicted future behavior. This level of detail allows businesses to target potential customers with highly personalized offers, increasing the likelihood of conversion. In the media sector, AI-powered tools have empowered publishers to eliminate A/B testing and move beyond propensity scoring with a new take on paywalls that could save the news industry.
• Optimized marketing campaigns: AI can optimize marketing campaigns by analyzing past performance data and predicting the success of different strategies. For example, AI can determine the best time to send marketing emails, the most effective messaging, and the ideal channels for reaching specific customer segments. This optimization ensures that marketing efforts are not only targeted but also cost-effective, maximizing ROI.
• Enhanced lead scoring: Traditional lead scoring models often rely on limited criteria, such as job title or company size. AI optimization, however, can incorporate a broader range of factors, including behavioral data and real-time interactions, to score leads more accurately. This improved lead scoring helps sales teams prioritize their efforts, focusing on leads that are most likely to convert.

How AI optimization drives customer retention

Retaining customers is just as important as acquiring them, if not more so. AI optimization offers several advantages for improving customer retention by enabling businesses to better understand and respond to customer needs.

• Churn prediction: AI algorithms can predict customer churn by analyzing patterns in customer behavior, such as a decline in engagement or a shift in purchasing habits. By identifying at-risk customers early, businesses can take proactive measures to retain them, such as offering personalized incentives or addressing specific pain points.
• Personalized customer experiences: Today’s customers expect personalized experiences that cater to their individual needs and preferences. AI optimization enables businesses to deliver this level of personalization by analyzing customer behavior in real time and tailoring interactions accordingly. Whether it’s personalized product recommendations, targeted promotions, or customized content, AI helps businesses create experiences that resonate with customers, increasing loyalty and reducing churn.
• Continuous customer feedback loop: AI-powered analytics can also facilitate a continuous feedback loop, where customer interactions and feedback are constantly analyzed to improve products and services. This ongoing process ensures that businesses are always aligned with customer expectations, leading to higher satisfaction and retention rates.

The future of customer behavior analytics

As AI optimization continues to evolve, it will further transform customer behavior analytics, making traditional methods like propensity modeling increasingly obsolete. Businesses that embrace AI-driven strategies will be better equipped to navigate the complexities of modern consumer behavior, leading to more successful acquisition and retention efforts.

• AI-powered predictive analytics: The future of customer behavior analytics lies in AI-powered predictive analytics, where machine learning models not only analyze past behavior but also predict future actions with high accuracy. This capability will enable businesses to stay ahead of customer needs and make proactive decisions that enhance the customer journey.
• Integration with other emerging technologies: AI optimization will increasingly integrate with other emerging technologies, such as IoT and blockchain, to provide even deeper insights into customer behavior. For example, IoT devices can offer real-time data on how customers interact with products, while blockchain can enhance data security and transparency, further improving customer trust and engagement.
• Ethical AI and customer trust: As AI plays a larger role in customer behavior analytics, businesses must also consider the ethical implications of data usage. Ensuring transparency, privacy, and fairness in AI-driven strategies will be crucial for maintaining customer trust and fostering long-term relationships.

The shift from propensity modeling to AI optimization

Propensity modeling, once a staple of customer behavior analysis, is becoming increasingly irrelevant in the face of rapid technological advancements. AI optimization offers a more dynamic, comprehensive, and real-time approach to understanding and influencing customer behavior. Back in 2020, Gartner found early evidence for this in a study that showed 63% of digital marketing leaders were still struggling with personalization even with better technology for modeling. At the time, only 17% of those surveyed were using AI and machine learning across the function, meaning the majority were facing their challenges with older audience modeling techniques.

As time and technology have progressed, AI has proven its worth, with many more businesses adopting some form of AI optimization. From here, the trajectory is clear: AI-powered tools will become increasingly central to business success. By fully embracing these advanced technologies, companies can revolutionize their acquisition and retention strategies, delivering personalized experiences that resonate with customers and drive sustainable growth.

In this new era of marketing, the ability to adapt quickly to changing customer behaviors and preferences is paramount. AI optimization not only meets this challenge but also sets the stage for future innovations in customer behavior analytics. As businesses continue to embrace AI, those that move away from outdated propensity models and fully harness the power of AI optimization will lead the way in customer acquisition and retention.