Have you ever felt overwhelmed by the sheer volume of data in Zendesk, unsure of what to make of it? You’re not alone.
In most software platforms, data is analyzed through various views, such as brand, contact reason, channel, team, or agent, especially in the case of Zendesk. While these views provide insights, they often miss a crucial component for addressing customer friction – Context.
Without context, gaining a holistic understanding of customer experiences becomes challenging.
Wait a minute! Sebastian, isn’t there already several software providers doing NLP (Natural language processing) to auto tag incoming tickets?
Yes, there is, but not in the way we do it. We put our own spin on it designed specifically for Zendesk users.
Our innovative approach integrates contextual topic analysis with Zendesk metrics, offering a comprehensive view of customer outreach and pinpointing the exact areas of friction. It’s one thing to know what is talked about, its a whole other thing to know the impact it causes.
Understanding the metrics and their impact on resourcing
Having a clear understanding of where resources are being allocated is essential, especially when it comes to customer support.
The first four metrics in the Conversations view provide a snapshot into this: for any specified time frame, they answer crucial questions such as how many tickets were created, the total time spent on them, the estimated costs, and the average price we’re paying per ticket on a particular topic. These metrics not only offer a quantitative perspective but also hint towards qualitative improvements.
Whether it’s identifying product issues or process bottlenecks, the data provides a clear picture of the financial implications. For instance, it quantifies the cost of not addressing a product flaw or the potential savings from rectifying a process inefficiency.
In discussions and decision-making, data-driven insights always hold an edge over mere hunches, ensuring that voices are not just heard but also heeded.
Evaluating process and knowledge gaps + automation opportunities
The metrics of average replies and assignees serve as a barometer for the efficiency and effectiveness of our ticket resolution process. Essentially, they shed light on our capability to address and resolve issues associated with any given topic.
A high average in these metrics often signals underlying challenges, be it in our processes or potential knowledge gaps among our agents. For instance, if a ticket frequently gets escalated from one agent to another, or even a third, it prompts us to question the efficiency of our routing process. Are there inefficiencies in how we’re directing tickets? Might there be knowledge gaps that need addressing? Or perhaps, are there restrictive policies hindering our agents from resolving issues promptly?
Conversely, if the metrics reveal low averages, it paints a different picture. When tickets are consistently resolved with minimal replies and non-existent agent handovers, it indicates a streamlined and effective resolution process. Such efficiency often hints potential for automation.
If we can solve a majority of tickets with just one reply, it’s a clear indicator that these processes are ready for automation. Implementing automated solutions in these areas can further optimize the workflow, reducing some of the workload from our agents and ensuring even swifter resolutions for our customers.
The power of reopen rates
The reopen rate, often overshadowed by other metrics, serves as a crucial indicator of our effectiveness in addressing customer concerns. A high reopen rate for a particular topic suggests that our solutions may not be meeting customer expectations or fully resolving their issues. This not only impacts customer satisfaction but also creates a cascading effect on our support system.
For instance, if out of 679 conversations, 5% were reopened, that translates to an additional 33 tickets. While this might seem manageable, imagine the strain on resources when the rate escalates to 20%. Such a surge in reopens means an added flow of tickets that ideally shouldn’t exist, further straining our agents and potentially compromising the quality of support.
Gauging customer sentiment
While operational metrics provide insights into performance, metrics like CSAT (Customer Satisfaction Score), NPS (Net Promoter Score), CES (Customer Effort Score), and FCR (First Contact Resolution) offer a direct window into the customer’s perception post-service. Integrating these metrics with specific topics allows us to pinpoint not only our strengths but also areas of potential concern.
Especially in scenarios with high ticket volumes, such granularity becomes invaluable. It enables us to identify and address specific friction points in the service experience, ensuring that we’re not just meeting operational benchmarks but also consistently aligning with customer expectations and sentiments.
Predictive performance scoring
The predictive performance score, though still in beta testing, aims to address one fundamental question: if every customer was to rate us, what would that collective score look like?
Often, feedback is limited to a small subset of interactions, leading to the question of its representativeness for the broader customer base. Our current approach looks at a combination of over 10 distinct metrics, focusing particularly on the performance aspects of each ticket. By modeling this against your unique dataset, we generate a score that offers a more holistic and representative view of customer experience.
Summarizing it all
Making sense of your Zendesk data can feel hard and chaotic. Yet, with ‘Conversations’, we’ve crafted a solution that brings calm to the chaos. By emphasizing context, we ensure that every metric, every feedback, and every interaction is seen through a lens that truly understands the customer. It’s not just about numbers; it’s about the stories they tell.
Your Next Step
Feeling intrigued by the potential of ‘Conversations’? Why not give it a go? Experience firsthand the synergy of contextual topic analysis and Zendesk metrics.
Request a free trial and discover the difference context can make in your customer insights journey.