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Retail and Consumer Goods

Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the most crucial questions in Retail and Consumer Goods across pricing, promotions, supply chain, marketing, merchandising, 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, not next best action

For example, they can predict if customer is predicted to churn but can’t recommend the 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?

Retail & Consumer Goods run on causal questions

Pricing & Promotions
  • What are the causal drivers of conversions?
  • What are the best actions (interventions) I should make in my promotions and discounts strategy to optimize for market share or profit margins?
  • What does competitor behavior impact my market share or margins?
  • What are my products’ price elasticities and what is the optimal price for a certain SKU?
Supply Chain Resilience & Operational Efficiency
  • What actions do I need to take based on multiple scenarios to maximize my service levels?
  • What decisions are impacting my margins when analyzing my fulfillment strategy, and how can I optimize those decisions?
  • How will initiatives impact operations, potentially in store, and what do I need to do to support?
  • What are the right suppliers for my components, given costs, lead times and demand for final products?
  • How might future supply chain disruptions impact my operations, and what are the best alternatives?
  • How can I identify bottlenecks to improve warehouse efficiency? What actions should I take to reduce scheduling inefficiencies
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)?
Merchandising Effectiveness
  • How should I optimize pricing, base or promotional, to hit my targets?
  • How are store-level initiatives impacting my overall revenue, profitability or share?
  • How do competitor behaviors and market environment impact my plan, and what should I do to address upcoming challenges?
User journey
  • How do I design a website that optimizes conversions?
  • What are the right up-sells and cross-sells at the checkout page for each customer?
  • What are the right recommendations for each user/SKU?
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

Lots of good executives have a causal theory of reality in their head. What they often want to see is dashboards, metrics, highly correlated data where the causal filtering is happening in a board room… The interesting challenge for the [Causal AI] community is now that we have these tools, you don’t have to do it intuitively, how do you reconcile this with the traditional mindset?

Amit Gandhi, Vice President and Technical Fellow

cAI Conference 2023

Transparency & explainability of AI models requires an understanding of causality – an inherent advantage of the causaLens platform.

Wendy Harrington, Chief Data & AI Officer

 

 

 

 

 

 

Causal AI at Nestle

Watch the talk from the Causal AI Conference 2022

Case studies

Customer Case Study: Manufacturing Root Cause Analysis

$15M annual value unlocked through reduced downtime for a leading commodity manufacturer

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

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

Use Cases

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

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.

Manufacturing Root Cause Analysis

Manufacturing leaders trust Causal AI to improve their manufacturing processes. decisionOS is the leading Causal AI platform used by Manufacturers globally.

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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