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”
Do these questions sound familiar?
Retail & Consumer Goods run on causal questions
- 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?
- 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
- 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)?
- 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?
- 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?
- 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?)
the first operating system for decision making powered by Causal AI, to address all those causal questions
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 more
Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.Learn more
Trusted by leading organisations
Causal AI at Nestle
Watch the talk from the Causal AI Conference 2022
Efficient production and the prevention of recurring issues are crucial goals for any manufacturer seeking to maintain high-quality standards and maximize profitability.
A leading manufacturer of IT products and equipment sees $19mn in savings from matching inventory levels to customer demand more accurately
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
Two to three hours
3Proof of Concept
Three to four weeks