Some people may fear that artificial intelligence is becoming increasingly dominant and will start taking away more and more jobs, leaving people unemployed. What we really should be thinking about is how we ensure that we automatize the jobs that could be done best by AI, leaving more time for humans to focus on their strengths.
In terms of customer service and call centers, there are a lot of agents all across the globe who are manually collecting data and making notes for call memos. If data entry would be done for them and the agents would be left to focus on communication, counseling, and customer service, how much better could that make customer service? Not only would the customer service win, but getting exact and automatized data would also mean more knowledge to back up business decisions.
In this post, we would like to shed light on the improvement possibilities that automated data collection could give for customer service, business decisions, and product owners.
Manual data entry and memos may not give sufficient data for analysis and it can affect the customer service negatively
When a customer calls a call center, the agent has to follow certain steps to make sure that the call is documented. There are many reasons behind that – like call separation into topics, making sure that the information is there for repeating calls, gathering data for business analytics, etc.
Each company has its own rules that they ask agents to follow when it comes to documentation and making memos. Some companies only ask for the agents to mark down the topic of the call and whether the issue was resolved, some require a lot more documentation.
The fact is that even when the agent is asked to write down the reason for the call, what they did during the call and how the issue was resolved, what ends up in the memo will vary based on the agents. Some agents tend to write short notes, others will write essays. Gathering data for analysis based on such notes is pretty difficult, as the content and amount of detail will be subjective, so there should be a better way. In the view of the agent, the more that they are asked to do, the more difficult their job is.
From a personal perspective, I have worked as a customer service agent for a few different international companies. The documentation rules varied, but it was always clear that the topic had to be documented together with a short description of the customer contact. All of that had to be done and saved in CRM before the customer contact had ended, meaning that documentation had to be simultaneous with the call. That made it much more difficult to keep the focus 100% on the customer. The time pressure was an issue for a lot of customer service agents, leaving them with either poor documentation or lower call quality.
How Feelingstream was born – from necessity
When conducting memos, some companies only request their customer service agents to define the reason or topic of customer contact. This is how Feelingstream actually got started as well – when Feelingstream’s CEO Terje Ennomäe was working for Bigbank and created a solution for registering customer contact reasons (read the story behind Feelingstream here).
For example, in the consumer loan business, the customer contact themes or topics may include:
- General information about the loan (conditions, interest rate)
- I’m applying and I have additional questions (documents, deadlines)
- Waiting for a decision to get a loan
- I got a positive decision, signing the documents, but I have a question
- Why was my decision negative? I’m not sure I understood the response.
- I want to change my payment schedule (partial or full repayment)
- I’m in debt, please help me try to fix this
- The information displayed on my credit file is incorrect
When creating such a list of reasons for customer service agents to choose from, the options come from data analysis of the contacts from the past. Having a drop-down menu with a list of options may seem like a good start for documenting, but it has its limitations.
When Terje was first starting this contact classification project, she added an option “other” for the agents to choose from as well – the first report showed that this was a mistake, as 80% of calls were categorized as that. It was a clear indication that contact classification needed to be done in some other way. Instead of relying on customer service agents, Feelingstream has created a more objective, smart technical solution to do that instead.
Feelingstream transcribes calls into text, meaning that calls can be analyzed, classified, and reviewed easily. The platform can gather data across multiple channels, so information from calls, emails, and chatbots can all come together for analysis. At first, the AI solution needs some teaching to get started. It needs training to understand the patterns and get the classification going – but with some work, the solution can be up and running quickly, providing the data fast and accurately.
What is the real impact of using Feelingstream’s solution?
Much more accurate classification of contacts, meaning that the classification data is more relevant and helpful for business decisions.
When calls are classified and there is a rise in calls on one topic – based on the previous example, let’s say topic 3 “Waiting for a decision to get a loan” – then it is clear that something should be done about it. It could be something that makes the process more efficient so that the customers get their replies faster. Another possible option would be to keep the customers updated and send them emails, advising how far along their loan process is and when they should be expecting a reply. Maybe it is some third or fourth solution or a combination that makes for the best business. All in all, knowing the reasons behind the calls and acting based on that data makes room for thought-out changes and business decisions.
Another example – data shows that there has been a decline in calls on topic 5 “Why was my decision negative? I’m not sure I understood the response”. Such a decline in contacts shows that the automated communication explaining the negative decision has been sufficient and the team can be proud of the clarity of the process.
From an agent’s perspective – if you take off the pressure to multitask, by having to find the right options in drop-down menus, and writing complicated memos, their work gets easier. The agents can focus on communication, active listening, and providing excellent customer service. If they know that the documentation will be automated and all they need to do is to actively listen and resolve customers’ issues, they can be greater at it.
Call length can decrease up to 25% – time won from not having to document calls manually. Agents can take more calls and be more focused during those calls.
Process leaders and product owners can get more exact data and therefore can make their solutions more effective. Regarding topic 1 from our example list “General information about the loan (conditions, interest rate)” – analyzing what the customers ask about specifically will let the product owner know where to add more information, what to make more clear and how to improve overall. When all of the information is available on the website, in all advertisements, campaigns, mobile applications, and every other communication channel, the likelihood of the customer call is minimized.
Bettering business processes based on actual needs and keeping up with the latest data, always thriving and working towards efficiency and clarity of processes.
Taking action based on understanding call reasons will also mean that First Contact Resolution (read our post about that HERE ) and customer satisfaction rates can go up.
Customer satisfaction rates could go up 10%.
Customer service agents that can focus on customers, will be more efficient and able to provide better service. Processes that are updated and created based on customer needs will be more transparent and clear. Every automatized process is a win for the company. If you wish to achieve all of this with the help of Feelingstream’s AI solution, please sign up for a live demo HERE.