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Marketing Mix Modeling

Traditional, correlational, machine learning approaches often fail to improve marketing mix

We have brought to market the first operating system for decision making powered by Causal AI, decisionOS, to empower enterprises to optimize their marketing mix

Traditional machine learning approaches often fail

to improve marketing activities as they are unable to answer key 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 doesn’t explain enough

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Prediction, not next best action

For example, they can predict performance per channel but can’t suggest the optimal allocation per channel

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From predicting to Influencing

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Do these questions sound familiar?

Enterprises run on causal questions


How do I ensure I attribute conversions to the right channels?


What is the incremental impact of increasing allocation on a given channel?

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What are the causal drivers of campaign performance? How well did my campaign actually perform?


What are the next best actions (interventions) to improve campaign performance?

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What are the next-best-actions on an individual customer basis? Why should I treat customers differently?

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Based on my budget, what is the optimal allocation of investment across channels (e.g., TV, digital, out-of-home, mail etc)?

License our platform to answer these questions using Causal AI

Discover cause-effect relationships within your marketing data

Discover cause-effect relationships in your data by combining the best of domain knowledge with data-driven, statistical, approaches

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Build Causal Models

Discover causal models, based on cause-effect relationships and causal graphs, that can robustly estimate the attribution to for each channel and allow you to run powerful what-if scenarios to optimize future spend

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Make better decisions with Decision Intelligence Engines

Leverage our pre-built engines on top of your causal models to:


Design next best actions

E.g How to allocate my marketing budget to maximise a specific marketing metric (conversions, brand awareness, etc)

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Test the fairness of your models

Assess how fair the model is with respect to gender

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Perform powerful what-if analyses and estimate counterfactual scenarios

E.g Understand the impact of increasing digital ad spend by 10%

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Build Decision Apps

Leverage Dara, our app building framework, to seamlessly deliver beautiful, interactive decision making applications to the marketing & executive teams

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Monitor and optimize marketing decisions with decisionOps

Build, deploy and monitor your decision workflows. Attribute KPI performance to the correct actions/ circumstances e.g., current marketing mix, seasonality,  the macro-economic environment to optimize your marketing spend

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Customer Case Study: Marketing Mix Modeling

One of the world’s leading Mobile App companies forecasts 15x ROI through a reduction of 5% in annual marketing spend while maintaining the same amount of new installations, returners and paying users

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Causal AI for Marketing

Watch the talk from the Causal AI Conference 2022

Proven value in weeks

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    Internal meeting

    One hour

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    Scoping sessions

    Two to three hours

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    Platform Trial

    Three to four weeks

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    Deployment in production

    Twelve months

Start your Causal AI Journey

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