Editorial
The Gist
- Leveraging AI in customer success. While AI continues to prove itself, customer success (CS) teams especially are closely watching to see how to leverage its superpowers to create more personalized customer connections, scale their time and impact and ultimately bring in more revenue to their organizations.
- AI boosting engagement. One thing is clear so far — AI infused into the customer journey is translating to more productive, more valuable engagements for CS teams and customers alike.
In recent years, the C-suite has encouraged a customer-centric mentality across their organizations, pushing leaders in every department to make decisions and chart paths for growth with customers at the forefront. It’s as simple as putting customers first and as complex as fostering cross-functional connectedness and toying with processes and procedures across the company.
With all this focus internally on customer centric-strategies, how can CS teams make sure that customer-centric approach is palpable to customers themselves? The new-age customer experience must be so precious and so seamless that every interaction with your organization leaves customers wowed.
This is why modern CS leaders are exploring how to combine people and AI to deliver real business value. The quality of brand interactions is integral to building customer trust. CS teams should embrace AI as a companion to increase their impact and help customers get the most value from their product or service.
Let's take a look at AI in customer success. Here’s how AI can champion your team to fill information gaps more quickly, create deeper customer connections at scale and improve customer outcomes.
AI in Customer Success: Refining Customer Face Time With AI-Driven Customer Insights
Forrester’s "Predictions 2024: Tech Leadership" report estimates AI will free up to 50% more time for employees to engage in creative problem-solving, driving customer-centric innovation and creating unprecedented business value. Imagine giving a CSM half their day back — how could they use that time to deepen connections with customers, their No. 1 priority, and come better prepared with data-enriched information for their next conversation?
You could argue that delegating time-intensive tasks to AI-powered tools frees you up to engage with more customers on a human level. At least 79% of customer service leaders think so as they plan to invest in more AI capabilities over the next two years. You not only gain the time back to meet with more customers, but CS teams can spend their energy where it really counts, which is on account strategizing and better solving customer problems.
With the help of AI-powered predictive analytics solutions, they can more quickly spot trends among customer segments that indicate customers are happy, healthy and likely to renew. AI solutions can synthesize account history and context to better equip the CSM with sensitivities and areas to focus on with customers. Take product usage trends, previous account notes, support engagement, or customer satisfaction scores (CSATs). CSMs deepen their knowledge of a particular customer’s behaviors and performance with your solution. Hence, the pressure is lifted off of your CS teams to manually sift through mounds of data and steers follow-up conversations to be based on the most recent and relevant information.
On the other hand, AI can also be an asset for building predictive models for other important KPIs like churn and customer risk and retention. AI can look for engagement dips, negative customer sentiment and correlated behaviors between customers that have churned, thus allowing you to spot risk earlier in the customer journey. Take advantage of AI’s ability to automate identifying high-risk customers before bigger problems escalate so you can make real-time decisions and quickly connect them with a human expert for a live conversation.
This data in turn should be leveraged by your organization’s entire go-to-market motion. Alerting sales to common problems within a customer segment identified by AI will give your account executives an advantage during the sales cycle. For example, account executives and sales engineers can use discovery calls and demos to show the prospect how the product solves a problem they might not even know they have. Delivering this proactively in a face-to-face conversation showcases your revenue team’s expertise and unlocks new value propositions for your potential customers.
Related Article: Dear CX Leaders: Are You AI-Ready? AI in Customer Experience Is Here
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Layering in Personalization at Scale
Threading in personalization in tactful ways throughout the AI-powered customer journey shows them that your CSM is putting in the work to get to know them behind-the-scenes. That can potentially unlock cross-selling and upselling opportunities. This can be challenging at times when you’re having many customer conversations, but — surprise, surprise — AI can help you personalize those interactions at scale.
Sure each customer has their own unique set of challenges, but many often have similar paths; this is where personalization comes into play. Create customer journey segments that speak to the needs of your different customers, which is a particularly valuable use case when you have multiple personas and want to streamline communications without detracting from the experience.
Personalized communications that tie new features and use case recommendations to a customer’s needs can go a long way in encouraging action. Feed the AI a customer’s previous interactions with the company — emails, chatbot conversations, meeting transcripts — so that CS teams can use that to build pre-call messaging tailored to the account’s specific needs. They’ll be able to make the value of the product clearer from that first touchpoint.
Stronger email templates and relevant product updates peppered with customer-specific data — like rate of adoption and how it’s driving a company goal or the launch of a new feature that they requested in a previous check-in meeting — can make all the difference between you and a competitor.
Related Article: AI in Marketing: More Personalization in the Next Decade
AI in Customer Success Isn’t a Perfect Science
We talked about how AI can help CS teams create personalized journeys at scale that boost retention while streamlining focus and saving time that they can dedicate toward the customer. Even still, AI isn’t a cure-all for CS, and knowing when and how to apply it is only the first consideration. AI is begging us to think critically about how we can work together with our CS tech stacks to redefine productivity and impact, and that challenge intrigues me.
Remember that AI-generated analysis and content is only as enriching as the data to which it has access. This makes sense in theory, but putting data cleaning into practice might be challenging for teams. The expectation of the CSM to be an expert who can find inaccuracies in the raw data comes with a learning curve. There's also an expectation to continuously retrain your technology and team since AI constantly learns based on the data your team feeds it.
And, of course, AI isn’t a substitute for the human oversight and touchpoints your customers require. There should always be a human check on any insights gleaned from AI, from data and email copy suggestions to everything else in between. You don't want to passively repeat or pass on information that you yourself don't feel confident in. My rule of thumb: use AI to get you 80% of the way there, but leave the final 20% to the human eye, ear, and voice.
Related Article: AI Customer Experience Solutions: Using Emerging Technologies
Enhancing the Customer Experience
CS depends on empathy, understanding and intention — all attributes AI can’t replicate, but it can augment. AI in customer success can work alongside your team to increase authentic connections with customers and provide a better experience with your product or service. Implementing AI strategically can result in higher productivity and better outcomes, giving CS teams the freedom to focus on high-impact work crucial to the business. That is absolutely critical in this era of business, where efficiency and ROI are the real measures of success.
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