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Telecoms

Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the biggest challenges across marketing, customer journey, pricing, network optimisation as well as centralized planning & strategy

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 only, 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?

Telecommunications run on causal questions

Marketing Optimization
  • What are the causal drivers of campaign performance? 
  • 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 spend on a given channel?
  • What is the optimal investment across channels for a given budget? (e.g.: digital, out-of-home, mail etc)
Pricing & Promotions
  • What are the causal drivers of conversions?
  • What actions (interventions) should make in my promotion strategy to optimize for customer LTV?
  • What impact does competitor pricing have on my customer’s decisions?
  • What are my products’ price elasticities?
Network Optimization & Maintenance
  • What are the root causes of outages or inefficiencies?
  • What actions can I take to prevent future network issues?
  • Where should I invest to improve network quality?
  • How should I prioritize customer support enquiries?
  • To what extent does poor network experience cause customers to complain or churn?
Centralized planning & strategy
  • What investments will help me achieve my sales and profit targets while improving customer experience?
  • How will the competitive, market, and macro dynamics impact my PnL?
  • How will decisions in pricing, marketing or supply chain impact the rest of the business? (e.g will pricing decisions disrupt the supply chain?)

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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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

Case studies

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: 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: Inventory Optimization

A leading manufacturer of IT products and equipment sees $19mn in savings from matching inventory levels to customer demand more accurately

Use Cases

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.

Customer Retention

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
    Production

    Twelve months

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