Marketing in the Age of AI

Marketing Attribution: Marketing attribution works by measuring the effectiveness of different strategies as part of a digital-only campaign across one specific device (for instance, what sort of ad resulted in a customer purchase). While attribution isn’t new for marketing teams that have dealt with it in the past, ML-based attribution (and thus AI) is more accurate than the old methods when dealing with the digital world and its vast number of channels. ML algorithms have the capability to interrelate all the times a consumer has been exposed to your marketing and determine the exact contribution of each channel in bringing the customer to conversion.

Combatting Churn and Increasing Loyalty: Churn analysis (also known as attrition modeling) helps prevent profitable customers from leaving without getting a chance to retain them. Churn analysis relies on careful preparation of customer data, examining available customer history, and taking snapshots at various points in time of the experience. It compares the different snapshots to get an idea of whether something has changed during their time as customers. Finally, a machine learning algorithm associates notable changes to customer actions, such as leaving or staying in your customer base. Used with a lifetime value model, a churn prediction model can promptly and automatically react when the risk of losing important customers appears.

Recommendation Engines: Recommender systems are the digital heirs of marketing research. Instead of conventional research methods such as questionnaires, recommenders use historical data together with customer profiles, and they immediately satisfy customer needs through targeted suggestions. Some recommenders rely on simple processes, while others require demanding math. However, all recommender systems are embedded in some customer process, such as a website that sells goods, as they directly interact with your customers by pointing out products you want the customer to discover. Meanwhile, the recommender system automatically learns to understand a customer’s needs better.

Marketing use cases are often the lowest-hanging fruit and can unlock the path to Enterprise AI for the entire company. That’s why today’s top marketing teams are setting the example by diving into machine learning, proving its worth, and thus leading the AI revolution.

“Exciting new measurement technologies hold the promise of lifting marketing effectiveness to new heights but, in the real world, no marketer can thrive on technology alone. The savviest marketers realize that making progress on the measurement journey involves mobilizing both their teams and their technologies, which leads to better business outcomes, whether that’s revenue, profitability or customer churn.”
– John Grudnowski, Digital Marketing Expert at Bain & Company