AI has long been a topic of discussion, but for marketers, it's practically routine. For years, enterprise marketers have leveraged AI to craft personalized experiences. Significant strides have been made in amassing data within centralized warehouses or Customer Data Platforms (CDPs), creating comprehensive customer profiles that amalgamate engagement, purchase history, and marketing interactions. This wealth of centralized data has laid a fertile foundation for machine learning (ML) models to deliver personalized experiences. However, the methodologies and best practices in this realm are evolving—and not just due to tools like ChatGPT. Here, we explore how marketers are currently employing ML and how advancements are redefining best practices.
Traditional Approaches to ML in Marketing
Traditionally, marketers have employed ML for personalization by utilizing predictive models followed by segment-specific multivariate testing. Initially, marketers define segments based on several key customer attributes such as:
- Average value of the last 10 purchases.
- Recency and frequency of purchases.
- Likelihood of repeat purchases.
- Risk of churn.
- Predicted customer lifetime value.
The simpler attributes can be directly calculated from existing data, not requiring sophisticated ML techniques. However, more complex characteristics like churn risk or repurchase likelihood are estimated using supervised learning—a type of ML. For instance, a churn prediction model might assign a churn likelihood score (from 0 to 100) to each customer. Marketers then segment these scores into groups (e.g., quintiles or deciles), allowing them to manage hundreds of microsegments based on several key attributes.
Though innovative, simultaneous A/B testing across extensive segments such as these (potentially numbering in the hundreds) is impractical. Instead, businesses use multivariate testing to discern the most effective strategies for each segment, subsequently crafting rules for actions like product recommendations or timing for promotional outreach. While this method was considered cutting-edge a few years ago, it has its limitations.
Limitations of Predictive Models
Predictive models, by nature, are static and become less accurate as market conditions and consumer behaviors evolve. For example, a marketer might devise a new promotional strategy that wasn't included in previous tests, making existing models and business rules less effective or even obsolete.
Businesses face the dilemma of either continually updating their models with new data and retraining them or risking inaccurate predictions from outdated models. Predictive models are inherently rigid, performing well with data similar to what they were originally trained on but faltering with new, unanticipated scenarios.
Moreover, even if businesses invest in continuously updating and retraining models, the strategies derived from multivariate tests become quickly outdated as market dynamics shift. The choice businesses face—either continually invest resources in updates and retests or stick with potentially ineffective outdated models—is far from ideal.
The Shortcomings of Rule-Based Personalization
Despite the efforts to update models and rules, traditional "segments and rules" based personalization falls short of truly individualized customer experiences. Microsegmenting might create the illusion of personalization with detailed test results suggesting optimal engagement strategies for each segment, but this approach often misses the mark. It effectively imposes a majority-rules scenario where potentially a large fraction of the segment does not engage as predicted, undermining the effectiveness of the campaign.
Advancing to AI-Driven Personalization with Reinforcement Learning
The new frontier for personalization in marketing is the application of reinforcement learning (RL), an ML methodology that adapts continuously and learns from each interaction. RL transcends the limitations of predictive models and rigid segments, allowing for truly individualized marketing decisions that consider all available data on customer behaviors and preferences.
RL models dynamically adjust and learn, accommodating new customer interactions or changes in behavior without the need for constant recalibration. This enables marketers to make precise, individualized decisions across all touchpoints—delivering the right message, through the right channel, at the right time, tailored to each customer.
Transitioning from rule-based to AI-driven personalization with RL not only streamlines decision-making processes but also enhances the accuracy and relevance of marketing efforts—ushering in a new era of state-of-the-art marketing personalization.
