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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 across marketing, branding, customer journey & pricing

Traditional machine learning approaches often fail to address critical business questions

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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)?
Branding
  • 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)

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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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DecisionOps

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

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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

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

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    Production

    Twelve months

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