Reinventing Customer Service with AI as a Growth Engine

Anyone who has ever called customer service knows how important it is to get your problem resolved in a timely and efficient manner. You do not want to suffer long response times while dialing through options, deal with disengaged support staff, or get transferred from one agent to another, with long hold times in between.

Businesses understand how painful bad customer service can be. But because improving it always involves additional time and resources, they continue with their existing systems. Not every company can afford to scale and transform its support operations, yet maintaining the status quo becomes a never-ending cycle of high employee burnout, high turnover rates, and constant onboarding overhead.

As the world becomes more digitized, organizations can no longer leave things as they are. Today, businesses can leverage artificial intelligence (AI) to reinvent their customer support and transform it into a growth engine for the entire enterprise.

If your business communicates with customers via email, it is a given that some employees have to manually manage the stream of requests. They need to read emails, categorize requests, and route them to support reps. Only then are the requests resolved.

What if this work can be done by AI? Through a combination of natural language processing (NLP), sentiment or intent analysis, and case classification, AI can categorize, sort, and route incoming emails. Requests are prioritized and sent to the most relevant department or support reps. This enables businesses to reduce wait times for customers while focusing support reps' efforts on high-priority requests instead of making them muddle through not-so-urgent tasks.>

Because AI operates in real time and can be scaled without adding support reps into the workflow, it enables businesses to optimize costs and increase the satisfaction of both customers and support staff.

Phone calls are trickier. No matter how advanced your customer support automation is, some calls will be routed to support agents. Given that most companies rush new hires through training and quickly put them to work, your reps might find it hard to answer some customer questions. Unhelpful customer support is bad for any business that wants to keep customers loyal and happy.

AI can make a huge difference by supplying support reps with FAQ articles as they type in a customer's request. Having all information at their fingertips in seconds, support can resolve more complex customer requests quickly. Recommendations are powered by AI's ability to find similarities in data and understand natural language, regardless of how requests are typed into the system.

AI can also predict next-best action based on customer profiles, preferences, and predefined rules. This gives support service representatives a powerful tool to not just address customer issues, but to keep customers in the loop through new offers, targeted promotional deals, and discounts.

If your customers prefer a self-service approach, AI-powered chatbots are the way to go. All you need to do is train AI algorithms that can accurately analyze requests to understand their meaning and intent, provide scripted answers, recommend FAQ articles, and route the request to a human agent if it is too complex to resolve automatically.

Chatbots are a highly cost-efficient solution, because they free up agents' time to resolve more complex queries. On top of that, they offer a viable alternative to customers who feel uncomfortable calling your support center but do not want to waste time waiting for an email reply.

Implementation of AI starts with adopting the right mindset. Company leadership should fully understand the ins and outs of the upcoming AI transformation, its objectives, timeline, key performance indicators, and cost. The best way to kick off AI transformation is to start with a simple proof-of-concept (PoC) project that can quickly deliver actionable results. Bear in mind that it takes time— from several months to a year—for AI to start driving value.

Aside from creating realistic expectations, it is crucial to check if the organization is ready technologically— data, infrastructure, in-house legacy systems, etc.—and what needs to be renovated and rebuilt. For example, if your customer service operation does not have sufficient data (e.g. emails, request messages, tables with resolution options, etc.) to train a custom NLP algorithm for request routing, a public database can be used, but the algorithm will need additional polishing and fine-tuning after deployment. Or, consider an outdated, on-premises legacy solution: it will be difficult to marry it with AI and machine learning algorithms; you might need to consider cloud migration and infrastructure transformation to ensure that your AI solution is fast, cost-efficient, and most important, easy to scale.

The management and cultural aspects of adopting AI are also important. AI should be integrated into your operations to start making a difference. But your non-IT employees might not understand the benefits of AI, so you might need to spend some time explaining the value of AI, educating your staff to use AI in their work, and reorganizing operations around AI as an integral component.

Just imagine that your business has released an AI-powered request routing system. This means that support agents who are used to manually sorting through emails no longer need to do so. They are now free to move from low- to high-value work, from navigating through the inbox swamp to addressing more customer requests by AI-assigned priority. You should be prepared to make necessary adjustments like staff retraining and implementation of new procedures.

Finally, you should bear in mind that any AI solution is a work in progress and should be maintained in production. No matter how accurate your machine learning algorithm is, its predictions will become less accurate as time passes due to changes in data. To avoid degradation in production, you should have engineers in place to monitor your ML model and to retrain and fine tune it when the need arises.

If your organization manages to nail the cultural, technological, and operational aspects of adopting your first PoC, it will be much easier to introduce new AI solutions and scale existing ones across departments. More AI means more customer support tasks done automatically, resulting in better-managed employee workloads, reductions in attrition and costs, and faster, more efficient customer support. All these benefits translate into customer satisfaction, higher staff retention, and accelerated business growth.

Almir Davletov is a technical account executive at Provectus.