Blog
Articles
Insights on customer service transformation, demand prevention, and the Answer Engine approach to modeling experiences as structured data.
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Articles
The Complete Guide to Demand Prevention in Customer Service
40-60% of customer service contacts are repeat or avoidable. Learn why demand prevention, not demand handling, is the key to transforming service operations.
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Forecasting Experiences, Not Just Contacts: The Future of Service Operations
Traditional WFM forecasts contact volumes. serviceMob forecasts experiences, predicting demand patterns by type with 98%+ accuracy at 15-minute intervals.
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Articles
How to Model Customer Experience as Structured Data
Learn how a prescriptive Data Ontology transforms raw service interactions into a structured experience model that reveals what traditional analytics cannot see.
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Software with a Service: Why Platform-Only Fails in Customer Service Intelligence
SaaS gives you software. SwaS gives you outcomes. Learn why Forward Deployed Engineers are essential for building the ontology that makes the Answer Engine work.
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What Is an Experiential Answer Engine for Customer Service?
Discover what an Experiential Answer Engine is, how it differs from traditional CX analytics, and why serviceMob created a new category for customer service intelligence.
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The Real Reason Customer Service Is Still Failing: It’s Not AI, It’s Your Data
Why AI Isn’t Reducing Contact Demand AI was supposed to reduce the number of customer inquiries, but demand has only increased.
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There is no LLM(ing) your way out of Service and Support ... not without MOB(s)
"Multivalent Ontological Blocks (MOBs) provide the foundation for Large Ontological Models (LOMs), fueling AI systems to revolutionize service and support by addressing the root causes of contact demand.
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Los Angeles Times names Anuj Bhalla a 2024 CEO Visionary
"During his tenure at Accenture, he led the Service Analytics practice and filed three patents in the customer service field, demonstrating his innovative thinking."
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Articles
Customer Support Doesn't Cause Demand ... They Solve It... Right?
The current solutions in the market are failing to address the core issues plaguing customer support.
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serviceMob Kicks Off Irvine Tech Week with Joshua Lee and Somasundaram Subbu
I’m super excited and humbled to join an esteemed panel and workshop for Irvine Tech Week for the second consecutive year representing serviceMob!
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Nomination Alert for the 2024 Excellence in Entrepreneurship by the Orange County Business Journal
The Excellence in Entrepreneurship Awards celebrate individuals who embody the American entrepreneurial spirit, and Anuj is among a distinguished group nominees representing the best in Southern California.
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Gratitude to Morgan Stanley: Empowering Startups Through the Inclusive Ventures Lab
With a shared commitment to inclusivity and to helping individuals succeed, MSIVL's support has been instrumental in fueling serviceMob's growth and success.
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Articles
Data Entropy - The Mission Impossible of Service & Support
Now, you might wonder, why does data entropy matter in the realm of customer service and support? The key lies in the partitions of your tech stack.
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Articles
Answer Contacts, Solve Problems: Making life easier for Experience & Support Executives
Forcing channels on customers prescriptively vs. understanding issue and customer preferences really creates a multitude of problems, including repeat contact demand, re-work, poor CSAT, and ultimately churn. Its still baffling to me that accessing the voice channel for customers is still harder than ever.
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Why Piles and Piles of Cases and Tickets - Suck the life out of support...
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.
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In a galaxy not to far away: serviceMob's Odyssey to Experiential Support
Despite the wealth of cases, tickets, and interaction insights, businesses face a fundamental challenge – the struggle to model data from the customer's perspective.
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The Hidden Dilemma in Customer Support: Quantifying Customer Experiences
The industry acknowledges the importance of improving customer experience, yet it largely lacks the conceptual data needed to understand customer behaviors and experiences
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The Tech-Mirage & Analytics Illusion of Service/Support
Data visualization tools struggle to explain the data of service in a manner that echoes the customer's voice.
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The Franken-Stack of Customer Service: A Monstrous Challenge for Executives
The fragmented data within the Franken-stack makes it nearly impossible to measure resolution effectively. Instead, it becomes a wild goose chase, with customers making multiple contacts to solve a single issue.
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Unlocking the Data Dilemma: A Call to Redefine Service and Support
serviceMob is positioned as a solution for service leaders looking to address the challenges of managing customer service effectively, especially in the context of dark data.
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Transforming the Service Analytics Landscape: A Solution to Dark Data and Technical Debt
The promise that we would AI our way to automating the service journey has in fact, not ... happened... and likely will not. This begs the question: how effective are the analytics of service today if we are still hiring more agents, still talking with customers, and still getting the same contact demand?
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How analytics failed the world of customer service
Frustration with customer service is experienced at the customer and enterprise level as a result of “Bad Data.”
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99 Problems with Customer Service and this is #1
Why customer service still sucks in the modern world, for you, me, and everyone in between, and why the first of its 99 problems is bad data.