Skip to Content

The Causal AI conference is returning to London on 24 Sept-24, and it's bigger and better than ever.

Register Interest

Transforming Retail, E-commerce and Consumer Goods with Causal AI

With Causal AI, your Retail organization gains explainable, trustworthy insights and suggested actions that you can take, driving business-critical decisions and delivering tangible results.
Unlock new levels of efficiency and effectiveness in your Retail organization, across Marketing Mix Modeling (MMM), pricing, promotions, supply chain, planning & strategy, and more.

Trusted by leading organizations

Do these questions sound familiar?

How did we do last quarter and what drove the results?

The Causal AI approach: Causal AI helps dissect the true impact of various factors on performance, distinguishing between causation and correlation. This enables more precise adjustments and informed decision-making for future strategies.

Why this matters: Understanding past performance is crucial for continuous improvement. By analyzing sales data, customer feedback, and other performance metrics, retailers can identify successful strategies and areas needing enhancement.

Is customer behaviour shifting and impacting the shopping journey?

The Causal AI approach: Causal AI identifies the underlying causes of these behavior shifts, helping retailers to tailor their marketing, product offerings, and customer engagement strategies effectively to meet evolving customer needs.

Why this matters: Shifts in customer behavior, such as changes in purchasing channels, product preferences, or shopping frequency, can significantly impact sales and customer retention.

What is the impact of our demand generation investments across marketing and promotions?

The Causal AI approach: Unlike traditional methods, Causal AI can isolate the causal impact of specific marketing activities on sales and customer acquisition. This precision helps in refining marketing strategies and allocating budget more effectively.

Why this matters: Measuring the effectiveness of marketing campaigns and promotional activities is essential for optimizing marketing spend and maximizing return on investment.

Are shifting consumer segments impacting our ability to create loyalty?

The Causal AI approach: Causal AI can determine the true drivers of customer loyalty and how different segments respond to various initiatives. This insight allows for more targeted and effective loyalty programs, enhancing customer retention.

Why this matters: Customer loyalty is vital for sustained revenue growth and profitability. Understanding how different consumer segments respond to loyalty programs helps in designing more effective retention strategies.

What do we need to do to maximize revenue, profitability, and market share?

The Causal AI approach: Causal AI provides a clear understanding of the causal relationships between various business activities and outcomes. This enables more accurate scenario planning and decision-making to maximize key business metrics.

Why this matters: Continuous optimization of business strategies is necessary to achieve and maintain competitive advantage. This includes pricing strategies, inventory management, and expansion plans.

How have our technology investments impacted the customer experience?

The Causal AI approach: Causal AI assesses the direct effects of technology implementations on customer satisfaction, operational efficiency, and sales performance. This enables better justification of technology spend and identification of further improvement areas.

Why this matters: Investments in technology, such as e-commerce platforms, mobile apps, and in-store technology, aim to enhance the customer experience and streamline operations. Evaluating their impact ensures that these investments deliver the intended benefits.

Get answers to your business-critical questions

True Causal Understanding

Causal AI goes beyond correlation to uncover true cause-and-effect relationships.

Learn more

Scenario Building & Analysis

Utilize causal structural models to create scenarios, perform historical ‘what-if’ analyses, and conduct comprehensive root cause analysis.

Learn more

Workflow Integration

Data engineering > Causal discovery > Causal Modeling > Intelligence engineering > Decision enablment. All in one platform.

Learn more

Proven Use Cases in Retail

Explore proven use cases in retail and ask us about how you can replicate this success.

Causal AI at Bergfreunde

How we're helping Bergfreunde to decode e-commerce

Customer Success Stories

Customer Case Study with McCann Worldgroup: Causal Drivers of Purchasing Behavior

McCann Worldgroup & causaLens partner to deliver 5-10% uplift in brand purchase intent for a leading Confectionary Company

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

Customer Case Study: Manufacturing Root Cause Analysis

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

Start your Causal AI Journey for free

Get your free trial