Feature
The Gist
- Causal AI and deeper marketing insights. Go beyond simple correlation analysis to uncover the cause-and-effect relationships between marketing efforts and outcomes.
- ChatGPT won’t get you there. There’s something even more powerful than generative AI, and it's called causal AI.
- Stronger together. Marketers are using generative and causal AI in tandem to create, test and validate multiple hypotheses with higher accuracy.
What’s the link between ice cream sales and crime rates?
Let’s say an overzealous rookie reporter finds that ice cream sales and crime rates tend to increase and decrease together. This can suggest a correlation between them. But it certainly doesn’t mean that ice cream causes crime or vice versa.
The true cause of both phenomena is likely the weather, which affects people’s preferences and behavior. When it’s hot, people are more likely to buy ice cream — and also more likely to commit crimes — due to factors like summer vacations, empty homes, and possibly higher levels of irritability and boredom. When it’s cold, the opposite happens.
Therefore, ice cream sales and crime rates are correlated, but not causally related. (I’m oversimplifying, but you get the idea.)
Now, extrapolate this to a marketing data set of a large business. There are millions of data points. While the current machine learning models like large language models (LLMs) can identify patterns and predict the next most likely action, they cannot explain the causes behind their outputs. That’s the reason generative AI makes mistakes based on incorrect assumptions and even invents “facts” out of thin air.
In this article, we will explore how utilizing causal AI can provide deeper marketing insights.
Related Article: Where Are Marketers on the Generative AI Adoption Curve?
Life Beyond Generative AI: What Causal AI Can Do
The next-gen of AI (Wait, what? We’ve barely even wrapped our heads around this one!) is causal AI, which helps us understand relationships between variables and outcomes in complex systems.
Causal AI is a subset or an approach within “causal Inference” — an area that focuses on understanding the cause-and-effect relationship of independent variables within a larger system.
For instance, marketers can use causal AI to get deeper marketing insights and analyze complex data sets to identify causal relationships between marketing activities and their impact on key performance indicators (KPIs), said Tyler Foster, chief technology officer and co-founder at AI-powered personalization platform Evolv AI.
By simulating multiple counterfactual scenarios, marketers can estimate what would have happened if specific marketing actions had or hadn't been taken. By comparing the actual outcomes with these counterfactuals, it becomes possible to attribute the true impact of each marketing channel or campaign.
More simply put, said Foster, it lets us explore “What if …?” in the marketing world. What if we had only run Campaign A instead of Campaigns A and B? What if we had only targeted Group A.b instead of all of Group A? What if we spend an extra $5,000 on TikTok instead of Instagram? How many additional conversions would that deliver? In other words, It lets us go beyond predictive accuracy and get insights into the incrementality of our marketing dollars.
Causal AI can help estimate the impact of specific interventions on a marketing campaign and get deeper insights by explaining the causes and effects between them.
Here are two examples to highlight the distinct capabilities of generative and causal AI in the marketing context.
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Generative AI can identify which promotion increased sales and create new promotions based on existing ones. Causal AI can tell a marketer why a certain promotion increased sales (what were the trigger factors?)
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Generative AI can show marketers how a price change would affect the demand curve, while causal AI can tell a marketer how much incremental revenue they can expect by changing the price of a product.
According to the World Economic Forum, causal AI is more robust, transparent, fair, accountable and contestable than black-box AI approaches, which also makes it more aligned with the emerging AI regulations.
Related Article: Transforming Ecommerce With Artificial Intelligence & Machine Learning
How Causal AI Provides Crucial Marketing Insights & Enhances Data-Led Decision-Making
While ChatGPT and Midjourney will continue to dazzle and disrupt areas like content creation and advertising, causal AI shifts our focus to another strategic (and problematic) area of marketing: measuring and optimizing performance.
Ariel Geifman, co-founder and chief revenue officer at Dealtale, a leader in causal AI for marketers, stressed the necessity of separating correlation and causation when it comes to KPIs. As marketers, he said, it’s easy to get swept up in the excitement of a creative campaign, especially when you see a spike in a key metric like website traffic following a launch.
“But at the back of our heads, we know that a brilliant marketing campaign isn’t always the direct cause of an uptick in web visitors. Measuring a true ROI on a marketing campaign relies on being able to look at the causation, not just the correlation — especially when you have multiple customer touchpoints to consider. That type of evaluation can make even the most daunting KPIs predictable and repeatable, ultimately informing business decisions.”
Causal AI solves the chicken-and-egg problem of traditional AI, Geifman added. In traditional AI, you find correlations like a set of coupons being linked to an increase in sales. However, you might be giving coupons to people who would buy anyway. Causation allows us to understand whether people would buy without coupons and whether the coupons actually made them buy.
In B2B marketing, causal AI could help marketers understand how to allocate marketing resources better for deeper insights and decision-making. For example, whether we should send sales development to chase leads, or would they convert on their own; does sending another email have an incremental effect on conversion, etc.
Generative AI can impact productivity and performance with patterns and predictions (Thanks, Pippi Pepennopolis!). Causality can drive incrementality by optimizing our market mix modeling.
When it comes to ROI, Foster added that causal AI can look at every campaign or touchpoint the person came in contact with to determine how each impacts the observed outcomes or conversions. That is very hard, if impossible, for a human.
He does caution, however, that it has its limitations. Most casual AI or inference applications are used in the analysis of existing datasets, which can lead to both false-positive (type 1) and false-negative (type 2) errors. With that in mind, he suggests, causal AI is primarily helpful in forming hypotheses to test in experimentation and validate, rather than drawing specific conclusions. “You can use causal models to isolate potential ROI, but it needs to be paired with experimentation. That’s a big data usability gap that impacts most BI efforts. All the data in the world means nothing unless you can discover patterns that lead to testable hypotheses. Causal AI can be a powerful tool in uncovering insights in a sea of data that traditional approaches couldn’t surface.”
By making accurate predictions with causation and finding patterns through correlation, said Vivek Sharma, CEO & co-founder of content personalization platform Movable Ink, we can more accurately anticipate what the customer will appreciate, based on trigger factors, and eventually impact behavioral change.
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For instance, if a retailer can better understand why their customer made a decision by identifying the multiple facets that drove customer behavior, they can then figure out the best way to influence future behavior, build more personalized experiences and impact stronger business outcomes. And like with all AI, causal AI gets smarter with each interaction and can potentially help marketers dig into deeper insights.
Stronger Together: Generative AI and Causal AI Work Best in Tandem
Generative AI has already to a large extent democratized analytics by giving more people the ability to analyze data quickly and easily, create visual reports, and uncover essential patterns, said Sharma. Geifman, too, points to the emerging popularity of “AI analysts or AI butlers,” which allow marketers to interact with and query the data, create analysis and share dashboards — all using plain English.
With Generative AI’s ability to create new data based on the data it trains on, it can create text, images, videos, code, etc., based on the inferences produced through causal AI.
For instance, said Foster, his team is using causal AI to form hypotheses, and generative AI to create prototypes for experiments to validate the hypotheses. Paired, they can understand user behavior, form hypotheses and validate them to draw conclusions without requiring human intervention.
Ultimately, evaluating multichannel marketing campaigns with causal and generative AI provides a deeper understanding of marketing effectiveness, allows for better resource allocation, and helps maximize ROI by focusing efforts on the channels and tactics that have a proven causal impact on desired outcomes.
