At a glance
- There are profound challenges with successfully deploying AI to optimize marketing budget-allocation including a widespread mistrust of the technology among 3 in 4 marketing teams piloting machine learning.
- Causal AI delivers on the promise of AI for marketing. It runs experiments at scale to probe which channels and messages are driving key performance indicators (KPIs), and makes precision recommendations with humans-in-the-loop.
Top of mind for marketers
- Which channels and messages are influencing our customers, and which are not cutting through?
- How should I allocate my marketing budget when customer behavior and preferences are radically shifting?
- What new approaches are required to measure and optimize campaign performance in a privacy-first, cookieless, world?
- How can I achieve greater marketing impact with a reduced budget?
Traditional attribution models are no longer sufficient at representing the complex, noisy and interconnected world we live in today.
The challenge of marketing attribution
Marketing attribution is a cause-and-effect challenge. In essence, marketers need to know the cause of a customer’s purchase (the “why” behind the buying decision) in order to attribute credit to a given channel. And they need to be able to anticipate the effect of a budget allocation decision on sales in order to make the most of their marketing efforts.
Heuristics like last-touch attribution have serious and obvious flaws. They ignore the entirety of the customer’s journey — in fact, a typical retailer engages with the average customer across 56 touchpoints. Last-touch assumes all this marketing effort is irrelevant.
Similarly, other attribution models such as first-touch, u shape, or linear, fail to correctly assign the correct responsibility of each channel for the sale. They all apply a one-size-fits-all model for every customer.
The essence of marketing attribution: causality
Marketing attribution really involves two problems — one is about understanding the past, the other about shaping the future.
Estimating true marketing ROI for a channel requires us to look into the past and understand the cause of a customer’s purchase decision. We need to evaluate the incremental financial value of the channel — how the marketing investment in that channel impacted the buying decision, if at all. To do that requires travelling to an alternative — “counterfactual” — world, in which we didn’t do any marketing via that channel.
There is also a challenge of shaping the future. Understanding where to allocate our capital requires us to peek into the future and assess which messages and channels will drive the best sales outcomes going forward. This is again a causal problem, in which we need to understand the impact of performing an “intervention” in the system.
Marketers need to be able to answer counterfactual and interventional queries at scale and at pace.
So how can marketers answer these causal questions, transform their marketing attribution, and optimize their marketing dollars?
Causal AI models
Causal models are at the heart of Causal AI. A causal model maps out all the qualitative cause-and-effect relationships in a system, combined with quantitative information about the strengths of those relationships.
Reimagine Marketing Attribution with Trustworthy AI
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