The Tech-Mirage & Analytics Illusion of Service/Support

In recent years, the realm of customer service and support has been transformed by a whirlwind of emerging technologies. Businesses have fervently adopted these innovations with the hope of enhancing their service outcomes. Yet, as they've ventured further into this brave new world, they've found themselves ensnared in a complex web of problems that thwart their progress. In the midst of this, serviceMob emerges as a transformative solution to the quandaries of modern service and support.

The Metrics Mirage

Numerous technologies boast about their prowess in providing analytics for customer service centers. They promise insights galore but often deliver only a fragmented perspective. None have quite mastered the art of taking the entirety of service data and looking at it from the vantage point of the customer.

A Data Jigsaw Puzzle

In the service landscape, CRM data remains a stranger to the Workforce Management system, and telephony data never gets acquainted with the Chat system. Data visualization tools struggle to explain the data of service in a manner that echoes the customer's voice. Consequently, contact rates and interactions continue to rise, compelling businesses to hire more agents. The anticipated service automation revolution? It's yet to make its grand entrance.

This leads to a pivotal question: just how effective are today's service analytics if hiring more agents and grappling with contact demand remains the norm?

The Dark Data Dilemma

The relentless influx of technologies has birthed a conundrum – dark data. This term refers to the vast reservoirs of untapped data, brimming with valuable insights. Organizations are locked in a perpetual battle to harness this data effectively, missing golden opportunities for service improvement. Adding to this woe is the technical debt associated with the tools they've adopted. Data science teams engage in a constant tug-of-war, striving to wrangle the data of service into meaningful insights but often end up with disjointed puzzle pieces that fail to complete the picture.

The crux of the issue lies in the inability to act upon this data. A sea of bi-grams, trigrams, and fractured data doesn't provide the much-needed impetus for reducing contact demand. The key here is action - how are you truly utilizing your service data today?

The Mirage of Analytics

The existing technologies generate a deluge of data – CRM systems, chatbots, and ticketing systems overflow with information. Yet, the lack of proper data management and analysis tools results in heaps of untapped data and the enigmatic presence of ROT (Redundant – Obsolete – Trivial) Data.

This treasure trove of ignored data fragments prevents organizations from grasping the full scope of customer behavior, preferences, and pain points as it pertains to the quantified experiences customers have with your business. Think of it this way, if you went and asked your Data Science team to provide you the quantified customer experiences your business handled, they would tell you – I have no clue ... there is nothing in market that can tell you here are the experiences customers had, this is how many contacts per resolution it took to solve this experience, this is how many agents per resolution it took during the experience, and this is our minutes per resolution invested in the experience. These highly valuable metrics (only accessible with serviceMob) have an R² of .9845 for CSAT scores and a R² of .9981 for NPS Bps– now that!

That my friend – Oh my-lanta! That is how you get people to care about service, service is absolutely a top line protector and can be a key in driving bottom-line cost effective customer centric outcomes; provided you have the right data ontology (model) for your business and industry model. For those of us not familiar with using statistics in service an R-squared of 0.99 means that the regression model explains 99% of the variation in the dependent variable, and only 1% of the variation is unexplained. This indicates a very strong relationship between the independent and dependent variables, and a very good fit of the model to the data. In this case the relationship between how many contacts and agents it takes to solve the experiential issue the more likely you are to detract and ultimately churn customers.  

The inefficiency in consolidating and analyzing data stored across multiple systems creates a gaping hole in understanding and addressing contact demand. These businesses continue to paddle in a sea of data, relying on outdated strategies, and seeing no decrease in contact demand, which is an ominous sign that their analytics are failing.

The Data Integration Challenge

Integrating data from various sources is a challenge. Each system operates independently, using different data models and formats, making comprehensive analysis a pipedream. Fragmented data and disjointed customer experiences are the bitter fruit of this lack of integration.

The core problem? None of this data was ever modeled to talk to each other - from - the - beginning! The entire ecosystem provides essential capabilities but often lack the extensible analytics necessary to get others to consume the data of service. The other major issue is none of these systems were meant to put data inside of each other as a repository to be modeled together - IE. No one puts CRM data in their Chat system, and chat systems have no insight into telephony data. Thus all of the in market BI tools while useful; cannot tell you how to model the data of service - if this were the case - then you would have less contacts and less FTEs - period.

The Need for a Paradigm Shift

Enter serviceMob , a ground-breaking service analytics platform. This solution doesn't just analyze data; it integrates data from disparate sources into a unified view, offering actionable insights and predictions.

Leveraging AI and Machine Learning, serviceMob doesn't just analyze data – it identifies patterns, trends, and anomalies, deepening the understanding of customer behavior and pain points. This equips businesses to proactively address issues and enhance the overall customer experience.

The platform also addresses the issue of technical debt by harmonizing data from different sources, eliminating data integration challenges, and enhancing the accuracy of insights.

The challenges of modern customer service and support, ranging from dark data to the elusive promise of analytics, have created a bottleneck in progress. serviceMob, with its unique capabilities, offers a way out. Let the Mob show you the light!  By minimizing customer effort, enhancing satisfaction, and providing AI-driven insights, we can partner with you and empower your business to unlock your service data's true potential and deliver exceptional customer experiences.

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