The Attribution Dilemma
7 min read
Marketers are frustrated with current AI technology, which is limited to predicting customer behaviors. Causal AI goes beyond predictions, generating insights into what drives customer behavior. Digital agency teams are leveraging these insights to take a strategic seat at the table with their clients.
Causal AI doesn’t just predict the future, it shapes it.
Current AI is limited to making predictions. However, forecasting accounts for a small fraction of the value chain in enterprise AI. The true potential of AI lies with empowering humans to make better decisions. Causal AI autonomously finds interventions that achieve a given strategic goal or that maximize a KPI (autoKPI™).
Consider a telecommunications provider trying to reduce customer churn. Conventional machine learning systems just attempt to predict likely churners. Causal AI recommends the most effective interventions (sales outreach, targeted advertising, price discounts) and the most responsive customer segments that minimize churn. It also factors in the telco’s business model and goals.
Read more on how Causal AI promotes optimal decision-making.
Put the “cause” in “because” with next-generation explainable AI.
If AI is to meet basic business-use, legal and ethical needs, it must be explainable. However, machine learning models are black boxes, and attempts to explain them aren’t suitable for non-technical stakeholders. Causal AI builds human-friendly glass-box models. Humans can scrutinize and alter the assumptions behind models before they are deployed.
Take an AI model used by a bank to approve lending decisions. Causal AI reveals why an applicant might be denied credit and allows the bank to audit the assumptions the model is making. Explanations can be generated before the model is fully trained, reinforcing trust in the model in deployment.
Find out more about how causaLens puts the “cause” in “because”.
Causal AI continuously adapts to real-world dynamics.
87% of machine learning projects are terminated during an experimental phase. The remainder that make it into production are prone to fail as the world changes. This is because current AI systems are not suited to real-world dynamics. Causal AI is robust to changing conditions because it learns invariant causal relationships in data that hold across different contexts.
We’ve integrated world class Causal AI capabilities into our Customer Retention Decision App. Our simple to use and easily understandable application draws on next generation explainability, machine imagination, and intervention design.
Without a more nuanced approach at attribution models, clients are running blind to the effectiveness of their initiatives. Which makes finding ways to tweak campaigns for the most impact, all the more challenging. Causal marketing attribution powers a new, more powerful method of marketing mix optimization.