Support conversations are a unique combination of easily available, highly valuable and yet...widely underutilised.
In many sizeable B2C & B2B brands, support tickets flood in continuously high volumes creating a large database of voice of customer data.
Whilst overwhelming for many, if harnessed quickly they become a real-time finger-on-the-pulse of the customer’s experience.
With a quality tagging taxonomy, the customer insight hidden away in the conversations opens the door for a wide range of use cases that prioritise experience and create efficiencies for support teams.
“Talking to customers face to face, en masse, every day is unique to customer service. That close contact is an untapped and unappreciated opportunity to build strong customer relationships, increase loyalty, hear customer feedback, improve products, build community, upsell, increase basket size, and so on.”—Sharad Khandelwal, CEO at SentiSum
So, why do most brands ignore one of their most valuable customer experience assets?
While many companies still do not value their customer’s voice, those that do tend to find customer service analytics difficult. The sheer volume of conversations is overwhelming and the complex nature of voice calls and free-text (i.e. every customer describes things differently) makes it hard to draw out trends in the data with automation.
Further to this, customer service has been historically under-appreciated. Many simply don’t make the link between customer support and the increasingly valuable role the department plays in customer experience.
With the arrival of artificial intelligence-driven customer service analytics[ADD LINK], it’s now easy for support leadership to derive accurate, real-time insights from every support conversation.
Choosing to leverage those insights in creative ways to support improvements and optimisations is one way I see customer support winning the respect it deserves.
Customer service analytics can support company growth in several ways. One of the most important use cases I see every day is tackling friction points in the customer journey that drive customer churn.
If properly analysed, support conversations are an easy way to identify high priority issues for customers.
In a recent interview, Gousto, the British meal-kit delivery company that delivers 8 million meals to customer’s monthly, revealed how they use AI to analyse their support conversations to help identify CX issues to fix.
They didn’t want to lose their understanding of the customer as they scaled fast, so Gousto invested in text analytics to derive insights from the hundreds of thousands of conversations they had with customers each month.
Gousto had 9 customer contact channels in total. They brought them together into one analytics dashboard that would enable a unified understanding of customer issues.
“We use the latest technology to give open access to our voice of the customer data across the business for teams to self-serve insights for anything from discovery work for Tech initiatives through to root cause analysis for any operational complaints to guide improvements.” —Joe Quinlivan, Head of Customer Service at Gousto
By breaking down their tens of thousands of monthly conversations, the Gousto team identified trend topics and repeat issues that would become the basis of new CX improvements projects.
For example, Gousto identified that their #1 reason for contact was a particular food item was arriving damaged. By quantifying the impact on customers, Gousto showed the logistics department the evidence and they addressed the issue quickly.
Gousto is not alone in using support conversations to improve customer experience—many of our customers invest in AI-based tagging for that purpose.
Further examples come from companies like Vinted and Loom who revealed on the Support Insights podcast that they’ve built processes to feed qualitative customer service analytics directly into the product development cycle.
Customer service is an important bottleneck for customer experience: when anything goes wrong in other touchpoints, the peeved customer funnels toward your department.
It is, therefore, critical that the customer service experience is on point.
Customer service analytics can help with that in a couple of ways. For example, conversation topic tagging analytics can help automate parts of the customer service journey—ensuring the customer gets better, faster service.
James Villas, a travel brand which owns more than 3,000 hotel properties, reduced first reply time by a whopping 46% and overall resolution time by 51% with topic tag-based triage. (Here’s the interview with their Head of Digital Transformation).
Real-time customer service analytics was able to identify urgent tickets amongst the high daily volumes, ensuring customers in dire need were prioritised in the queue. The results speak for themselves, as James Villas’ CSAT increased by 11%.
There are many more interesting and creative use cases we’ve come across with our clients. For example, an increasing number of companies want to leverage AI conversation analytics to help train their service agents.
By running sentiment analysis across CSAT feedback and agent-customer conversations, we’re now able to identify the behaviours and language used that leads to poor customer satisfaction.
Having a clear understanding of why customers are contacting customer service, and the issues that are trending up, is a necessary step towards reducing support ticket volume and their associated costs.
This was exactly how this leading e-commerce tyre retailer used their service analytics capabilities.
“Topic volume analysis allowed the retailer to quantify the problem and its cost to their business. Armed with quantitative evidence, the contact centre manager was able to direct the web team to prioritise fixing the booking page without the effort of manual analysis.”—From the case study here.
In short, your analytics capabilities are what helps build evidence and drive the change needed to reduce ticket volume.
In our podcast episode with Loom’s customer service leadership team, we discussed several other ways to reduce support ticket volume.
Over the pandemic, Loom saw their product usership explode past 10 million (from 700k). At that time their contact rate was 4%, which meant huge growth in their support ticket volumes each month.
By analysing their support tickets, the Loom team identified the most frequent topics driving contact and built a targeted knowledge-base to preemptively answer those common customer queries.
Using tactics like these—better for both the user and the company—Loom brought their contact rate down to 1%. Saving the company millions in the process.
Perhaps one of the most important use cases of customer service analytics is fuelling support-driven growth.
In their podcast episode with us, the ethical clothing brand, Organic Basics, gave us a brilliant example of doing just that. The customer service team ran an in-depth analytics project and, as a result, built a revenue-focused ticket prioritisation algorithm.
Organic Basics’ analysis revealed that some customers, at specific points in time, would approach customer service to ask questions right at the moment before making a purchase. By auto-prioritising customer tickets at that moment in the buying funnel, they could help those ‘leads’ quickly before their attention turned elsewhere—increasing sales.
Our AI-based topic analysis takes this one step further by prioritisation support conversations and tickets based on importance, profitability or ‘risky’ words and phrases.
These examples and use cases show that customer service analytics is clearly a valuable investment.
A word of caution, however. The impact your analytics has is dependent on the speed and quality you can do it. When your monthly customer service conversations drift into the thousands—both speed and quality become difficult to achieve.
Manual or semi-automated conversation tagging is one option for getting regular insights—although, results are regularly inaccurate or too surface-level to open up the full number of use cases possible.
At SentiSum, we build AI-based analytics engines for support teams like Gousto, Hopin, Nestlé and HotJar. They chose SentiSum because of the accurate and detailed ticket tagging, real-time sentiment analysis and time-saving automations.
Book a product tour with us here to see how we can help you.
Customer service analytics is the process of analysing your customer support calls, texts, chats and emails to discover valuable customer insights. Customer experiences converge at the customer service touchpoint, making it an abundant place to understand CX issues faced in other departments. Customer service analytics brings those issues to the surface in a fast and reliable way—usually led by data tagging to get quantitative insights from qualitative data sources.
Customer service analytics has many valuable use cases. The most obvious is to understand customer sentiment on different issues, which can help identify which touchpoints and experiences need improving if you want to tackle churn and inspire more customer loyalty.