Causal Impact (CI) of customer actions are broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate the CI values across 100s of customer actions of business interest and 100s of millions of customers. We operationalize DML through a causal ML library based on Spark with a flexible, JSON-driven model configuration approach to estimate CI at scale (i.e., across hundred of actions and millions of customers). We outline the DML methodology and implementation, and associated benefits over the traditional potential outcomes based CI model. We show population-level as well as customer-level CI values along with confidence intervals. The validation metrics show a 2.2% gain over the baseline methods and a 2.5X gain in the computational time. Our contribution is to advance the scalable application of CI, while also providing an interface that allows faster experimentation, cross-platform support, ability to onboard new use cases, and improves accessibility of underlying code for partner teams.

With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.

Marketers use any number of data points to inform the recommendations they make to customers. But do they really know the causes behind why customers prefer one product or message over another? One way to find out is using AI to analyze more data from the entire customer journey, instead of depending on limited results from specific A/B tests. "The ability to understand the true drivers behind customer behavior across the journey is transformative," said Zubair Magrey, GM, marketing for U.K.-based decision-making software company causaLens at The MarTech Conference. That's because the data alone doesn't provide a full picture.

Artificial intelligence (AI) has been a game-changer for the retail and E-commerce industries. According to Statista, retail sales are projected to amount to around $30 trillion by 2023. According to Nasdaq, 95% of purchases will be facilitated by E-commerce by 2040. No doubt, AI will be shaping retail digitization. AI has pretty much to offer the retail industry.

The degree to which AI is integrated into a brand's marketing strategy is now the standard measurement for how effective that brand's campaigns can be. AI lifts the ceiling on possibilities and performance. This applies to elements of both online and offline strategies, since available tools allow brands to seamlessly integrate and manage data from one channel to inform decisions and strategies in another. This unified marketing approach is managed on the basis of the ability to gather and analyze the greatest currency of our age--data. For most companies, one of the main goals behind digital transformations is optimizing the ability to gather this data and to use it in the automation of various processes.

Data is the foundation of every modern marketing plan. What distinguishes some marketing approaches from others is the way that data is collected, how it's leveraged and how effectively it can drive desired outcomes. The ability to nail these competencies, which can be optimized by the right technologies, can mean the difference between success and failure. Loyalty programs can be robust sources of customer information and are particularly suited for collecting data at the top of the sales funnel. It is a mistake to view loyalty and promotion programs as simply a vehicle for driving incremental purchases.