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Five Ways AI will Transform Customer Success

By Durand Vadnais

Today, even the most experienced customer success teams rely on a little intuition and a whole lot of running around—talking with sales, finance, engineering, product, support, and so on—to discern each client’s status and build a systemic view of the entire relationship. It is an imperfect human-driven operation, and the results that come from it are too.

Fortunately, several tech companies have begun to bring artificial intelligence (AI) to the discipline, and their new tools promise to revolutionize the impact of customer success teams. With minimal effort, these solutions will deliver a much more efficient picture of a business’ ecosystem and the specific parts within.

It’s happening now because of advances in both computing power and data collection. But this is not just more data for data’s sake; a recent Harvard Business Review/Salesforce study found that only 23% of business leaders are able to act on valuable customer data they currently collect. The new AI solutions, rather, will enable customer success teams to take that action, especially in five key areas:

●     Forecasting risk

Leveraging and learning from multiple data points—contract size and length, number of support calls, engagement with learning tools, a client’s financial health, even text or voice sentiment analysis (like Gainsight) —will allow for a more explicit calculation of risk.

●     Predicting and preventing failure

We’ve already seen the beginnings of AI in CS software that can replace “Hmm, I have a bad feeling about this” with actual data science. Ultimately, the best will be able to identify major product or process gaps well in advance of those gaps impacting customer satisfaction. We are currently deploying as part of our stack to better understand our product and customer engagement at a hyper-granular level.

●     Configuring the product

There will come a time when deep learning enables seamless product personalization for every client. No more will we need to rely on customer input while we pull the levers. Instead, it will be the job of AI to understand the client’s context, priorities and needs.

●     Personalizing communication

Time and budget constraints often make it challenging for customer success teams with diverse portfolios to fully engage each client. But the marketing side’s email bots and virtual assistants hint at the future collaboration between humans and AI on customer success. While these tend to be a first line of defense today,  the technologies will soon be smart enough to provide well-rounded relationship support—helping customers at scale.

●     Informing customer strategy

Those diverse portfolios also mean customer success managers often group customers into imperfect buckets that don’t adequately reflect their needs and concerns. AI will mine communications, competitor information, and other online data sources for customer nuance, highlighting elements and insights that are most essential to success for each one.

Yes, any discussion of AI’s impact inevitably begs the jobs question, but here’s the thing: Human connection is essential to customer success; that is not about to change. What AI can and will do is make us more effective at our job. Consider the effort it now takes to transition a customer from one manager to another. Without AI, continuity is completely dependent on lots of notes and an understanding of subtle connections between people and companies. One day soon, your dashboard will surface all of the subtle insights you could possibly need.

What’s not to like about a technology that will only arm our industry’s leaders and managers with robust, insightful, meaningful data? AI’s actionable data will eliminate the guesswork and allow us to create the valuable customer experiences that drive business growth.

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