As customers and businesses become more familiar and comfortable with artificial intelligence, the conversations around AI have become less “pie in the sky” and more along the lines of how it can be actually used. There’s still plenty of speculation concerning robot coworkers and what the potential of machine learning will bring to analytical insights and chatbots won’t end anytime soon, but there’s lots that can be done with today’s applications of AI technology.
Many of those applications are prevalent in customer service, but what about in relation to the entire customer experience? As much as businesses want to implement artificial intelligence, experts advise not to start using AI for the sake of having AI. The argument is that artificial intelligence is most successful when it enables overall better customer experiences.
That’s why analysts are encouraging the adoption of AI tools with a calculated approach, one focusing on the “how” and “why” an AI tool can provide a better overall customer experience. Forrester, for example, advises that “having a successful AI-driven customer service or sales program will depend on the processes that support a blended AI approach. Humans will play a critical role in the ongoing optimization of AI.”
For those looking to be ahead of the curve in providing AI-based customer experiences, here are five ways it can be leveraged right now:
1) Self-service that’s more efficient for help-seeking customers
Many customers would rather help themselves instead of interacting with support agents or a chatbot, which leads to them to dig around for a solution on their own. The self-service process can be borderline tedious when it entails a combination of a Google search, navigating an online help center for a relevant article, and confirming that the article’s solution adequately addresses the issue. Some customers, especially the non-tech-savvy, may find that typical kind of self-service arduous and not exactly fitting for a smooth CX.
Recent innovations in artificial intelligence can both alleviate the hassle of customers searching for help articles and ensure they’re given the right information to solve their problem. AI that utilizes machine learning and natural-language processing (NLP) is capable of learning which help articles can best solve a customer’s problem and recommending the appropriate article to the customer. Customer experience leaders can determine where it makes the most sense for customers to encounter this kind of automated self-service—that may be in front of a help center, at a critical point in the buyer’s journey, or on a mobile website or application.
2) Content that’s better tailored to the customer’s needs and issues
Personalization is a major part of the customer experience, and businesses are seeking ways to make every point of the customer journey more personalized. One method is through better help articles: as products and services become increasingly complex, support organizations are finding it difficult to keep their help articles relevant and up-to-date.
If customers are bouncing quickly from a help center or from the articles within it (or if they mention in a follow-up interaction that the article failed to help them), it’s likely that the content wasn’t adequately tailored to their need or issue. Few things are as frustrating to a customer as unhelpful support content, and there aren’t a lot of intelligent, proactive ways to prevent bad content from being published.
Luckily, AI can support the creation and actualization of better tailored content for a specific customer base. Deep learning models can catch the common words and phrases related to specific issues found in support tickets and make tactful recommendations for optimizing help center content.
Here’s an example: if customers are submitting support tickets with the subject “change my password”, the AI will recommend appropriate editorial adjustments to the related help article entitled “How to update your login credentials”. The article can be changed to reflect how customers communicate their issues and make the content easier for them to find and understand. By providing a support organization with insights into their customers’ issues and recommendations for communicating the solutions to those issues, content managers can do their part in creation more personalized experiences.
3) Customer support agents who are more efficient
How many times have you heard a support agent say “Let me check on that for you” because they simply don’t know the answer to your question? Support agents generally spend 20% of their time on the hunt for product information—this can draw out a support interaction and have a negative effect on customer satisfaction.
The same artificial intelligence that automates self-service suggestions to customers can also be utilized by agents. If a customer is locked out of their account from too many failed password attempts and desperately needs access, they’ll usually submit an urgent support ticket. But if the agent isn’t familiar with the internal process for unlocking an account, they’ll need to review the appropriate internal documentation first—that is, if they can find it.
The right AI tool can analyze a support ticket and recommend the relevant help article from within the company’s knowledge base. This is all done directly in the agent’s interface—by enabling agents with the right information when they need it, they can efficiently address their customers’ issues right after they arise.
4) Customer engagement that’s enhanced with data-driven suggestions
Our digital activities and interactions result in tons of data for machine learning algorithms to utilize; it’s essentially the fuel for AI’s predictive proficiency. Ever wondered why machines are so good at answering questions like “what’s the fastest route for my commute home at 6:00 PM on a Thursday?” By aggregating numerous trips taken by those who’ve traveled a similar route, the AI can predict and determine a well-researched, real-time recommendation for getting you home quickly.
Similarly, the data recorded from customer service interactions can be utilized to improve CX. By assessing the details of previous support tickets, an AI tool can predict whether a current support interaction will lead to a positive or negative customer experience, resulting in an accurate customer satisfaction (CSAT) score prediction. The details that impact CSAT scores might include the length between the first reply and subsequent response times, how much effort is put into resolving the customer’s issue, and if text responses with similar wording have resulted in satisfied customers. This is the kind of AI application that doesn’t replace agents (like a chatbot), but instead augments their efforts to deliver better customer experiences.
5) Organizations that have more time to innovate on the customer experience
Of course, one of the most compelling value propositions of automation is that it frees up time for humans to focus on other high impact efforts. Artificial intelligence is already enabling businesses to improve the customer experience in ways they haven’t been able to before.
Dollar Shave Club, the on-demand shaving razor service, benefits from the extra time that Zendesk’s Answer Bot affords them to optimize their customers’ experiences. With the time they’ve saved through automatic ticket resolutions, they’ve been able to:
- Create a “Help Center Task Force” that ensures their help articles are constantly relevant and up-to-date for their self-serving customers (and to supplement Answer Bot’s recommendations).
- Increase the amount of time that they offer live chat support throughout the day, permitting them to offer more real-time assistance without increasing headcount.
- Launch an internal monthly e-newsletter on customer engagement insights that highlights trends and keeps their agents better informed on their success metrics.
- Find bandwidth to launch a “Test & Learn Team” to try out new email messages that might improve their members’ customer experience.