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Causal AI for tomorrow's Digital Agency

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.

Top of mind for client teams

  1. Which channels and messages are influencing our customers, and which are not cutting through?
  2. How should I allocate my marketing budget when customer behavior and preferences are radically shifting?
  3. What are the causes of churn and how can I predict retention?
  4. How can I achieve greater marketing impact with a reduced budget?

Ways Causal AI is revolutionizing marketing decision making

  • Enables the identification of true causal drivers of churn
  • Receive recommendations for interventions to optimally allocate resources and budgets to increase retention
  • Reduce churn by an extra 4-9% above standard machine learning-based churn prevention models
  • Generate an causal attribution model that is a true representation of today’s complex world
  • Measure and optimize campaign performance with the latest ML optimization models
  • Run experiments across channels, at scale
  • Understand what is driving your customers’ behavior to achieve more with less
  • Generate an causal attribution model that is a true representation of today’s complex world
  • Measure and optimize budget allocation and campaign performance with the latest ML optimization models
  • Scenario planning together with a causal model, prepare for and understand more deeply how events and or changes in the market will impact your clients KPIs
  • Understand what is driving your clients’ customers’ behavior to achieve more with less

What is Causal AI?

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Decision-making AI

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™).

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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.

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Explainable AI

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.

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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”.

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Adaptable AI

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. 

Discover the causal drivers of churn to drive retention

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.

Zero in on the most impactful optimizations by understanding cause-and-effect

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.