The AI Product Manager's Handbook by Irene Bratsis

Author:Irene Bratsis
Language: eng
Format: epub
Publisher: Packt Publishing Pvt Ltd
Published: 2023-02-27T00:00:00+00:00

Experimentation – discover the needs of your collective

With B2C products, because there isn’t such a hyperfocus on the domain you’re serving, the pressure on the market you’re serving is a bit more relaxed, but this creates another kind of pressure: the pressure to build a product that appeals to a much wider audience universally. Casting a wider net brings other areas into focus. As a PM building in a B2C environment, you’re going to approach your product and the market it serves more experimentally and derive insights about what’s most useful to your customers by tracking how they use your product. You can conduct focus groups, nurture beta testers, or interview your customers through in-product surveys, but because you can’t conduct customer interviews in the way you might for a B2B product, you’re left with understanding your customers’ impressions of your product through their in-product behaviors.

This business model also consolidates all your user and buyer personas in one because, typically, the person that’s using your product is also the person buying it. Understanding the main drivers, desires, hopes, and dreams of your customer base becomes a very nebulous task because what you’re trying to capture are underlying needs, pain points, and moments of delight that will apply to all your users at once. This complicates a PM’s ability to empathize with their end users. Because most of these users aren’t signing the kind of year-long contracts you see with B2B products, the pressure to keep these consumers charmed is constant because they can leave at any point. This puts pressure on product, leadership, and development teams to consistently be providing their customer base reasons to stay and choose them.

Because of the bird’s-eye view B2C products enforce on their builders, this means PMs have to be very discerning with their data analytics and metrics. Investing in understanding their customer lifetime value and customer acquisition costs and tracking those metrics is an important part of staying profitable and sustainable in the B2C landscape. B2C offers PMs an easy outlet for applying AI/ML toward the acquisition and retention of customers since they have such few touchpoints with the end users of their product. PMs in the consumer space also need to hone in on the demographic and individualistic qualities of their consumers to better understand what to build. If you see that it’s mostly people within a geographic area, gender, generation, or subgenre that appeals most to your product, you might start to build future features and releases in your roadmap with them in mind.

We’ve mentioned many times that AI/ML products are experimental in nature because you want to leverage AI/ML in ways that will impact your product most obviously for it to be worth the top-heavy investment it requires. This is doubly true for B2C products because you’re building and using AI to deliver something that saves your consumers money or delights them, as well as using the data your product produces to decide on how to pivot your product. B2C PMs are reliant on data and analytics to make global decisions about their products on a regular basis.

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