Customer interactions are the lifeblood of the relationship between a company and its customers. Small companies with few customers and low transaction volumes can apply great care and attention to every contact. However, as business scales, it is critical to track customer contacts to ensure all questions are answered and issues are resolved efficiently. Although support tickets are often used to track a variety of contacts like sales inquiries and product questions, software packages typically refer to the contact records as “support tickets.” For this discussion, we’ll refer to them simply as “tickets.”
A ticket is essentially a digital record of the customer engagement converted to a referenceable number to enable a company to respond to clients within agreed to SLAs. They capture why and when customer engaged, when to respond, and the status. Tickets also provide process compliance and service level effectiveness.
A robust ticketing system provides customers a reference to their engagement, with details of the discussion(s) to date, employees involved, and actions taken. Tickets get updated real-time along the customer journey, particularly when they are transferred between company representatives. This provides the benefit of not having to repeat their personal information or the history of their case. For some engagements requiring multiple action follow-ups, the ticket can help the customer reconnect with the same representative to provide continuity.
Ticket data is inherently valuable to companies because customers provide real-time information about their questions and issues without a prompt from a survey, however up to 80% of the data is unstructured.
Ignoring unstructured ticket data introduces business risks. A common example is when companies implement procedures that reduce or eliminate the human-to-human interaction, causing engagements to feel impersonal, or as if the customer is “just a number.” We discussed why human experience (HX) matters in more depth in a previous article, but to put it simply, when the human relationship between a company and a customer is weak or absent, customer satisfaction and loyalty are likely to suffer, along with revenue and profitability.
What’s worse, many companies focus more on promptly closing their open tickets rather than the fundamental customer needs driving creation of the ticket. When support teams aim for highest “efficiency” for ticket closure rather than helping their customers, HX relationships with customers can be seriously impacted.
Even when companies focus diligently on maintaining the human experience in their ticketing process, many still overlook the opportunity to leverage the data collected in their tickets and fail to understand what their customers are telling them in every contact. It is time- and resource-intensive to analyze the vast amounts of ticket data manually (especially the unstructured data), and when they try, results are often inconsistent due to variations in how each person analyzes data.
In recent years, a popular trend is for companies to hire data science teams to build AI and machine learning models to analyze their data and “automagically” provide transformative guidance to the company. Unfortunately, these efforts often fall short due to the complexity required to both create effective automated data models and to then apply domain expertise and specific company business factors.
The ActioHX Ticket Insights service begins by connecting to and ingesting data from your ticketing tool, typically via API. Next, we apply intelligent categorization of the data in each ticket. Our processing engines perform detailed topic, intent, and sentiment analytics, extracting key values including customer-reported symptoms, key resolution steps, and sentiment scores. In addition to the unstructured text captured explicitly in a ticket, we can also include structured and unstructured data from live chat, social media chat, emails, and calls.
After analyzing the data, our solution provides reporting and dashboarding for both the structured ticket data (ticket counts, closure metrics, etc.), and for the classification and sentiment analysis of the unstructured data.
One of the first insights we provide to most companies is an identification of common customer questions and issue resolutions that can be addressed with self-service options. Though it may seem counterintuitive, strong human experience need not rely solely on direct human conversation but can be fostered by optimizing processes and interfaces for the way humans think and behave. Options to find what’s exactly needed, quickly and efficiently, are important contributors to positive HX.
Irrespective of whether customers used self-serve or assisted service options, ticket data analysis can highlight process gaps and inefficiencies in how the company responds to customers. Improvement opportunities are also identified for knowledge content, organization and links, and user interface. All of these significantly impact effectiveness of and satisfaction with customer-facing websites and apps.
Next, sentiment analysis classifies each ticket as positive, negative, or neutral, identifies trends and issues, and provides actionable recommendations. Sentiment analysis also drives useful refinements to the focus areas in the company’s CSAT surveys.
Other insights we’ve discovered from analyzing tickets include:
In summary, tickets contain rich data that ActioHX can leverage to help you improve human experience and customer satisfaction at your company.