LOCALOGY ENGAGE 19: AI’s Impact on the B2B Customer Journey

AI is a buzzword that shows up in nearly every piece of marketing collateral or press release. However, for most people working in technology, it’s still something of a black box. Demystifying AI and the role it can play in B2B customer acquisition and retention was the big topic that data scientist Dalia Asterbadi tackled at the LOCALOGY ENGAGE: SaaS/SMB event in Washington, D.C.

Many people believe that simply running data through machine learning algorithms will solve fundamental business problems. But Asterbadi made clear that while AI tools can surface insights and do data analysis humans might miss, it can’t replace human creativity, learning and problem solving. And it can’t compensate for a broken process or sales culture.

The biggest limitation of AI and machine learning is that it’s only going to be as good as the available data sets. Incompleteness or inaccuracy in the data will come out on the other side in flawed or biased results.

Fundamentally, argued Asterbadi, companies need to know their customer and have a sense of the buyer journey. They need to have a basic understanding of their data and KPIs and then identify questions they’d like to answer before machine learning and AI can be utilized. There needs to be enough data and relatively clean data to maximize the value and reliability of potential insights.

AI Session

Despite the need to have an understanding of the customer and the data, not everything needs to be fully worked out ahead of time. Machine learning might deliver insights about customer behavior or audience segments that the marketer or publisher didn’t know (e.g., which customers are most engaged according to some metric defining engagement).

Machine learning can potentially be used at every stage of customer journey analysis, Asterbadi points out. However, she explains, “AI is also only as good as your business’ health.” Poorly run businesses aren’t going to be able to compensate with AI.

Indeed, AI isn’t a magic solution where you can push a button and get answers. Users must have a general sense of what they’re trying to understand or what they’d like to know about their audience or their data. Machine learning and AI can then help remove “blind spots” and identify windows of opportunity.

It’s not a neutral technology – or even “a technology.” AI and machine learning will potentially work well for high-functioning organizations, according to Asterbadi. But it won’t solve fundamental problems for others that don’t have healthy cultures and processes already in place.

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