In recent years, businesses have made significant strides in enhancing customer support and service outcomes. Yet, despite all the investments in technology and manpower, there's a hidden dilemma that continues to haunt the industry: the struggle to quantify customer experiences effectively.
Ticket Data - A Superficial Glimpse
When it comes to measuring customer support effectiveness, many businesses heavily rely on case and ticket data. These metrics provide a superficial glimpse into customer interactions, often centered around operational efficiency. They can tell us about response times, case/ticket close time , and some context around issues types, but they fall short in revealing what truly matters: the customer's experience.
The Illusion of Progress
Consider this: despite adopting various technologies and investing in AI-powered chatbots, many businesses are still grappling with the same volume of support contacts. In fact, in some cases, contact demand is on the rise. This glaring disconnect highlights a fundamental issue. These technologies and metrics are addressing operational efficiency but not the essence of customer experiences. The industry needs essential capabilities across the tech-scape of service but the illusion of progress is often mistaken for genuine improvement in quantified improvements to the customer experience. This is because despite these essential technologies they are not setup to deliver exponential transformative analytics as it pertains to the experiences they have - They are setup to provide you a view of the data they retain.
BI tools - well as I have previously mentioned, they are unable to provide and dictate with certainty the model needed to improve the customer experience, and with no model that is designed from the perspective of the experience, you end up with the same metrics, in different views, and have the same amount of contact demand.
The Missing Piece: Quantified Experiences
The missing piece of the puzzle is the ability to quantify customer experiences. Yes, we can measure how efficiently we answer contacts, but what about the experiences customers have? Do we know how many contacts it takes to truly address their concerns? Are we identifying and improving upon the factors that lead to repeat contacts, customer frustration.. and ultimately churn? Do we even know which agents are better at preventing re-work, repeat contacts? Can I even see the data to determine if we need a frequent caller queue, or do we need an escalation queue for repeat contacts that arrive in less then 24 hours? The analysis paralysis executives find themselves with is no data to make a decision about their service model, channel strategy, issue prioritization, etc. all to difficult to make decisions.
The Blind Spot in Service Data
The root of this problem lies in the fragmented nature of service data. Businesses accumulate vast amounts of information across various systems, but often these data silos fail to deliver a holistic view of the customer journey. What's missing is the ability to unify the data from different touchpoints and model it from the perspective of the customer. This blind spot means that the data we're analyzing is more about the business's perception of efficiency than the customer's behaviors of their experience. This technical hurdle for business prevents them from modeling the data from the perspective of the customer. Which in turn prevents the ability to analyze the data of factual concrete experiences customers are having with your products, services, goods, support teams, etc.
An Industry in Need of Change
The industry acknowledges the importance of improving customer experience, yet it largely lacks the conceptual data needed to understand customer behaviors and experiences. It's like trying to solve a puzzle with missing pieces. The frustration and limitations are evident. It's not that businesses lack the intention to improve customer support; it's that they lack the means to do so effectively.
The Path Forward with serviceMob
This is where serviceMob steps in. We recognized this dilemma and engineered a solution that's designed to quantify customer experiences. Our analytics platform has the unique capability to model the data of service from the customer's viewpoint.
Through proprietary technology and data ontology, we provide metrics that no other platform can. Metrics like AMPx (Average Minutes Per Experience) and Contacts Per Experience offer a fresh perspective. They empower businesses to understand the behaviors and experiences of their customers, moving beyond operational efficiency to genuine customer support effectiveness, improve NPS/CSAT, increase gross margin, and decrease cost to serve.
Preventing Contact Demand
Our mission is clear: if your analytics was working, you'd have less contact demand. We aim to break down the data silos, democratize data, and improve the speed at which businesses make decisions. This leads to not just operational efficiency but real effectiveness. It allows your teams to take fact-driven actions, prioritize painful customer issues, and ultimately reduce contact demand and cost to serve.
In an industry hungry for data that truly understands the customer experience, serviceMob is the first to step up and deliver. It's time to change the way we approach customer support by measuring what truly matters - quantified customer experiences.
Thinking about staying at the tip of the spear of transformation
The era of superficial support metrics is fading, making way for a new paradigm where quantified customer experiences are at the forefront. Businesses must adapt to this change to thrive in a customer-centric world. By adopting a data model that represents true customer experiences, serviceMob is revolutionizing the way we approach customer support. It's time for the industry to evolve, and serviceMob is leading the way.
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