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Media & Entertainment

Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the biggest challenges in Media & Entertainment across marketing, branding, customer journey & pricing.

Traditional machine learning approaches often fail to address critical business questions

Correlation, not causation

Spurious correlations lead to bad decisions


Read more on our blog:
How can AI discover cause and effect

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They struggle to answer why

And are often perceived as “black boxes”


Read more on our blog:
Explainable AI (XAI) doesn’t explain enough

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Prediction, not next best action

For example, they can predict if a customer will churn or not but can’t recommend the optimal next best action to retain the customer

Read more on our blog:
From predicting to Influencing

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Do these questions sound familiar?

Media & Entertainment run on causal questions

Marketing Optimization
  • What are the causal drivers of campaign performance? How well did it actually perform?
  • How do I attribute customers to the right channels while accounting for confounders in the data?
  • What are the next best actions (interventions) to improve campaign performance?
  • What is the incremental impact of increasing allocation on a given channel?
  • Based on my budget, what is the optimal allocation of investment across channels (e.g.: media, shopper marketing, digital, and mail)?
  • What are the root causes of consumer sentiment towards my brand across demographics & geographies?
  • What are the optimal actions (interventions) to improve brand performance?
Customer Journey & Pricing
  • What is the optimal price level for gated content given my strategy?
  • What are the best actions (interventions) to reduce churn at an individual or cohort level?
  • What is the best engagement strategy with users that balance short term metrics with long-term revenue & brand performance (e.g estimating the effect of “clickbait-type” content to long-term revenue)

See Our Solutions in Practice

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Leverage decisionOS

the first operating system for decision making powered by Causal AI, to address all those causal questions

Causal AI

To move beyond traditional ML and into a world where you can provide actionable recommendations by leveraging state of the Causal AI tools and methods.

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DecisionApp Building

Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.

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For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.

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Trusted by leading organisations

Causal AI at MRM

Watch the talk from the Causal AI Conference 2022

Case studies

Customer Case Study: Marketing Mix Modeling

A leading Mobile App company sees a projected 15x ROI through a reduction of 5% in annual marketing spend using decisionOS to optimise marketing allocation

Customer Case Study: Client Retention

North American pension plan improved beneficiary satisfaction and increased retention by 17% using decisionOS powered by Causal AI

Customer Case Study with McCann Worldgroup: Causal Drivers of Purchasing Behavior

McCann Worldgroup & causaLens partner to deliver 5-10% uplift in brand purchase intent for a leading Confectionary Company

Use Cases

Customer Retention

Marketing Mix Modeling

Marketing leaders use Causal AI to improve their marketing mix, attribution, and modeling. decisionOS is the leading Causal AI platform used by marketers globally.

Pricing and Promotion

Enterprises use Causal AI to optimise their pricing and promotion strategies. Traditional ML approaches aren’t sufficient, see why Causal AI could be the answer

Proven value in weeks

  • 1 Icon
    Internal meeting

    One hour

  • 2 Icon
    Scoping sessions

    Two to three hours

  • 3 Icon
    Platform Trial

    Three to four weeks

  • 4 Icon

    Twelve months

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