Using AI to Scale Your Business
Artificial Intelligence and Experimentation have a symbiotic relationship. AI is changing what’s possible from an Experimentation
standpoint, giving your product managers, UX strategists, and data scientists more time to focus on novel innovation and
experimentation ideas instead of maintaining run-of-the-mill CRO tests. Experimentation techniques allow business leaders to test
and measure their AI products, techniques, and strategies to ensure quicker paths to a return on investment. The Artificial
Intelligence + Experimentation continuum includes the following modules that layer on one another to create a profitable program:
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A/B Testing is the most basic form of experimentation, but it is often the most impactful. These experiments can lead to true innovation and design thinking, and every program should get the basics of A/B Testing down before they try to add AI into the mix. Even once simple experiments are automated, big bets will still get high-touch, product manager interaction. Think: Amazon One-Click Checkout. Segmentation allows for more targeted experiences through data-driven user personas and consumer behavior analysis. Once you master macro-level A/B Testing, it’s time to apply that formula against various user segments. Additional investments are often required during this phase of experimentation such as connecting a testing tool to a CRM or CDP or conducting advanced analytic techniques such as cluster analyses. Multi-channel experiments let you test out strategic user journeys and cross-channel consistency. However, it requires tracking investments to understand your customer journeys fully, and ensure consistency across multiple channels, particularly as cookie best-practices evolve. For both segmentation and multi-channel approaches, loyalty programs that encourage multi-channel sign-ins and data opt-ins can be helpful mechanisms to acquire and connect data. Machine learning measurement applies automation to the previously mentioned methods to increase experimentation velocity and unlock 1-to-1 personalization, but it requires investment in a larger infrastructure. This strategy can help capitalize on ROI with a multi-armed bandit deployment. Generative AI incrementality allows your multi-armed bandit and operationalized measurement machine to test experiences at scale. It’s best to stick to small changes that don’t introduce too much risk—e.g., button color changes—and define clear success metrics. Even with heavy automation, it’s important to keep a human in the loop to avoid adverse outcomes and monitor the algorithms for overall learnings and assess when it’s time to retrain and models.
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A Blueprint for CMO Success in the High-Stakes AI Marketplace
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