Imagine you read a CSAT survey result that angrily calls out an agent for being unhelpful and slow.
You look closer at the conversation and you can see that the problem is the customer wants to update their password, but the agent wasn't able to help them out.
You kick off a new training around helping customers with simple tasks like this one, but the requests (and negative CSAT scores) keep flooding in. And, not just for this agent, but all of them.
This issue is driving 10% of your customer contact volume, so you decide to take an even deeper look by trying to change your own password, and discover it’s not the agents at all. There’s a bug in the process—customers aren’t receiving their password reset email.
You jump on with the product team and walk them through the benefits of fixing this bug: saving costs, saving agent time, and improving CX.
Below, we’re going to look at more benefits of doing regular root cause analytics in customer service.
We’ll also spin through an actionable 4-step process you can follow for your very first root cause analysis in customer service.
Getting to the root of reasons for contact in customer service is mutually beneficial for your team, your company and your customer.
We work with customer service teams of all sizes and company types, and their use cases for support conversation root cause analyses vary, too.
Generally, they fall into three categories:
By identifying the true root cause of support conversations and CSAT feedback, you’re able to better train agents and better equip them to quickly and diligently solve the issue.
For example, if you know that 5,000 tickets per month are about a specific, easily solvable issue, you can build a knowledge base article that your agents can send to the customer. A win for ticket resolution time.
CSAT feedback is also a valuable resource for agents. Root cause analysis of CSAT feedback, for example a topic analysis, reveals the true driver of the score and the associated sentiment.
With our customers, we help them identify the behaviours and words that agents use which create negative sentiments, this tech-led QA technique helps each agent know where to develop their skill set.
Getting to the root of ‘reasons for contact’ is the first step towards tackling those reasons.
For example, in a Support Insights podcast episode with Lauren Cunningham, Loom’s customer support lead, we learned how Loom brought their contact rate down from 4% to 1%.
Over the pandemic, Loom’s customer base exploded from 700,000 to 10 million! At a contact rate of 4%, that meant an enormous surge in ticket volumes.
Starting with a root cause analysis of their customer conversations, the Loom team identified FAQS, things users commonly struggled with, and particularly time-consuming issues. That analysis fed into their ticket minimizing strategy, like their new knowledge-base which receives over 1 million in traffic each month—significantly reducing the need for customer’s to reach out to an agent.
Bringing down costs in this way is top of mind for many support teams. Gousto shared in this interview, that their real-time support analytics:
“Significantly reduces the lead time to unearthing customer insights and sharing these across the business, which allows us to positively improve [metrics like retention and reducing customer contacts].”
In a separate interview, Gousto’s Director of Support shared how root cause analyses in customer service also helps them identify the most frequent ‘reasons for giving a refund’ to their customers. By prioritising fixes to these issues, the company significantly reduces the financial impact of refunds.
Many of our customers invest in AI-support conversation analysis because they want to improve their CX.
Support conversations are a valuable source of customer feedback, to quote the UK government website:
“We believe user researchers should be as close to our user support tickets as our support teams and developers. They are a constant source of feedback and feature requests that, unprompted by us, reveal the issues that are most important to our users.”
Your root cause analysis can be as easy or complicated as you make it. I recommend you kick off with a simple project to ease everyone in and prove the value.
Here’s the process we guide our customers on:
Starting with an idea of what you’re looking for saves a ton of time. For example, some goals you might be interested in are:
These will help accelerate your direction of travel and, importantly, tell you who to get involved in the planning process.
To make your analysis worth it, the results have to make an impact. The number rule of getting other teams to listen and act on your findings is to bring them in early and make them feel like their needs are being met (Ed Deason, Pret A Manger’s Ex-Director of Experience gave us this great advice in our guide to Customer Journey Mapping.)
The UK Government’s Product User Research team note in their planning stage that:
“To make sure our analysis focused on what was most useful, we initially ran workshops with each product teams to agree the objectives of this research.
In these sessions we printed off a random sample of tickets and gave each team member 5. Everyone was asked to write on Post-its what things they’d want to capture about the ticket, for example the type of request or the date it came in.
We then refined everyone’s Post-its and, by the end of the session, came up with a basic framework for analysis.”
Anyone who works in the customer service industry will know the sheer volumes of conversations that happen every month. They’re not only high volume and frequency, they’re also made up of complex, qualitative free-text.
So, how do you approach the problem of reading them all and finding patterns?
Your first point of call is categorise all your conversations or CSAT feedback using topic tags. This will allow you to separate relevant tickets and track volume patterns.
You have two options here:
If you have only a handful of tickets and a good amount of time to invest, a manual analysis is your best bet.
To identify the root cause of your support conversations, you first need to categorize them into topics using tagging.
Use our guide here to build a tagging taxonomy that suits your goals (we also tell you what a tagging taxonomy is, if you aren’t yet familiar with the term).
With your taxonomy in place, you must either go through your support request backlog and apply the tags or ask your agents to tag new conversations as they come in—which would only show topics in the latest tickets.
If you have thousands of monthly calls, chats, and emails, you’ll need an automated free-text analysis tool to categorise them.
The video on our homepage is an example of SentiSum’s machine learning-based analytics platform, which automatically understands, tags topics, and reports the hidden sentiments in customer support conversations.
Sakichi Toyoda, the founder of Toyota, popularised the “five whys” root cause analysis technique.
It’s as simple as asking why five times, which typically gets you to the core of the problem.
In a customer service root cause analysis, the five whys technique might look something like this.
👉 Our top reason for contact is ‘I can’t login’
👉 Why? Because they can’t find the login page (you’ll have to drill down into conversations to find this level of detail—SentiSum does this automatically for you).
👉 Why? Because they’re first visiting the homepage, which doesn’t have a clear login button.
👉Why? The website team didn’t think about it.
We got to our root cause pretty quickly here, but you get the idea. The five whys helps you understand what the real problem is and, if you’re lucky, points you to who can fix it.
Periodically, or at the end of the project, you’ll want to create visual reports that show the volume of each topic mentioned and give guidance on the ‘why’ behind it.
Depending on the goals you set initially, your analysis should point to quick fixes or bigger projects that need to be initiated.
Top tip: Illustrate your quantitative data (e.g. # of monthly tickets on each topic) with qualitative data (e.g. an example quote from a customer about the issue). This really helps spur action amongst other departments. (This is how Heidi, Head of Support at Trivago, influenced other departments).
There aren’t many detailed examples out there on the web, but my favourite examples are these ones:
Here, the UK government’s user research team used a customer service root cause analysis to identify quick fixes (gaps in product pages or documentation) and also used support tickets to do a usability test on certain features. Luckily, they only had a small number of tickets to manually analyse.
Zendesk’s documentation team member does a basic categorization to find common issues in support conversations. This is then used as a foundation to start a knowledge base.
SentiSum customers use our platform to automatically surface important topics and customer sentiments from customer service conversations. With our accurate, granular tags you can unlock time-saving automations and experience-boosting insights in real-time.
Book a product tour with us here to see how we can help you.