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Chattermill’s new Support Data Analytics enables CX, support and products teams to understand customer contact reasons in support tickets and chat conversations, and prioritise improvements to elevate customer satisfaction, foster loyalty, and drive growth.
Most companies currently sit on a wealth of real-time and historical support tickets, email conversations, and customer chats.
Understanding the contact reasons behind thousands of your Zendesk support tickets, Talkdesk or Drift messages and sharing those insights across your organisation is how you transition your voice of the customer program from a nice-to-have, to a must-have.
Still, for most companies analysing customer support data is like opening Pandora's box. The sheer volume and diverse formats - be it chat logs, emails, or call recordings - and unstructured nature of the data can be overwhelmingly complex to navigate without the right approach.
Time to insight is often poor, and unresolved customer issues arise, substantially consuming resources, with the result being teams lack a clear direction.
That's why today we're announcing enhanced Chattermill Support Data Analytics, including several new capabilities to make understanding contact reasons in support tickets and conversations easier, more efficient, and faster than ever.
Support Data Analytics will help CX, support, and product teams gather insights from support tickets, chat logs, and conversations, prioritise improvements, and address the most significant pain points to drive customer satisfaction, loyalty, and growth across the customer journey.
A better, more efficient way of analysing customer support data
Chattermill launched over eight years ago as a feedback analytics tool for CX, product, and customer support teams.
To help them unify the customer's voice, we've enabled over 50 integrations, including your favourite support tech stack - be it Zendesk, Intercom, Talkdesk, Kustomer, Dixa, LiveChat, Drift, Salesforce, and more.
For the last few months, we've been perfecting our approach to support data, focusing on creating a better, more efficient way of understanding customer contact reasons in support tickets and conversations.
To turn this vision into reality, we've improved how Chattermill leverages advanced Natural Language Processing (NLP) technology to analyse support data, allowing you to address central issues more quickly and efficiently.
We've eliminated the need for a tedious, manual data cleaning process, usually required for advanced analytics techniques, such as sentiment analysis, topic modelling, or customer segmentation.
With Chattermill, your support data will now be automatically cleaned and perfectly organised from day one. Navigating, searching, and extracting relevant information will become easier as you enjoy a streamlined and clean data set without inconsistent formatting.
Now, there’s no need to read through your support agents’ messages, including timestamps, signatures, and automated responses. You’ll be able to get straight to your customers’ words and understand what’s happening.
This saves time and improves productivity for data analysts and CX teams, allowing them to focus on generating insights and taking action rather than struggling with messy and inconsistent data.
But that's not all. Have you ever tried understanding customer pain points from a long thread or a chat conversation? Us too! It can take hours to understand the context from a back-to-back discussion can take hours, get to the root cause, or find the crucial details you need.
With quick insights from AI-powered summarisations, you can now receive immediate, concise summaries of long threads and grasp essential information in seconds.
No need to read through lengthy emails or conversations to understand what's happening - by leveraging GPT technology, you'll save time and effort in understanding high volumes of interactions with your customers.
Read more about summarisations in our Help Guides.
On top of this, we enabled our customers to unify their support data with other sources of customer feedback, such as surveys, online reviews, and social media comments.
This is because, at Chattermill, we believe that only through such a unified approach can companies gain a true understanding of all the pain points and opportunities that arise at every stage of the customer journey.
Seeing critical issues reported in your support tickets and conversations with other channels also unlocks more comparison and benchmarking options, as companies can now investigate and validate the size or impact of the problem across multiple customer touchpoints.
You don't have to spend hours on manual, painful data tagging or rely on a team of developers and data analysts to gain valuable insights or create the reporting you need.
Now you can use AI-powered Support Data Analytics to gain accurate and meaningful insights, effortlessly navigating through vast amounts of data without the overwhelm of manual analysis.
Why it's time to move on from manual tagging
If you think you don't need Artificial Intelligence (AI) to help you with your customer support data tagging and analysis, think twice.
We spoke to dozens of Chattermill customers, prospects, and partners, and they all shared common challenges and frustrations:
- As companies scale, manually tagging contact reasons in large support data volumes quickly becomes time-consuming, resource-intensive, and unscalable.
- Time to insights is poor, and teams lack clearly defined customer-centric goals and prioritisation.
- Issues with handling support tickets can consume resources and budgets substantially.
- Data sampling provides partial results and prevents understanding the full extent of the problem.
If these challenges sound familiar, it's time to implement a customised AI model to handle vast amounts of data. And yes, we're talking about thousands of customer emails, chats, and interactions each month.
But don't take our word for it. See how our customers benefit from analysing their support data with Chattermill.
"Chattermill has allowed us to be more effective and scalable. We no longer have to manually tag conversations which has freed up a lot of time on our team. It's also allowed us to analyse in more depth what the customer conversations are about - giving us further context on the issue.
Now we can pinpoint at which exact place in the customer journey the issues are arising and sort them more efficiently.
For example, in a difficult holiday shopping season, we leveraged Chattermill to analyse delivery issues, and we were able to use that data to optimise our logistics. This allowed us to have an optimal season delivery period, and our customer feedback helped us navigate through it and improve."
Jonathan Beirne, Chief Customer Officer, musicMagpie
Summary
The launch of Support Data Analytics is more than just this collection of new capabilities. It's our commitment to continue adding power and functionality to ensure we're the best Unified Customer Intelligence platform for every CX, product and support team.
Want to see how the Chattermill Unified Customer Intelligence platform can help you deploy improvements in tagging and analysing your support data? Schedule your demo today!
Already a Chattermill customer? Get in touch with your CSM to learn how you can get started with Support Data Analytics today.
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