How to use AI to improve customer service and drive long-term business growth How to use AI to improve customer service and drive long-term business growth
Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More In... How to use AI to improve customer service and drive long-term business growth

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

In a rocky economy, finding and keeping happy customers is critical to a business’s long-term growth. In fact, American businesses lose upwards of $83 billion due to poor customer service. Therefore, any technological advancement that improves their experience—while increasing your efficiency—is worth considering.

In the past decade, AI has transitioned from a freaky sci-fi concept to a tool working behind the scenes of our lives, to a mainstream topic of discussion. From a business perspective, it’s fast becoming the differentiator that businesses need to stand out from competitors when the landscape is tough.

Even if you’re not using AI to improve your customers’ lives, your rivals most likely are. Here are some key ways businesses can leverage AI to build a customer service experience that inspires loyalty and delivers value both for you and for them.

Customer service that’s fast and consistent

Speed is expected in customer service today. We’re used to getting answers to all kinds of questions with a few taps on our smartphones, and that extends to solving any problems we might have with the businesses we interact with. On average, customers expect companies to respond to a phone call within five minutes and an email within one to 24 hours.


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While AI can’t do everything humans can, what it can do, it does much faster than humans, and at greater scale. For example, AI can transcribe customer conversations in real time and identify sentiments associated with the customer’s language. From there, it can advise agents on how best to proceed based on models derived from thousands of other similar conversations.

It can also operate as a search engine, scanning knowledge bases and producing relevant answers to a customer’s queries—all without having to leave them quietly growing more impatient on hold. New AI models such as GPT-3 can learn these tasks at a truly astonishing pace.

Businesses can also use AI to run wide-reaching quality control checks. Whereas one supervisor might be able to listen to 20 calls a day at the most, AI can assess thousands of transcripts in minutes and flag moments that don’t meet the standards it’s been trained to recognize for a person to review.

In theory, a supervisor can do a similar job. They can listen in on a call, judge how it’s going based on their previous experience, and recommend the next steps to the agent while also making sure they are upholding the company’s standards. But a supervisor can’t sit in on every single call while remaining productive in their everyday tasks. AI, however, can be available to all your agents, providing real-time insights that improve customer and agent experiences and the consistency of your service.

Many businesses have customers whose needs don’t fall within call center hours. For example, banks and airlines need to be able to answer questions 24/7. As chatbots become more prevalent and sophisticated, there will be more self-serve options for these kinds of queries, allowing customers to find answers wherever and whenever they need them.

AI excels at this, and at scale, which lines up with the majority of customer service communications. As it improves, expect to see faster resolutions and shorter wait times.

Personalization with a purpose

We don’t just accept that our phones know everything about us; we expect them to. If I Google “Greek restaurants” and my phone shows me a list of places hundreds of miles away—or in Greece—I’m pretty frustrated (especially if I’m already hangry). When I’m texting, I get annoyed if my phone doesn’t automatically correct my most common typos. If I’m scrolling through social media, I’m confused if I see ads for products I have no interest in.

These are all examples of personalization generated by AI. In a customer’s view, a personalized experience is a good experience. 76% of customers feel frustrated when brands fail to deliver a personalized interaction—and 71% expect personalized service.

Imagine a scenario where a customer has to call customer service. The agent has all the information available about them and their account before they call, thanks to all the insights drawn from transcripts of previous calls and integrations between the AI and a CRM. The agent can even see a score for the account based on the sentiments of the various members involved across all channels.

That means the customer doesn’t have to spend the first five minutes of the call outlining their history and detailing previous issues. They can jump immediately to the current issue knowing that the agent has all the necessary past context. With all that information and support from the AI, the agent needs less time to find a resolution, improving the customer experience.

Another hope is that chatbots powered by advanced AI like ChatGPT will one day be able to deliver the same level of personalized experience as human agents. This will encourage customers to take their basic queries to bots, allowing more complex problems to go straight to the front of agents’ queues.

Beyond making every customer feel like a VIP, personalization improves the quality and speed of the service they receive.

Being the proactive party

Until recently, the challenge was getting AI to the point where it can provide real-time insights. The next stage has been building AI that can make predictions that help businesses forecast customer outcomes. This is particularly useful in economic downturns when businesses are looking for data that can give them a clear indication of their financial situations. It can also help them prioritize resources accordingly.

Closing deals and retaining customers are crucial to surviving a recession. In financial services, a 5% increase in customer retention increases profit by more than 25%. And in apparel, the average repeat customer spent 67% more in months 31 to 36 than in their first six months as a customer, indicating that long-term customers are more valuable than new customers.

AI can predict both purchase intent and churn risk to an impressive degree of accuracy. This means that sales teams can increase their likelihood of closing deals by concentrating on the strongest leads rather than spending time chasing what ultimately turn out to be dead ends. Meanwhile, AI can also identify trends that indicate a customer is unlikely to renew or is about to cancel. With this information, businesses have the opportunity to identify the issue and fix it preemptively.

It’s been AI’s ability to mine data extremely fast that has made it so useful. On top of that, it’s now able to make predictions about that data that can help us make informed predictions and forecasts with direct implications for revenue.

There have been a lot of positive developments in the AI space that are expanding the ways people and businesses can benefit from this technology. A lot of it comes down to data: where we can find it, how it’s processed, and what we can do with those insights. Businesses need all of those pieces to get the most from AI. In customer service, it translates to what we know about the customer, how we access and analyze that information, and how we use it to improve their experience.

It used to be that AI was used on historical data, which was useful but retroactive. More recently, it’s become possible to use AI to make real-time decisions. The next generation of AI is forward-thinking, using data to make predictions that humans can’t come up with nearly as quickly. As with any tool, AI is most effective when we understand how to use it and its strengths and limitations.

With technology like GPT-3, we’re just starting to find out what those look like in the future.

Dan O’Connell is Chief Strategy Officer at Dialpad.


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