Two Ways to Improve Customer Support Efficiency｜Use of AI and KPI Metrics
Many call center managers and BPO personnel consider improving the efficiency of customer support operations to be a challenge. In order to solve problems such as frequent errors when responding to inquiries, low user satisfaction, and high operating costs of support centers, it is essential to improve the efficiency of customer support operations.
In this article, we will consider the following two tools to improve the efficiency of customer support.
- AI
- KPI Metrics
Communication Business Avenue has been helping some of the world’s largest companies to integrate call center systems and digital communication tools for the past 16 years.
The Role of Customer Support
Why should you be serious about improving the efficiency of your customer support operations? Because the role of customer support is changing.
Traditionally, the role of customer support has been to answer customers’ inquiries. Whether it was before or after the purchase of a company’s product or service, the role of customer support was to answer customer questions.
Today, support operation teams are expected to do more than in the past.. Customer satisfaction is becoming more important, so the time it takes to answer inquiries, the accuracy of the response, and the channel through which the response is given are becoming more significant. In addition to responding to inquiries, they are also expected to communicate personalized messages and information.
Improving operational efficiency is essential to fully fulfilling the customer support role demanded by today’s customers.
Challenges Support Operations Face
More Inquiries Than Usual
During the height of the pandemic, the number of inquiries to customer support increased significantly as people spent more time at home, with greater opportunities to make purchases on e-commerce sites. This trend looks set to continue post-pandemic, with one study by Forrester reporting that 53% of support teams have seen an increase in support queries since the start of the pandemic.
Adaptation to Work at Home
Many resources are being allocated to prepare for and operate the introduction and maintenance of work-from-home programs. This may result in a shortage of personnel who can respond onsite.
The volume and content of inquiries that can be handled by operators working at the office and those working from home may differ. As a result, on-site operations may become more complicated.
Remote training
Another impact of the pandemic is that staff training is often conducted remotely. This requires new training preparation tailored to the remote environment and prolongs training time because the amount of content that can be taught at one time is limited. Both the teaching staff and participating operators spend less time onsite, which may reduce work efficiency.
Different Procedures for Different Operators
If the procedure for answering customer inquiries differs from operator to operator, efficiency will suffer. If the procedures are not the same for veteran operators and newer operators, confusion may also arise in the field. This could also result in frustration for customers as each time they inquire, the response may be different.
Information Is Not Shared
If information on frequent questions, more complicated issues, and complaints is not shared, it becomes much more difficult to respond to inquiries efficiently. When product knowledge is not shared, this can lead to confusion.
Is a Contact Management System Necessary for Customer Support?
A quick way to improve operational efficiency is to use an inquiry management system. If you are using Excel to respond to inquiries, you can easily streamline your operations by implementing an inquiry management system.
If you already have an inquiry management system in place, you can improve efficiency by devising ways to use the system. It should be noted that if the system is used incorrectly, operations will not become more efficient.
The best way to use inquiry management systems to streamline operations is to utilize AI and KPI analysis functions.
Two key points to improve the efficiency of customer support operations
In order to improve the efficiency of customer support operations in today’s world, utilization of AI functions and KPI analysis are indispensable.
Let’s take a deeper look into why AI functions and KPI analysis are necessary.
AI
AI capabilities are necessary because they help streamline today’s increasingly complex customer support operations.
A recent analysis by Gartner, Inc. states the following:
“By the end of 2024, 75% of organizations will move from piloting artificial intelligence (AI) to formal operationalization. As a result, streaming data and analytics infrastructure will increase five-fold.”
June 2020 Gartner, Inc.
AI speech processing and natural language processing (NLP) technologies have advanced significantly in recent years, and AI can be used to efficiently analyze the vast amounts of data gathered by customer support.
The data to be collected and the ability to analyze it will be five times greater than before. Incorporating AI into an inquiry management system can improve operational efficiency by 25%.
The introduction of AI into customer support will continue to be a trend in the future.
Advantages of Introducing AI
There are three main advantages to implementing AI for customer support.
- Automation of customer support operations
- Operator support
- Predictive analysis
The following is a brief explanation of each of these benefits.
Automation of customer support operations
Customer support handles a vast amount of data. This includes customer data, inquiry information, and knowledge. AI can automatically organize the data that grows every day.
AI automates the work, reducing AHT time and human error.
Operator Support
Operators who respond to inquiries can spend a lot of time researching. They may have to search through past FAQs or manuals to find the appropriate response. Or they may have to search through past emails with similar questions.
With AI, however, it can tell you what information and search terms you need, saving you time in your research.
Predictive Analysis
AI is good at making predictions based on vast amounts of data. It analyzes data on a daily basis, such as each operator’s ability to handle tasks, personnel management, and the topics most frequently asked about. It can then predict the expected future inquiry topics and busy periods.
AI’s predictive analytics can help improve customer satisfaction.
For example, let’s say that at some point in time there is a spike in return inquiries. AI would then predict that keywords such as “returns,” “refunds,” and “product number” would continue to increase and alert you. With AI alerts, the manufacturing department can make necessary improvements to the product and improve customer satisfaction.
KPI Metrics
Let’s now consider the second key to improving the efficiency of customer support operations: setting KPIs.
Setting KPIs promotes customer satisfaction and enables the company to make progress. Why is this? An Accenture study points to the following: “45% of customers say they would pay more for a product if it guaranteed a higher level of service”
Insights from MARCH 23, 2016 study by Accenture:
- Customers will pay more if you set KPIs and provide a high level of service.
- New customers will increase and company revenues will rise. In addition, customer retention and churn rates will improve.
For customer support, however, customer growth is a time for caution. This is because as the number of customers grows, the size of the customer support team grows. The larger the support team, the harder it is to maintain quality service.
If you have KPIs in place, you can continue to provide quality service. You can continue to promote the interests of your customers and the progress of your company.
What KPIS Should You Set?
Here are 11 KPIs that international customer support companies have established.
- CSAT
- Conversation Review
- Internal Quality Score
- Reply per conversation
- Response Time
- Average processing time
- Conversation Volume
- Conversations per operator
- Conversations in progress
- Cost per conversation
- First-time resolution rate
Each KPI is explained below.
CSAT
CSAT stands for Customer Satisfaction. It is an indicator used to determine the degree to which customers are satisfied with a company’s services and products.
The most common way to measure it is to ask the customer, “How would you rate the support you received this time?” after the customer has been served. It is often measured with pictograms like the one above.
Conversation Reviews
Conversation reviews are sometimes referred to as support QA or internal reviews. It is a measure of customer support within a company.
Conversation Reviews and CSAT can be used to provide an analysis of the support being provided. For example, if conversational reviews are high and CSAT is low, there is a risk that the company is providing self-serving support. Conversely, a high CSAT and low Conversation Review may be a sign that the company is not fully aware of customer needs.
Internal Quality Score
The Internal Quality Score is a more detailed KPI than the Conversation Review. The following items are measured as Internal Quality Score
- Tone of the operator’s speech
- Technical knowledge
- Usage of help materials
Target values for each item are set and attached to each operator as positive labels for those above the target and negative labels for those below. Internal quality scores allow you to identify operators who need focused training and support.
Replies Per Conversation
Replies per Conversation is abbreviated as RPC. It is a KPI that measures the number of times an inquiry is resolved in a single conversation.
To measure RPC, one must review 20,000 to 70,000 conversations in a month. Due to the sheer number of conversations involved, some call centers randomly extract a few conversations and determine an average value.
Modern call centers, however, are letting AI measure and analyze all conversations.
The lower the RPC, the more quickly the inquiry was resolved. One study reported that “75% of companies that reported an improvement in FCR (first contact resolution rate) over a 12-month period also experienced an increase in customer satisfaction.” Hubspot’s analysis also shows that a 1% increase in first time resolution rate is associated with a 1% increase in customer satisfaction.
First Response Time (FRT)
Response time, or FRT, is the time it takes for a caller to get an initial response.
According to some statistics, 53% of customers who call believe that 3 minutes is a reasonable amount of time to wait for a response from a support representative. However, in the case of live chat, the response time expected by customers is shorter: 92% of customers were satisfied with an average response time of 1 minute and 36 seconds. In other words, customer satisfaction is lower if they have to wait longer than 1 minute 36 seconds.
An inquiry management system with AI capabilities can help operators reduce response time.
Average Handle Time
Average Handle Time (AHT) is the average time it takes to process an inquiry. In the past, it was the time per call, but now the indicator has evolved with the shift to multi-channel inquiries.
Traditionally, the only channel for customers to inquire was by phone. Therefore, AHT measurement was only required from the beginning of the call to the end of the call. However, now that email, video, and chat are included, traditional measurement methods are no longer applicable. The current AHT methodology measures the time from the time a customer hears a query to the time it is resolved.
Conversation Volume
Conversation Volume is a measure of how many queries are handled by the entire location. It includes not only the number of calls handled, but also the number of emails, SNS, chats, etc.
By measuring the conversation volume of the entire base, it is possible to visualize the busiest times of the year and efficiently manage personnel. Especially during periods when the volume of conversations is high even beyond business hours, it may be necessary to temporarily operate 24 hours a day.
Conversations per Operator (Conversations per Agent)
This indicator measures the number of inquiries handled by each operator.
The timing for measuring conversations per operator should be at times when averages are easier to obtain. Avoid holidays and busy periods. Furthermore, the number of inquiries that can be handled varies depending on the task for which the operator is responsible. Averages should be taken within the same team.
Current Open Conversations
Current Open Conversations include the number of tickets currently being handled and the time since the ticket was opened. It is an indicator of how efficiently each ticket is being processed.
If the hold time is longer than average, the operator may be having some problems. Leaders can provide knowledge and advice on how to prioritize tickets.
Cost Per Conversation (CPC)
Cost Per Conversation (CPC) includes operator salary, benefits, hardware costs, software costs, and other overhead.
When calculating cost per conversation, it is important to include only the time spent by operators responding to customer inquiries. It does not include operator training time or time spent entering history.
First Contact Resolution Rate
The first contact resolution rate is the percentage of customer issues resolved on the first response.
The larger the size of customer support, the lower the first contact resolution rate tends to be. Ideally, the First Contact Resolution Rate should remain the same even as the number of locations grows.
What is an inquiry management system that helps improve the work efficiency of customer support?
There are two features of inquiry management systems that help improve the efficiency of customer support operations.
- AI functionality is available
- Easy to analyze
AI Functionality Is Available
If an AI function is installed, the system automatically categorizes customer inquiries and assigns them to the most appropriate person according to the content. It also analyzes the content of the inquiry from the body of the e-mail and automatically enters it into the operator screen. The system also displays the knowledge necessary to reply to the customer’s inquiry, as well as similar responses from the past, so the response can be completed in a short period of time.
Easy to analyze
Ease of analysis is another important feature. No matter how detailed KPIs are set, if they are difficult to analyze, operations will not become more efficient.
Choose a tool that allows you to visualize your KPIs with bar graphs and scatter plots, etc. The ability to set and change KPIs with no code also makes operations easier. Tools that allow drag-and-drop dashboard reordering are also popular.
Final Thoughts
There are two key points to make customer support more efficient: use of AI and setting KPIs.
If you already have an inquiry management system in place, then you should utilize AI functions and analyze KPIs. If you want to implement an inquiry management system in the future or are considering replacing it, choose a product that has a full AI and KPI analysis function.
If you make your customer support operations more efficient, customer satisfaction will improve and your company will grow more.
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