Articles
March 20, 2024

Why Piles and Piles of Cases and Tickets - Suck the life out of support...

The article discusses the limitations of relying solely on traditional metrics like FCR (First Contact Resolution) and the importance of shifting focus towards understanding the entire customer experience by considering metrics like Average Minutes Per Resolved Experience and Contacts per Resolved Experience.

Why Piles and Piles of Cases and Tickets - Suck the life out of support...

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The world of tickets and cases is a magical land of dissatisfaction and lack of clarity. Cases and tickets are an essential capability needed to memorialize the reasons for support—period. Now, what about all my wonderful case and ticket metrics? Not important. Why? Because cases and tickets are outcomes—let that really, really, REALLY sink in. Cases and Tickets are outcomes, so when it comes to understanding operationally where we should focus, we often rely on these outcomes, which are created, to point us directionally on where we need to focus.

Often, we look at metrics like FCR (First Contact Resolution) within these systems, but really all this represents is the percentage of tickets closed. That’s right—all this metric shows you is the percentage of cases you have closed. It’s a completely inaccurate representation of customer effort and is a false positive for the customers of Salesforce/Zendesk/You Name a desk. The data around tickets, i.e., ticket closure time, ticket/case reason quantities, heck, even all the reports that exist around tickets and cases all point to metrics of operational insight, with little to no actionability.

We know this to be true because every single one of the businesses which utilizes these systems to memorialize the support issue, all of these businesses, keep creating a 💩 ton of cases. There are so many cases created that an entire ecosystem of startups has popped up, literally spitting out the same data that exists in these Case and Ticket systems. Couple this with agent footprints not shrinking, contact rates remaining consistent, it begs the question why do we rely on cases?

Now, consider the Case/Ticket Data model has no connectivity to other systems' data models. Each one of these systems is an island with its own data ontology (data model). In other words, who is throwing their CRM data into the WFM system? Who throws Case data into their CCaaS Systems? Nobody. Now couple that with the technology utilized by the business units who created the contact demand, or where the support issue resolution data truly lies—this is often where no linkage is made. What ends up happening is cases and case data are consumed from operations teams and executive teams are left with head scratchers.

The truth is, in order to reduce cases, or extract cases, you need to be able to see the inputs that go into the case. If you can’t measure it, you can’t improve it (Peter Drucker). The goal is to see and measure the experience. The goal is to quantify the experience in your data so you can prevent higher effort support experiences from impacting churn, to reducing the number of contacts it takes to resolve the experience. While cases are great to memorialize and tie to customer data, the real truth is to get any real productivity that is both operationally and customer-centric, you need to look at the experience based on the one-to-many contacts a customer has for each of their support experiences. Once you understand all the effort it takes to resolve a support issue, you can see a whole new world—Aladdin...

The metrics you care about have to be modeled from the perspective of the customer. Metrics you can see-measure-action-resolve-and transform, include Average Minutes Per Resolved Experience, Contacts per Resolved Experience, Experiential Contact Resolution; these metrics along with a host of others have an R-squared of over 98% to the likelihood of a customer to churn, their willingness to recommend, their overall satisfaction, and are measured on all of the experiences your customers have with the business.

Seriously—I still cannot believe in 2024 here we are, still thinking surveys are the best dataset we can get. When it comes to cases, we create no common linkage to the behaviors of customers, nor do we collect all the data in a meaningful way to truly reduce OPEX, improve Gross Margin, and drive consistent service outcomes. While businesses want to improve the experience, cases just are cases—you cannot use case data models and truly tie them to all of the systems of record that exist in the tech-scape of service/support.

The evidence of cases is simple, they are extremely essential, ticket management systems, also extremely essential—but think back and look at your data around cases, what is changing? Have you seen less support contacts, are you seeing a reduced contact rate... even worse are you forecasting outcomes (CASES!!!!) Cases are outcomes and CONTACTS my friends, Contacts are the inputs to cases. This is what should be forecast and often is not; instead, people think they are forecasting but they are getting a moving-average of Erlang C, worse yet—they don't even look at cost per contact... yet again—cost per case.

Cases are good records for support, but without a comprehensive data ontology that is modeled from the perspective of the customer, taking all the dark data of service— including AI solutions, CRMs, Client Systems, etc.—you need to sit on top of all of that to be able to get to the metrics I described. Then the data has to be engineered, repaired, synthesized, essentially the neural network emerges, and with this vast, ever-growing dataset, you now have the most optimal entropic dataset for service. This is only achieved with serviceMob—full stop—no company can do this today—it's why every service/support business still has a ton of cases, still has a ton of agents, and still cannot get contact demand down. All of the data of service has to be included in the analytics of service and support.

serviceMob is the world’s first data ontology and experiential analytics platform specifically designed for service/support functions agnostic to industry. We sit on top of all (including AI—solutions) the technology (often referred to as the Franken-Stack) of service/support centers. Our thesis is very straightforward; if your analytics were working—you would have less contact demand and have quantifiable evidence of the experiences your customers have via your service/support functions.

Stop the crazy train folks, get off the case/ticket diet, we get it—we need cases—we definitely need support tickets—but beyond the surface folks—we need actionable intelligence, we take all of the case and ticket data your teams generate and extend the data of service to ensure the teams that help to drive demand into support can truly consume the data to remove contacts—remember folks contacts are the inputs to cases—so—when you are ready to revolutionize your support world, get a seat at the table to show the business how many customer experiences it had, and ensure the enterprise can action all the data of service – well, the Mob... we’re here – we’re always here 😉

Engage with the article on Linkedin: https://www.linkedin.com/pulse/why-piles-cases-tickets-suck-life-out-support-marcel-barrera-1rrdc

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