Chapter 7 : strategy 3

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3. Apply the Data (No, Really)

"Take 100 decisions that you may make in a day and six that you make will be made based on data. More than half will be made based on intuition," says Hoyne. He's alluding to the idea that retailers are perhaps less data-driven than they think – and that even a small step in the right direction can help them be that business model that they envision themselves to be. To get there, as an executive, one ecommerce business priority should be to double down on first-party data.

The good news is that machine learning (ML) can observe a lot of this customer data. The key is applying that data through automation and ML in next-generation ways. This often requires a human touch.

If done well, however, the results can be game-changing. AI-powered personalization with machine learning to make it better over time reduces the cost of acquiring new customers, possibly up to 5%. The same McKinsey study that shared this good news also credits advanced personalization with revenue and retention boosts of up to 15%. With an overall cost savings potential of 30%, most companies can't afford to skip this next generation of tech in their ecommerce platform and marketing strategy.

One very good example of personalization at work is with product recommendations. Sure, you can suggest batteries to somebody buying a toy online. But what about the products they're unlikely to discover on their own, but that increase the chances of an incremental purchase? Knock-knock, that's customer loyalty at the door.

This scenario doesn't even consider the next-level applications of AI that are happening right now. Generative AI, like that used to fuel Gemini, CoPilot, and ChatGPT, is available to organizations for internal knowledge sharing and as an ultra-responsive customer support tool. This tech is in the early stages but is already showing results for the brands that use it.

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