There is no LLM(ing) your way out of Service and Support ... not without MOB(s)

How Multivalent Ontological Blocks Inform Large Ontological Models to Feed Large Language Models in Service and Support

Customer service is not just broken—it’s fundamentally misunderstood. Despite AI advancements and vast troves of data, most organizations remain stuck using outdated metrics and disjointed analytics. These metrics do little to drive meaningful improvements in service efficiency or customer experience. Enterprises invest heavily in AI tools, yet contact demand remains high, costs are rising, and churn threatens long-term profitability. This isn’t just a tech problem; it’s a data problem.

The solution? Multivalent Ontological Blocks (MOBs) and their ability to form the backbone of Large Ontological Models (LOMs), which can then fuel Large Language Models (LLMs) to revolutionize service and support.

Let’s dive into why serviceMob’s MOBs are the cornerstone of this evolution and why they’re crucial to shaping how we think about service and support data in the age of AI.

Why Traditional Metrics Fail to Move the Needle

Enterprises have spent billions trying to optimize customer service using surface-level metrics like CSAT, AHT, and NPS. These metrics tell us what happened but fail to explain why customers are still dissatisfied, why contact demand continues to rise, and why churn rates stubbornly refuse to improve. This superficial approach focuses on isolated interactions, failing to capture the complexity of the customer journey and service experience.

Imagine a customer reaching out for a refund. Traditional systems log the interaction and measure it by the time taken or whether the customer was satisfied. What these systems fail to do is assess the impact of that interaction on future contact demand, identify the root causes that led to the inquiry in the first place, or predict whether that interaction will increase churn. Without these insights, businesses remain reactive, unable to prevent contact demand or improve customer experience in any meaningful way.

The Power of Multivalent Ontological Blocks (MOBs)

MOBs radically shift the way service data is understood. MOBs move beyond transactional data, focusing on capturing multiple dimensions of customer interactions and synthesizing them into a holistic, actionable framework. Think of them as the building blocks for understanding the full customer journey.

Each MOB informs Large Ontological Models (LOMs), which integrate data from every touchpoint—sales, support, product usage, and more. These LOMs, in turn, feed Large Language Models (LLMs), giving AI-driven systems the context they need to make intelligent, proactive decisions about customer service and support.

Here’s what a Multivalent Ontological Block can capture:

• Impact on Contact Demand: MOBs assess how each interaction influences future contact volumes. For instance, an unresolved issue in the billing department may drive repeat inquiries through multiple service channels (chat, email, phone).
• Root Cause Identification: MOBs reveal the deeper reasons behind each contact, such as product flaws, poor communication, or gaps in process efficiency. This insight is critical for preventing future issues at their source.
• Financial Implications: MOBs quantify the cost of serving each interaction, including the potential revenue risk from unresolved issues. This also includes assessing how unresolved cases may lead to customer churn and lost revenue.
• Resolution Efficiency Metrics: MOBs track how well the issue was resolved and measure broader implications on customer sentiment and loyalty.

In essence, MOBs allow businesses to move beyond the superficial and focus on understanding why contacts happen, how they affect the broader customer experience, and how they can be mitigated.

From MOBs to LOMs: Building the Infrastructure for Actionable Insights

Large Ontological Models (LOMs) take the data captured in MOBs and integrate it into a unified, system-wide view. These models don’t just track individual interactions—they build a comprehensive knowledge graph that reflects every point of contact and its implications across the organization.

LOMs represent the full service experience: not just support calls or tickets, but every interaction a customer has with the product or service, from browsing the website to using the product. LOMs structure this data in a way that’s consumable not just by service teams, but by every business unit, from product development to marketing.

For example, let’s say a specific feature in a software product is generating 30% of all customer inquiries. The LOM ties this data back to the product team, highlighting the root cause and offering actionable insights. Once implemented, the improved feature reduces inquiries and improves the overall customer experience. MOBs give you the “what,” and LOMs give you the “how” to fix it.

LOMs enable service and support to stop being a siloed function and instead become a feedback loop that drives change throughout the organization.

How LOMs Fuel Large Language Models (LLMs)

The next layer is using Large Ontological Models (LOMs) to feed Large Language Models (LLMs), which can then generate contextually rich, actionable outputs that service agents, managers, and even automated bots can use in real-time.

Most AI systems today struggle with fragmented data. They can process the information they’re given, but without context—which LOMs provide—they can’t deliver the deep, actionable insights businesses need. By feeding MOB-informed LOMs into LLMs, companies can deploy AI that understands not just what a customer is asking, but why the question is being asked, and how to proactively resolve it.

For example, a customer may ask a chatbot about a billing discrepancy. A traditional system would process the question, provide a generic response, and move on. But with LOM-fed LLMs, the AI can reference prior interactions, understand potential issues in the customer’s billing history, and suggest specific solutions while keeping future contact demand low. The chatbot is no longer reactive—it’s proactive.

Real-World Impact: Preventing Churn, Reducing Contact Demand, and Improving Profitability

This integrated approach yields tangible results across key business metrics:

• Churn Prevention: The Post-Experiential Churn Risk Score generated from MOBs predicts which customers are most likely to churn based on their support interactions, allowing businesses to take proactive measures and reduce churn by up to 30%.
• Reduction in Contact Demand: By identifying the root causes of customer inquiries and tying them back to responsible business units, serviceMob helps companies prevent contact demand at the source, leading to a 20-40% reduction in overall service inquiries.
• Cost to Serve Optimization: Rather than just making the service center more efficient, serviceMob reduces the need for customer interactions entirely. This results in lower cost to serve and higher profitability. By providing actionable insights across all business units, serviceMob saves companies millions in operational costs annually.

The Future of Service Analytics Lies in MOBs, LOMs, and LLMs

Customer service is no longer just about solving individual problems—it’s about understanding the complete customer journey. Multivalent Ontological Blocks (MOBs) provide the foundational layer that businesses need to move beyond outdated metrics. By informing Large Ontological Models (LOMs), MOBs create a comprehensive view of service interactions, which then feeds into Large Language Models (LLMs), enabling smarter, more effective AI systems.

This is not just about making customer service more efficient—it’s about transforming it entirely. By focusing on the root causes of contact demand, preventing churn, and optimizing the cost to serve, businesses can finally realize the full potential of their data.

If your enterprise is ready to evolve its approach to customer service, it’s time to embrace MOBs as the future of service analytics.

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