Causal AI For Products and Manufacturing

Current Approach and Pain Points

Marketers invest resources, money, and time to ultimately drive conversion and long term value with customers. However, there are many channels of investment, the path to purchase has increased in overall complexity, and attribution models are generally not delivering a thorough understanding of causality.

Causal AI Solution

causaLens enables marketing teams to autonomously discover the causal drivers of marketing performance. Distinguishing between statistical correlations and true causal drivers translates to vastly improved models and, as a result, optimized marketing spend and product recommendations.

Questions you can answer with causality:

• Based on my budget, what is the optimal allocation of investment across channels (e.g. media, shopper marketing, digital, and mail)?
• What are the material drivers of customer engagement and conversion?
• What external data is relevant for scenario planning?

Current Approach and Pain Points

Organizations are constantly seeking means to manufacture products as efficiently as possible to meet the changing demands for products. Forecasting labor requirements, forecasting demand, understanding the demand impact of innovation, and knowing the key drivers of output inefficiencies.

Causal AI Solution

Causal AI enables organizations to understand the changing demands for manufacturing which includes allocation for demand planning and procurement, planning for labor, and drivers of material changes in the manufacturing KPIs with explainable and transparent insights.

Questions you can answer with causality:

• What is the forecasted demand given multiple scenarios?
• What is causing lower output in the manufacturing process and which alerts are driving downtime?
• What is the optimal labor allocation based on the changing demand and expected output?

Current Approach and Pain Points

For Consumer led companies, understanding the consumer is essential to company success. Knowing the consumer needs and wants and whys create a potential competitive advantage.  This is difficult to do well given the fragmentation of data, the changing dynamics of the customer journey, the changing macro environment, and the competitive landscape.  Also, organizations spend time, resources, and money searching for insights that are actionable and valuable.

Causal AI Solution

Understanding the consumer is complex and a difficult process. Causal ai enables organizations to understand relationships, their impact, and drivers across multiple data sets, the impact of tactics throughout the consumer journey, and scenario plan for future changes to enable organizations to much more proactive and innovative.

Questions you can answer with causality:

• How is consumer behavior changing over time (e.g. brand preference) and what are the key drivers?
• Throughout the consumer journey, what are the most impactful tactics to increase engagement with the brand?
• As the environment changes, what actions should we be taking to win in the marketplace?

Current Approach and Pain Points

Supply chains are complex and there are many variables which can impact the readiness of an organization to ensure a smooth inventory flow. From promotions, competitive activity, macro events like inflation growth, and supplier initiatives, it is difficult to truly understand and explain the key drivers of supply chain KPIs.

Causal AI Solution

Models which autonomously adapting to the changing dynamics of the supply chain to inform the decision makers with explainable and transparent insights. Whether enabling a better demand forecast, warehouse efficiency, transportation allocation, or smarter fulfillment, causal ai enables a complete and dynamic platform for the supply chain.

Questions you can answer with causality:

• What actions do I need to take based on multiple scenarios to maximize my service levels?
• How will the macro factors, in addition to other data sets, impact my demand forecast?
• What decisions are impacting my margin when analyzing my fulfillment strategy and how can I optimize those decisions?

Current Approach and Pain Points

Retail and Commerce companies are constantly looking for growth, more importantly profitable growth. With so many variables to account for, constantly shifting macro and market dynamics, and so many levers to potentially utilize, Retailers and Commerce companies seek to understand what data is relevant and what insights are material.

Causal AI Solution

Causal AI enables organizations to optimize their investments and decisions based on the goals, whether it be the top line the bottom line or a blend. Dynamically plan against your targets and know, with transparency, when you may need to adjust and by how much.

Questions you can answer with causality:

• What investments will help me achieve my sales and profit targets?
• How will the competitive, market, and macro dynamics impact my business?
• What business questions should I be asking that I may not be asking today?

Current Approach and Pain Points

Financial forecasting can be a drain on the entire business, requiring input from many and difficult to integrate into a repeatable learning process. Also, as market dynamics quickly change, organizations are looking to develop multiple scenarios to cope with the changing variables.  This process has proven to be inefficient and often not very impactful.

Causal AI Solution

• Enable organizations to understand the key drivers of historical performance which can be leveraged to inform smarter and faster output
• Quickly deliver scenario planning, dynamically, to become a proactive organization but also deliver models that are dynamic and adjust as the environment adjusts
• Deliver forecasts which are explainable and transparent, enabling trust and adoption within the organization

Questions you can answer with causality:

• What data and variables are important when developing the financial forecast?
• What have been the key drivers of change and how do they impact performance over time?
• How important and impactful are the macro factors and how should we adjust our strategies to account for changing scenarios?

Current Approach and Pain Points

Innovation is a key enabler for growth for Consumer Products companies as they search to remain relevant through product premium-ization, improvement, and introductions. They invest significant time, investment and resources to create and commercialize innovation.  Many times, it takes too long to bring innovation to market and even when developed the company many do not know what to expect in terms of the drivers for success or failure.

Causal AI Solution

With Causal AI, organizations will be able to truly understand:

• • Historical causality
    • What were causal drivers of success or failure
    • Conduct counterfactual analysis to improve knowledge base
    • Analyze competition’s causal drivers and impact (e.g. syndicated)
  • R&D
    • Understand causal trends of consumer behavior, categories or segments
    • Integrate macro data/market data into analysis (e.g. inflation/commodity, IRI/Nielsen)
    • Effectively and efficiently conduct A/B testing with Causal AI
  • Commercial
    • What if scenario planning integrating causal analysis against multiple KPIs (e.g. market share, margin, revenue)
    • Determine commercial strategy (e.g. pricing, distribution targets) while understanding potential outcomes
    • Know what marketing (shopper, DTC, media) levers have historically delivered value to innovation and dynamically track

Questions you can answer with causality:

• What are the drivers of success for innovation across categories and segments?
• How will my innovation perform given multiple scenarios and investment?
• How much incremental value has our innovation delivered and how much success has the competition experienced as well as what were the key drivers?