AI testing is heralded as the next big advancement in marketing, a field already saturated with buzzwords like "personalization" and "AI." In the swirl of industry hype, marketers are on the lookout for straightforward explanations and concrete value propositions.
One prevalent approach to using AI in marketing is through what's often termed "next best action" (NBA) models. At first glance, NBA models promise a straightforward benefit: they suggest the most effective subsequent step with each customer. However, the term "next best action" is not precise and is rarely used by data scientists or machine learning experts. It's important for marketers to comprehend the actual functionality and the type of personalization these models provide.
Deciphering the Best Action
Conceptually, a NBA model might appear overly simplistic. For instance, a bank might use a rule like, “If a customer opens a checking account, offer them a savings account next.” Such simplistic rules aren't genuinely representative of AI, and perhaps don't merit the label “next best action.”
In reality, marketers employ various machine learning (ML) techniques to address the NBA challenge:
- Predictive Models:
- The standard approach involves deploying predictive models using supervised learning. For instance, a model might evaluate historical data and customer profiles to forecast the likelihood of a customer purchasing a specific product. These models are especially useful in sectors like financial services with fewer product lines.
- However, these models have inherent limitations. If a model predicts a customer is likely to buy a specific product based on historical data, it doesn't necessarily mean it's the best product to promote. Market dynamics might shift customer preferences, or higher-margin products could be more appealing if introduced appropriately.
- Collaborative Filtering:
- This method is more suited for industries with a vast array of products, like retail or streaming services. Collaborative filtering analyzes sparse data sets to identify patterns and recommend products or content that a customer is likely to enjoy based on similarities with others.
- Like predictive models, collaborative filtering relies heavily on historical data and lacks the capability to explore potentially better options that have not yet been tried.
- AI Testing with Reinforcement Learning:
- Unlike methods that depend solely on historical data, AI testing incorporates reinforcement learning, which allows for experimentation and learning from each customer interaction. This approach doesn't just replicate past successes; it explores and empirically discovers what might work best in individual cases, potentially uncovering more lucrative or engaging options that haven't been considered before.
The Broader Implications of "Next"
When marketers discuss an "action," they typically refer to a product offer or promotion. However, determining the most suitable action involves more than just selecting a product; it also includes deciding the optimal timing, channel, frequency, and even the creative aspects of how the offer is presented. These factors are crucial, as the effectiveness of an offer can diminish if it's not delivered in the right manner or at the right time.
Why AI Testing Represents Next Best Everything
AI testing, exemplified by platforms like OfferFit, transcends traditional NBA by considering every variable in customer interaction—from the channel and timing to the frequency and creative presentation. This approach doesn't just adapt to past behaviors; it dynamically explores and identifies the most effective strategies for each individual, making it a comprehensive solution that embodies the concept of "next best everything."
In essence, AI testing doesn't only determine the next best action—it seeks the next best everything, optimizing every aspect of the marketing strategy in a deeply personalized, data-driven manner.
