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Causal AI For Retail & E-Commerce

Understanding the consumer path to purchase can be complex given the number of options for
engagement. Until now, organizations have spent too much time, resources, and investment
on analyzing and informing smarter actions. Causal AI enables organizations to quickly understand the key drivers and dynamically plan for the future, so teams deliver value with explainable, transparent, and unbiased decisions.


Priority issues facing our retail clients

  1. Sales and Profitable Growth
  2. Marketing
  3. Merchandising
  4. Supply Chain
  5. Operations


Forecasts based on standard machine learning techniques only extrapolate from historical trends, making them fragile to new market entrants, product line changes and evolving consumer behaviours such as demand for omni-channel fulfillment. causaLens uncovers the true structure behind the supply chain to create predictive causal models for any supply chain variable. These models autonomously adapt up to three times faster than current state-of-the-art machine learning under regime shifts. CausalNet, causaLens’ proprietary inference engine, delivers actionable insights that not only evaluate the factors impacting fulfillment performance down to individual SKU and node level, but go beyond predictions to provide dynamically updated optimal warehouse layouts. With causaLens, retailers can prioritize slow-moving or obsolete store inventory, and make use of it at the most profitable price point. 

Data-driven approaches can optimize the advertising ROI and increase the customer conversion & profitability. 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. It also allows business users to automatically answer what-if questions and assess market interventions that happened before without the traditional, costly trial-and-error process

Current approaches rely purely on historical data and they fail to efficiently model market interventions as well as competitors’ behaviour, leading to suboptimal results. causaLens allows you to autonomously discover the most profitable pricing points based on true causal drivers and market interventions. It also allows you to conduct virtual A/B testing by modelling novel scenarios without relying on traditional, costly trial-and-error experimentation.

Retail & E-Commerce Pain Points

causaLens’ approach to the retail and e-commerce industries’ biggest challenges

Sales & Profitable Growth

Current Approach

  • 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?

Marketing Optimization

Current approach

  • 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?

Merchandising Effectiveness

Current approach

  • Merchandising is accountable to make decisions quickly to enable the organization to achieve their financial goals.  With so many inputs across promotions, pricing, assortment, shopper marketing, and more, the amount of data and complexity creates a difficult environment for the team to truly understand what decisions to make and the potential impact.  Additionally, the complexity of the path to purchase, in many cases engagement with suppliers and vendors, all increase the number of stakeholders and points of view.

Causal AI Solution

  • Causal ai enables merchandising team to truly understand the drivers of their overall business, categories, segments, shopper behavior, and channels.  With causal ai, they can create plans which are optimized based on their goals and understand, with transparency and explainability, what resources or investments are necessary to achieve them.

Questions you can answer with causality:

  • 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 does that macro and market environment impact my plan and what should I do to plan for any additional changes?

Supply Chain Resiliency

Current approach

  • 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?

Operations Efficiency

Current approach

  • Operations leaders are focused to efficiently enable the movement and selling of product given the assets within their responsibility.  These assets include labor, inventory, stores, and processes.  There are many challenges within retail operations today such as efficient labor allocation and enablement, maximizing the store footprint, and improving processes.

Causal AI Solution

  • Causality will enable teams to determine within the data, what truly is driving the changes in the KPIs as well identify how decisions impact across the operational landscape.

Questions you can answer with causality:

  • What are the optimal allocations of resources given the changing environment?
  • How will initiatives impact operations, potentially in store, and what do I need to do to support?
  • What is driving inefficiencies in labor (e.g. tasks per hour) and how can I optimize?

The value of causality in retail and E-commerce

With Causal AI, teams have the ability to integrate macro and market data with their data to better inform a more holistic understanding of relationships and their impact on the business.  Examples of these data sets include:

  • Syndicated data 
  • CPI and Inflation
  • Unemployment
  • Weather
  • Commodity pricing