Retail & Consumer Goods
Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the most crucial questions across pricing, promotions, supply chain, marketing, merchandising as well as centralised planning & strategy
Traditional machine learning approaches often fail to address critical business questions
Spurious correlations lead to bad decisions
And are often perceived as “black boxes”
Read more on our blog:
Explainable AI (XAI) doesn’t explain enough
For example, they can predict if customer is predicted to churn but can’t recommend the next best action to retain the customer
Do these questions sound familiar?
Retail & Consumer Goods run on causal questions
See Our Solutions in Practice
Visit Demo hubLeverage 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.
Learn moreDecisionApp Building
Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.
Learn moreDecisionOps
For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.
Learn moreTrusted 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 Optimization
Efficient production and the prevention of recurring issues are crucial goals for any manufacturer seeking to maintain high-quality standards and maximize profitability.
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
Proven value in weeks
-
1Internal meeting
One hour
-
2Scoping sessions
Two to three hours
-
3Platform Trial
Three to four weeks
-
4Production
Twelve months