While causal AI requires high-quality data, computing power, and skilled human interpretation, its benefits outweigh these challenges.
Imagine you run an online store specializing in athletic shoes. Traditional data analytics show that many of your customers also buy sports equipment like free weights, action cameras, and fitness trackers. You assume customers buy your shoes because they are fitness enthusiasts, so you launch a marketing campaign promoting your shoes alongside these items. Yet, after six months, the campaign fails to boost sales. Disappointed, you research alternative methods and processes to eliminate the guesswork of creating effective offers and loyalty programs.
While generative AI has certainly accelerated some marketing use cases, it’s still a novel technology that is continuously evolving. For example, generative AI can uncover new personas and build creative, targeted content and campaigns within minutes. It’s highly effective in augmenting and accelerating traditional methods of marketing segmentation. However, it does not understand the cause-and-effect relationships and the “why” behind customer behavior. This differentiation is key in how causal AI helps retailers set a new standard for customer engagement.
Instead of looking for correlations and patterns, when causal AI analyzes data, it looks for clear evidence of causality. Using the example above, the retailer could uncover exactly which customers buy their shoes because they are dedicated runners, not because they are general fitness enthusiasts. Understanding the correlation allowed them to adjust their strategy to focus on the running community only. By drilling down to individual motivations within that group, the retailer could build targeted ads identifying marathon runners versus sprinters and treadmill runners versus trail. They can offer promotions on running-related gear customized to each person’s interests and inclinations.
Loyalty teams are also embracing causal AI to maximize customer retention and keep pace with the dynamic nature of retail. Causal AI moves past the limitations of traditional AI to deliver capabilities that are far more powerful, penetrating, and useful to retailers.
Correlation to causation: AI’s shift from probability to certainty
Sure, traditional AI can reveal a lot. It can identify customers who use a particular mobile app and spend more money. But it can’t tell why people do certain things. It’s vital to know whether the app is causing an increase in spending or is just a coincidence.
Armed with these answers, retailers can design and implement their marketing campaigns. Accurate analysis leads to highly effective marketing efforts and loyalty programs. Unlike traditional AI, causal AI delivers stunningly accurate analysis, not just correlations, and knows why something is happening, not just how.
Insights that are accurate and actionable
Another key differentiator is that traditional AI feeds on historical data, assuming that the past will repeat itself sooner or later. However, retail environments change rapidly, and established trends break down and reconfigure. The result is that traditional AI, relying on established models, often fails to deliver the insights retailers expect. It has trouble recognizing and adapting to change. By contrast, causal AI relies heavily on counterfactual analysis, which is superior in almost every way to analyze past customer behavior to predict future behavior.
Causal AI can conclude the impact of free shipping on each individual and those who increase their spending. For loyalty teams, this is marketing gold. It provides the power to create and test various “what if?” scenarios and explore the likely results of different marketing interventions before funding and implementing them. If causal AI can tell you that free shipping is (or isn’t) helping increase spending and for whom, that answer can have enormous benefits in maximizing a business’s time, energy, and resources. More specifically, causal AI overcomes the limitations of traditional Al by helping retailers:
- Use resources more efficiently: Traditional AI often recommends overly broad and inefficient interventions that waste resources. Alternatively, causal AI offers narrowly focused, specific solutions, separating factors shaping customer behavior from mere associations, leading to robust returns.
- Increase personalization: Causal AI enables deeper personalization by understanding individual motivations. It can isolate specific causal chains, honing in on customer preferences and designing targeted interventions. For example, a bookstore can personalize reading recommendations for each customer, leading to more frequent visits and increased spending.
- Minimize bias and quickly adjust for confounding factors: Traditional AI is vulnerable to biases and confounding factors in historical data. Causal AI addresses these distortions directly, providing more reliable insights and better decision-making. It can identify when relationships are correlational rather than causal, avoiding costly targeting and resource allocation mistakes.
- Promote flexibility and ongoing learning: Causal AI autonomously adapts to changing conditions, continuously learning and adjusting to new data. This allows loyalty teams to respond effectively to shifting customer attitudes and behaviors.
Causal AI can also be applied to loyalty programs in various ways, including assigning specific rewards to each customer, real-time analysis of marketing campaigns, and optimizing individual customer journeys. Thanks to targeted interventions suggested by causal AI, a major global retailer focused on delivering personalized offers and rewards that resulted in a 25% increase in active members, a 30% drop in churn, and a 20% increase in ROI.
Challenges and future outlook
While causal AI requires high-quality data, computing power, and skilled human interpretation, its benefits outweigh these challenges. As it evolves, causal AI is expected to shape loyalty marketing, which will become increasingly sophisticated — potentially integrating with IoT and machine learning for even greater impact.
The technology provides retailers unprecedented opportunities to understand, predict, and shape customer behavior, which is becoming crucial for retailers’ survival and success in a shifting landscape.
Companies that rely on casual AI to deliver the right experience to ” the right customer at the right moment for the right price, promotion or markdown are the ones that are poised to come out on top.
