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March 20, 2024

The Hamster Wheel Dilemma: Why Support Metrics Aren't Changing

Current tools don't quite capture what customers really need. It introduces serviceMob as a potential game-changer, promising to help teams understand and tackle customer problems better.

The Hamster Wheel Dilemma: Why Support Metrics Aren't Changing

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What new social media mobile apps are available in 2022?

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Use new social media apps as marketing funnels

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Why is it that teams today within support still cannot change the reality? Maybe it's because the mirage they live in tells them everything is A – O – K. For a majority of these leaders, the only metrics they have in front of them are telling them everything is okay (CSAT/ABN Rate). Even with advances in technology, service and support organizations still deal with a significant amount of contact demand. Things, ladies and gentlemen, are not going well in service. Leaders today are using the same metrics, expecting different results. For years, teams have used operational efficiency metrics, case/ticket metrics, and subpar perceptual survey data with response rates well below 15% to make any decision about support.

Now, add to this, the teams of support today keep running in circles, not making a dent in case counts, contact rate, remain month over month, consistent – meaning we see the same number of contacts and create the same amount of cases without making any significant impact on the customer experience. Essentially, support interactions and cases become a complicated and ambiguous dataset as it relates to business effectiveness. While businesses want to reduce cases and understand interactions, the data models themselves are not set up to be worked together. No one factors that into their support data model. Everyone assumes the data of service is already defined, so when it comes to really understanding customer interactions, how do you look at the data of interactions?

Today, businesses only look at interactions as a commodity. Every single system of record (SOR) consuming, utilizing, or creating interaction data treats the data as a general commodity. This is why we are operationally centric when it comes to modeling interaction data. We lack the depth of analytics needed to determine its fitment to the overall data ontology of support; and not only support; support at your organization/your industry/aligned to the strategic outcomes which both business and customer are equally measured on. While we want to improve the experience of support, we have to be able to truly understand the level of effort customers put into solving their specific issue.

Today, the modern customer support organization still receives a significant amount of support interactions day in and day out. Support interactions are outcomes we today memorialize as cases or tickets. For interactions to be mined, teams have largely utilized speech analytics/conversational intelligence to determine what is happening in support interactions. What we end up with is largely qualitative and not a quantifiable form of impact to contact rate/reduces contacts/less agents – what we see (even with automated QA and CI tools), is largely a big squishy ball of feelings. Needless to say, these tools can provide some form of intelligence if you are going from zero to something, and if you think squishy sentiment measurement of outcomes against outputs is useful, you’d likely find little to tie the data to in terms of the input of the experience.

Remember these tools are helping your contact centers measure perception... and in service perception is not always a reality. Think of it this way – perception is not tied to behaviors. Even worse – perception... is being measured on less than 15% of the interactions you have today! What is even more shocking is businesses think this is the best data source to make changes to their service strategy or operating model. But when we are using response rates on avg. for some industries in the low 5%, all you are getting is noise, and you start creating and playing with bi-gram and tri-grams scratching your head with words... like seriously this is what we are using to understand interactions, then the data is not tied and linked across the entire Franken-Stack of service.

In the end, I think you get it – nothing in the market today... is helping you reduce your agent footprint, nothing is helping you de-scale your support/service footprint. This might be the craziest thing ever – a good percentage of executives think that we will always have to deal with the way things are today, it’s a hamster wheel, nothing in the market is changing for some of the largest logos out there ... you think AT&T has less agents ... how about Delta, or Southwest. What about banking , software, gaming... Its like a bad Sonny and Cher rendition of the beat goes on, and we all just keep bopping our heads with false positive perceptions of service. The metrics of service have largely not changed and therefore nothing has changed when it comes to really reducing agent footprints beyond the simple interaction types people take.

Look ......Klarna recently posted their results with AI – and if you believe that, well ... I’d ask a simple question, should those contacts have even been going to humans to begin with??? IE all the higher effort contacts are still coming in, nothing changing there, and if it were that bad, then the business likely should have been automating those interactions to begin with!

Data today for many organizations is not in the shape it needs to be in to make any significant impact to support demand. As organizations navigate the complexities of customer support, embracing a data-driven approach is essential for driving meaningful change. BCG emphasizes the importance of data-driven transformation, stating that "organizations that harness the power of data-driven insights will gain a competitive advantage in today's digital economy." By leveraging platforms like serviceMob and embracing the only customer-centric data ontology for service, businesses can unlock the full potential of AI-driven support and deliver unparalleled experiences to their customers.

You have nothing today that can tell you how many customer experiences you had, how many contacts per resolved experience it takes to solve an issue... instead you have CSAT and operational metrics .... all outputs and no inputs... its why you cannot get demand down ... period... we aim to solve that folks... serviceMob is the only service/support analytics platform that takes all interaction data, across all of the systems of record within support. This includes your AI solutions, CCaaS, Sentiment, CRM, WFM – all of the systems must be included into the data ontology (model) and be reconfigured from the customer's perspective to make a dent in reducing support interactions. All of the data teams have today is pointing them in the wrong direction.

This is why supervisors look at issue types and interactions with the wrong metrics; all of the metrics are modeled operationally when it comes to understanding customer effort. No system to – date tells you how to model the data of service, nor do they show you the statistical confidence of their data with back tests, MAPE values, P Value, etc. all of them sell you “Squishy Experience” insights... with no connectivity to the actual outcomes business and customers care about. Stop the hamster wheel – while there are many essential technologies for support – none of them – NONE OF THEM – can quantify the customer experience in your data... only serviceMob retains the ability to help you truly reduce contacts, see- measure - action - resolve - and transform your customer experiences at scale. Metrics like Contacts Per Resolved Experience, Average Minutes Per Resolved Experience, Post Service Experience Churn Score , and so many more metrics are revealed once you model the data from the perspective of the customer. None of these systems today can truly model the data from the perspective of the customer - its why all service/support professionals struggle with interactions, cases, and tickets.

Engage in the conversation here: https://www.linkedin.com/pulse/hamster-wheel-dilemma-why-support-metrics-arent-changing-barrera-ptn2c

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