Using AI Analytics to Improve the Customer Experience
Artificial intelligence (AI) has been used to make customer service processes easier —but how can the analytics provided by AI supply additional benefits for the customer experience?
From streamlining phone calls to providing directions for self-service content, AI has helped customer service organizations maximize their success for many years. While chatbots immediately come to mind as a common AI customer service tool, there are a myriad of ways that AI can assist in the world of CX.
The value that AI provides begins with the customer service agent, who can use AI tools for training and on-the-job experience. The benefits then extend to the customer, who receives faster, more effective service. And finally, the broader organization, who benefits from cost reduction and improved customer satisfaction.
Across the entire customer journey, AI identifies ways to improve customer service outcomes, and provides tools that support and empower the customer service agent to deliver outstanding service. Though for a growing customer service organization, AI’s greatest benefit may come in the form of the data and analytics it generates along the way.
The key to improving customer service lies in testing, training and pivoting operations where necessary to best serve the customer’s needs. With AI supplying a steady stream of customer data that is ready for interpretation, service orgs can identify a wealth of opportunity to improve processes, create more valuable content and ultimately, increase the value of CX.
Here, we’ll explore 4 ways that AI analytics can be utilized to support the customer, the organization and the agent throughout the entire CX journey.
4 Ways that AI Analytics Can Be Used to Improve CX Outcomes
1. Streamlining and Improving Recruitment
Even before a customer service agent starts their journey with a company, AI can assist in streamlining processes and developing talent. In the hiring stage, AI can identify more qualified candidates, filter for certain skills or levels of experience and even scale up hiring quickly during times of peak demand.
If a company is looking for candidates who speak a certain language, have experience with certain industries or are available to work at certain times, AI tools can be trained to scan for and flag applications or resumes that indicate those qualities or skillsets. From there, the candidate pool can be narrowed significantly, often going from thousands of applicants to hundreds or even dozens. This gives hiring managers a more accurate, direct path to hiring the talent they need, whether for seasonal staffing or general team needs.
With more qualified candidates in their queue, companies are in a better position to retain talent, which leads to improved performance across the board. Companies using AI technologies for recruiting can find confidence in knowing that applicants were well-matched with the role at hand, and candidates can find confidence in knowing that the job is a good fit for their experience and future goals.
2. Developing More Effective Training
When it comes to training, AI-driven analytics can be used to discover and create relevant trainings based on the data that is sourced from calls and support interactions in real time. Analytics like call drivers, call time, and resolution time can be turned into opportunities for training guides, videos, presentations, courses and more. Using these statistics as the baseline for educational opportunities empowers customer service agents to improve upon their skills and thrive in a culture of life-long learning.
AI-based trainings can also take the form of call simulations, which produce a practice environment for employees. The knowledge and comfort gained from these simulations creates confidence and quicker paths to resolution when agents encounter similar situations in their day-to-day work.
3. Identifying Opportunities for Self-Service Content
According to Zendesk, self-service content is a popular customer service approach for many companies, with 69% of customers trying to resolve their customer service issues on their own before making a support call. This makes self-service content a clear winner for brands aiming to improve CX seamlessness in the age of multiexperiences and an omnichannel approach.
AI-driven analytics can help customer service organizations identify patterns in common customer issues that may warrant the creation of a self-service content piece. These content pieces can take the form of a Q&A guide, video or other form of content, and can help customers to address questions they may have upfront. With the ability to solve problems on their own, the customer’s support journey becomes more streamlined, and call volume is reduced.
4. Personalizing the Customer Experience
The rise of multiexperience has reminded us of customers’ changing needs during their journey with a brand. Personalization continues to serve as a key differentiator between a good experience and a great experience, and AI can help in personalizing the customer service journey.
Data and analytics gathered during support interactions can be stored and surfaced to an agent or chatbot when they are interacting with a customer in the future. The number of calls made, the service issues discussed, and other data points will give service organizations a fuller picture of the customer. Recommendations for self-service content or other helpful tips can be tailored to the customer’s individual journey, creating a stronger connection with the brand.
As customers interact with chatbots and other AI technologies across their service journey, those technologies will learn from how customers respond and how effectively issues are resolved. This ongoing feedback gives the chatbot the opportunity to better support the customer and the CX team, which in turn fosters greater satisfaction and continued loyalty from the customer.
The Continued Value of AI and Humans Working Together
As customer service organizations progress in their digital transformation and adapt to the concept of multiexperience, AI will continue to be an invaluable tool. Beyond making tasks easier, though, AI creates a feedback loop that follows the customer throughout their entire journey with a product or service, giving companies crucial insight into how they can improve.
When supported by AI analytics, both the customer service agent and the greater organization can meet the customer where they are and anticipate the needs they may have in the future. This allows the customer journey to thrive from the point of purchase all the way to retention — and gives a shining example of how AI and humans can work together for the benefit of the customer experience in every stage.
To learn more about how AI will change CX, download our 2022 CX Trends Guide.