March 20, 2024

In a galaxy not to far away: serviceMob's Odyssey to Experiential Support

The article highlights the technological abundance in customer service but underscores a crucial gap in evolving the customer experience due to the inability to shift from mere management to genuine evolution. Despite vast insights from cases and tickets, businesses struggle to model data from the customer's perspective, leading to an operational quagmire lacking transformative potential.

In a galaxy not to far away: serviceMob's Odyssey to Experiential Support

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In the Franken-Stack landscape of customer service, we've been bestowed with powerful tools — CCaaS, CRM, WFM, and QA solutions. Yet, amid this technological abundance, there's an undeniable gap: the inability to shift customer service from mere management to a genuine evolution of the customer experience.

Despite the wealth of cases, tickets, and interaction insights, businesses face a fundamental challenge – the struggle to model data from the customer's perspective. The result? An operational quagmire that lacks the transformative edge needed for a seamless support journey.

This is more than a dark data echo chamber; it's an infinite loop of DATA ROT (Redundant – Obsolete – Trivial) even with the integration of essential tools. We have more toys, but we lack a meaningful way to consolidate insights into a cohesive view across the entire support journey. The consequence is evident – a failure to rationalize the true number of customer experiences the business handled and an inability to garner support from other business units to leverage this valuable data. And seriously, does support cause the initial contact? No, it doesn't. But who does? What department, what function, what process... (In your best Napoleon Dynamite voice ... "What the Flip!")

To make this a reality, data must be consumable and actionable by teams responsible for "issue" resolution and experiential "service" resolution. Yet here we are in a ocean of data ... the largest amount of data still not captured effectively is within Service/Support.... that's why customers churn, it's why it takes multiple contacts to solve our experiences...its why teams can only look at cases/tickets.

The data we use – and that largely goes unused in service beyond operations – is focused on Efficiency, Quality, Productivity, and Operations. The data they need, the data they should be clamoring for, is what issue types represent the most painful and prevalent experiences, prioritized for engineering, product squads, field operations, marketing, success, partners, insert business unit name "here" to prevent contacts. We think we have that data today, right? Right? The truth is, no one really does.

We need to rethink service from the customer's perspective- which is exactly what every Chief Customer Officer wants, because if you can't measure it ... you can't improve it. Once revealed the ability to see, measure, act, resolve, and transform the experience becomes a reality. This small shift has been the missing element for the last 30 years of customer service/support. In a desert of solutions there's a tech mirage of products claiming to offer analytics, in the end - they offer the same metrics and the same views of data with nothing tied to actionability of the data.

The irony lies in the fact that despite technological advances, organizations still struggle to prevent rework, reduce repeat contacts, and model data effectively to unveil experiential churn via service. The missing piece in this puzzle is the scarcity of platforms that truly understand and model data from the customer's viewpoint - IE (WHAT PLATFORM TELLS YOU HOW MANY CUSTOMER EXPERIENCES YOU HAD!!)

The difficulty lies in the ability to understand how to engineer and model the data regardless of industry... You need to take all the data of service across all systems of record (including AI solutions) and create the end-to-end service journey in the data.

serviceMob retains the world's only platform with the unique ability to model data from the customer's perspective and define all the necessary metrics, attributes, indicators needed for your Data Ontology (Model). This paradigm shift leads to metrics that redefine the customer experience, such as AMPx (Average Minutes Per Experience), Contacts Per Resolved Experience, and Experiential Resolution Rate, just to name a few.

The statistical models employed by serviceMob not only provide insights into customer effort, now - for the first time ever, we can empower enterprises to see and understand the one thing they've always wanted to improve but could never see.. (Say it with me guys - - - The Customer Experience).

For those ready to transcend the limitations of conventional customer service analytics, and are tired of hearing the over hype of Ai; here are some compelling questions to ponder:

Quantifying Customer Experiences:

  • Does your organization find it challenging to move beyond surface-level metrics like CSAT and NPS?
  • Are you able to see and improve your contacts per resolved experience? Do you know how many customer experiences you had last week?

Unifying Disparate Data:

  • Do you sometimes feel overwhelmed by the sheer volume of data but starved for actionable insights that can genuinely enhance your service and support... and reduce contact demand?

Incomplete Insights:

  • Have you experienced the frustration of working with vendors who can only provide a fraction of your reporting and analytics needs, leaving you with a disjointed view of your service operations?

Output vs. Input Metrics:

  • Are you able to identify churn stemming from service and support, and do you often find yourself trapped in a cycle of managing output metrics?

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