How to Improve Productivity in Customer Support With A.I.
By: Kavita Ganesan
Tags: Artificial Intelligence, Technology, UX, Productivity, Business

Figure 1: Sources of customer support queries

Companies receive support inquiries from various channels. This may include emails, support tickets, tweets, chat conversations with customer support representatives (CSRs), chatbot conversations and more.

This is a lot of data that you are dealing with and it’s mostly unstructured and scattered in nature, making it that much harder to manage.

The question becomes, how can you leverage all this text data to improve speed in responding to customer support inquiries or reducing the number of tickets that are being opened?

This can partly be done with automation using Natural Language Processing (NLP) and Artificial Intelligence in general.

This article will give you an overview of 5 areas in the customer service workflow that can benefit from A.I. / NLP based automation. This is not an exhaustive list, but a list that applies to most customer support teams at medium to large organizations.

#1 Recommend best answers

When CSRs try to respond to a customer problem, they can be overwhelmed with identifying the best answer from the pool of possible answers. What they need is one answer that will address the customer problem.

Some companies maintain an exhaustive list of problems and corresponding answers which the CSR has to search through, sometimes even manually. This can be painfully slow and draining if you have to perform a search for each and every question.

NLP can be really useful here by recommending the best answers given a support inquiry. It becomes even better when the answers have an associated “score” that indicates the likelihood of a particular answer solving the customer problem.

Using this approach, instead of explicitly performing a search, the CSR is now having information pushed to them automatically, preventing a break in their workflow.

With this, the response time can be much faster, which also means, CSRs will be able to handle a larger volume of support issues. What’s even nicer is that the CSRs will not be overwhelmed by the end of the day due to the number of searches they have to perform.

#2 Suggest historical threads

While some support questions can be easily answered with the recommended best answers, others can be complex, requiring extended research.

One way for CSRs to solve complex issues is by looking into related historical threads (which have been successfully resolved), and understanding how those issues were resolved. With this, the CSR will be able to better resolve the issue at hand or form a more complete answer to the support question.

With NLP and Text Mining technologies we can automate this process by recommending related historical threads for any given support inquiry. This saves CSRs from having to conduct various searches, contact peers and their manager for help on an issue.

The benefit of surfacing related historical threads is (a) the potential decrease in response time and (b) reducing follow-up support questions as complex issues are resolved in fewer interactions.

#3 Group questions to limit context switching

As we all know, context switching can be hard. Going from resolving issues related to signups to billing and then back to signups can be a productivity killer. According to Bud Roth, author of Be More Productive: Slow Down, distributing your energy over a wide variety of tasks can dilute your effectiveness the same way interruptions do.

By grouping similar support questions, CSRs can address similar problems in chunks, where the knowledge bank that they’ll have to tap into and the pool of potential answers are related.

Figure 2: Grouped customer support questions

With the use of clustering techniques in Natural Language Processing and Text Mining, we can automatically group similar questions as shown in the example in Figure 2. Notice that the first group of questions is all about adding a profile picture.

The benefit of doing this is that it maintains the same train of thought in resolving issues. In some cases, the solutions may be identical, while in others the CSRs will know what steps to take to resolve an issue while everything is still fresh in memory. By limiting context switching you can expect to see a reduction in response times.

#4 Auto-route Questions Based on Expertise

Support questions come in all shapes and form and customers may express the very same question, quite differently. For example, the questions in figure 3, are all related to adding a picture to the customer’s profile, with a slight difference in how it’s structured.

Figure 3: Similar questions, different expression

By classifying each incoming question to a predefined set of categories with text classification methods (e.g. profile, picture, attachment), you can use these categories to route the questions to agents who are best at handling those topics. Some CSRs may be highly qualified at handling certain topics more than others.

By intelligently routing questions to relevant expertise, you’ll be increasing the productivity of CSRs as they are not spending time learning how to address support issues that are out of their wheelhouse.

#5 Auto-prioritize support threads

A few companies that I’ve worked with have mentioned addressing support threads in the First in First Out order (FIFO), meaning the oldest support threads get addressed first. Other companies, manually assign priority based on the severity level of the issue.

Don’t forget, that not all customers are equal and not all problems deserve the same level of attention. By addressing threads in the FIFO order or assigning priority based on severity alone, you are missing out on the opportunity to retain your highest-valued customers. If you are spending your time solving 100 low priority problems for low-value customers before serving your most valuable ones — it’s time to think about making changes.

While you can start serving your VIP customers first, with A.I. based automation, you can combine various factors into prioritization. For example, you can develop a model that combines the priority of historical support threads with the value that each customer brings (e.g. customer lifetime value) to assign priority to new support questions. This will ensure that your highest value customers with high priority issues get served promptly and by your best CSRs.

How to Get Started?

If you are a leader in customer support or a product manager, you may be wondering where and how to get started? I recommend that you start by listing down your most inefficient processes. What’s taking you the most time?

Is it that the search for answers or question routing has gone bad? Once you know what’s hurting, the next step is to determine if the inefficiency can benefit from Machine Learning, Text Mining and NLP automation.

Trying to solve all problems at once will set you up for failure as there’ll be too many changes in your workflow. In addition, some of the automation can be at odds with each other.

For example, by auto-prioritizing threads, grouping questions may or may not be effective. Optimizing all at once also prevents your ability to measure success. You’ll not know if the reduction in response times was due to the recommendation of the best answers or if it was due to the auto-prioritization of threads.

Despite the hype, not all problems benefit from A.I. and can be resolved with other standard solutions or simple changes to how your software works. In cases of uncertainty, you can always get in touch with me for a recommendation.