Data science enhances product. It’s part of product. But far too often I see data science teams reporting to the CTO or a technical leader and I think that’s wrong, and part of the reason data science isn’t always as effective as it can be (read: why it fails).
Don’t get me wrong, data scientists need technical prowess and must work cross functionally to build strong data and deployment pipelines, but when those areas are complete, model building and insights should be driven by product teams. Our industry needs a course correction. Some companies get it. Fortune’s excellent newsletter, Eye on A.I. has covered this topic recently, espousing the idea that effective data science comes from not only stakeholder buy-in but understanding.
To drive home this idea that data science supports product (and should report to product leaders), here are a few of the applications of data science that are product driven:
- Product recommenders – one of my favorite areas of data science. Product recs are there to add personalization to a product catalog; to increase customer engagement. When more customers see what they want, when they want it, everyone wins. After all, that’s why customers come to a site. Product recs should be driven by a product lead. This is a person who understands the product catalog, where profits come from and what’s required to grow revenue. It’s not necessary to understand complex data pipelines to effectively manage a data scientist running product recommendations.
- Customer Lifetime Value (LTV) calculation – this is a model that predicts the value of a customer at any given time. It’s important for companies of all sizes to understand customer LTV. Think about how much it costs to acquire a new customer versus incremental sales from existing customers. For most industries there’s no comparison; it costs vastly more to acquire new customers. And marketers know this. That’s why customer retention is essential, and this is driven by a product, retention or sometimes a marketing team. Specific offers and reach-outs can reengage old customers, and using data science to increase LTV can help companies identify the appropriate customers and even prescribe appropriate methods. Churn prediction is closely related to this topic.
- Customer Segmentation – Customer segmentation helps companies understand groupings of their customer (or products) based on myriad metrics. Often, segmentation helps marketing and sales reach the right customers or prospects with more personalized messaging or offers. For segmentation, we almost always see marketing teams driving the objectives for data science. And this makes sense, as the insights are directly tied to a major concern for marketing teams: getting the right message to the right customer or prospect.
Those are just a few examples of why I believe end users of data science are not technical teams. The implications here are huge.
Initially, data science must align with tech. But as soon as it’s operationalized and functioning, data science almost always enhances product and should align to support product. In terms of company structure, I prefer to see the head of data science report to a CPO or, optimally, the CEO.
What do you think? I’m taking a strong stance here and I’d appreciate your feedback. Have you seen something different work better for your company? Hit reply and let me know.
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Criminals used artificial intelligence-based software to impersonate a chief executive’s voice and demand a fraudulent transfer of €220,000 ($243,000) in March in what cybercrime experts described as an unusual case of artificial intelligence being used in hacking. https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402
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