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Why You Aren’t Getting More from Your Marketing AIFewer than 40% of companies that invest in AI see gains from it, usually because of one or more of these errors: (1) They don’t ask the right question, and end up directing AI to solve the wrong problem. (2) They don’t recognize the differences between the value of being right and the costs of being wrong, and assume all prediction mistakes are equivalent. (3) They don’t leverage AI’s ability to make far more frequent and granular decisions, and keep following their old practices. If marketers and data science teams communicate better and take steps to avoid these pitfalls, they’ll get much higher returns on their AI efforts.

Managing AI Decision-Making ToolsThe nature of micro-decisions requires some level of automation, particularly for real-time and higher-volume decisions. Automation is enabled by algorithms (the rules, predictions, constraints, and logic that determine how a micro-decision is made). And these decision-making algorithms are often described as artificial intelligence (AI). The critical question is, how do human managers manage these types of algorithm-powered systems. An autonomous system is conceptually very easy. Imagine a driverless car without a steering wheel. The driver simply tells the car where to go and hopes for the best. But the moment there’s a steering wheel, you have a problem. You must inform the driver when they might want to intervene, how they can intervene, and how much notice you will give them when the need to intervene arises. You must think carefully about the information you will present to the driver to help them make an appropriate intervention.

Measuring Your Algorithm’s PerformanceUnderstanding how to evaluate and manage algorithmic performance could be the difference between success and failure. This article outlines a six-step approach for defining what to measure and monitor. Central to this approach is to work out where the waste is by measuring failure states. These are critical to monitoring performance, prioritizing enhancements and understanding if actions are actually improving performance.

Do You Really Understand Your Best (and Worst) Customers?Most companies analyze financial performance by looking at geographic segments, product lines, or channels. These perspectives overlook the fact that aggregate revenues are dependent on individual customers, and that’s why analyzing the business by looking at its customers can provide managers with useful insights. This process, called a customer-base audit, can be a tool for businesses as they seek to answer five questions: Who are our best and worst customers? How is customer behavior changing? How does a cohort of customers change over time? How do different cohorts behave differently? And when you put the answers to these questions together, what do they suggest about the health of the business?

3 Core Principles of Digital Customer ExperienceSuccessful businesses focus on three core principles of their customer experience: They put the digital experience in the business context. They recognize that customers are not created equal. And they make zero-based decisions. Continuous improvement is fine, but it’s important to be willing to think from first principles, and to re-examine and justify what you are doing and what you could do differently. This helps ensure that the digital roadmap is prioritized and focused on the highest impact actions.

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