Common Challenges of Conversation Analytics

On the surface, analyzing conversations seems simple. With AI and LLMs, it should be easy to extract insights from calls, chats, and emails. But in practice, most teams run into the same problems: BI tools can’t handle unstructured data, DIY pipelines become costly to maintain, and AI models often deliver results leaders can’t fully trust.

These challenges slow down adoption and limit the value of conversation analytics. The sheer volume of interactions creates complexity that traditional approaches weren’t built to manage. Extracting value requires not just transcription, but structuring conversation data, connecting it to business metrics, and ensuring results are explainable enough to act on.

When these pieces are missing, insights get stuck in silos or never move beyond surface-level metrics. Leaders are left without clarity on the real drivers of churn, efficiency, or revenue, and conversation analytics fails to deliver the strategic impact it promises.

The Challenge of Structuring Conversation Data

Customer conversations don’t arrive in neat rows and columns. They’re long-form, messy, and full of context that doesn’t translate easily into numbers. To analyze them in a meaningful way, companies need to transform this raw text into structured data that can be measured, compared, and trusted.

That transformation is more than transcription. It involves identifying themes, mapping conversations to business metrics, and standardizing language so results are consistent. For example, when customers say “cancellation,” “closing account,” or “ending service,” all of those need to be recognized as the same intent. Without this layer of consistency, analysis becomes fragmented and unreliable.

The challenge grows when data from conversations must be joined with operational data—things like churn, handle time, or revenue. Without this connection, analytics stay descriptive at best (“what happened in conversations”) and fail to answer strategic questions like “how do these conversations impact the business?”

At scale, this becomes even more complex. A single enterprise may generate millions of minutes of conversation every month. Structuring and linking that data in a reliable way is the foundation for every downstream use case, from customer experience to compliance to product strategy. When this foundation is weak, the entire conversation analytics program struggles to deliver value.

Challenge 1: Traditional Data Stacks Struggle with Conversation Analytics

Data warehouses like Snowflake are excellent at handling structured data—metrics that already fit neatly into rows and columns. They’re optimized for financials, transactions, and other operational reporting. But conversations don’t arrive in that format. They’re unstructured, messy, and require significant transformation before they can be analyzed.

SQL engines are built for precise queries on structured datasets, not free-form text at scale. A question like “what percentage of customers mentioned canceling their account this month” is very different from slicing clean revenue data by region. Without heavy preprocessing and custom pipelines, warehouses can’t handle these types of queries with the speed or flexibility business leaders expect.

Some teams try to bridge this gap by building pipelines on top of Snowflake. While possible, it comes at a cost. Every new business question often requires another pipeline, another model, or another pass of data engineering. Over time, exploration slows down, costs rise, and insights arrive too late to be useful. We cover these trade-offs in more detail in Conversation Analytics: MaestroQA vs. Snowflake.

The result is predictable: conversation analytics efforts stall. Instead of giving leaders clarity on churn, efficiency, or revenue drivers, data gets stuck in engineering cycles. For conversation analytics to work, the underlying stack must be designed with unstructured data in mind.

Challenge 2: DIY Pipelines Drain Engineering Time and Delay Insights

When traditional data stacks can’t handle conversation data on their own, many teams try to close the gap by building custom pipelines. With enough engineering effort, it’s possible to preprocess transcripts, classify intents, and push structured outputs into a warehouse like Snowflake. On paper, this looks like a workable solution. In practice, it quickly becomes a burden.

Every new business question often requires another pipeline. If leaders want to track 50 different KPIs, that can mean building and maintaining 50 separate pipelines. Each one has to be tested, updated, and monitored as models drift or priorities shift.

The result is a system that drains engineering resources and slows down decision-making. Analysts wait weeks for new metrics. Engineering teams spend more time maintaining pipelines than innovating. And as use cases expand across support, sales, compliance, and product, the costs and complexity multiply.

What starts as a quick fix becomes a long-term drag. Instead of enabling conversation analytics, DIY pipelines lock teams into high costs, long lead times, and fragile systems that can’t keep pace with the business.

Challenge 3: Human-First Sampling Misses Critical Patterns

For many, the standard approach to quality and analytics is to sample a small set of conversations and assume it represents the whole. But even the most advanced programs rarely review more than 5–10% of interactions. That leaves the other 90–95% completely unseen.

The problem is that the unseen majority often contains the most valuable signals. A rare compliance failure, early indicators of churn, or emerging product issues don’t always show up in the slice that gets reviewed. By relying on samples, organizations blind themselves to the very risks and opportunities conversation analytics is meant to uncover.

Sampling also creates false confidence. The data looks clean, the reports look structured, but the underlying inputs are biased. Conversations are usually chosen based on convenience, assumptions, or what managers believe matters most. Instead of reflecting the customer reality, the analysis reflects human guesswork.

This is why so many initiatives stall at surface-level metrics. With only a fraction of conversations analyzed, the insights can’t scale, patterns remain hidden, and critical decisions are made on incomplete or skewed data. 

Challenge 4: Tool Sprawl Creates Silos and Incomplete Stories

As conversation analytics gains traction, different teams often buy their own point solutions. Sales may adopt Gong, compliance might bring in AuditBoard, and support may rely on a QA add-on from Zendesk. Each tool promises insights, but none provide a complete view of conversation data.

The result is fragmented insights. Sales can see pipeline trends, compliance can spot risks, and support can monitor agent performance—but no one can connect these dots. A billing complaint raised in support might be tied to lost deals in sales, or a compliance issue might highlight a product gap, yet those connections remain invisible when each team looks at its own slice of the data.

This creates blind spots, slows down analysis, and duplicates effort. Instead of a single, consistent view of conversation data across the business, they’re left stitching together partial reports from multiple tools. The data may look useful in each tool’s dashboard, but when stitched together it tells an incomplete—and sometimes misleading—story.

Point solutions may check a box for one team, but they make it nearly impossible to operationalize conversation analytics at scale.

Challenge 5: Black-Box AI Undermines Trust and Prevents Custom Questions

Many tools rely on out-of-the-box AI models to process interactions. These models can surface general themes or sentiment, but they often function as black boxes. Teams don’t know exactly how the model reached its conclusions, and when results can’t be explained, trust erodes quickly.

The problem becomes even more serious when organizations need to answer custom questions. A retail company might want to track mentions of a specific product defect. A financial services firm might need to flag compliance language tied to regulations. Out-of-the-box models aren’t designed for these organization-specific queries, and without transparency, it’s impossible to know if the outputs are accurate enough to act on.

This lack of explainability limits adoption. Analysts hesitate to base reporting on results they can’t validate. Compliance teams can’t defend findings that can’t be traced back to a clear source. Executives are reluctant to make strategic decisions when the data can’t be trusted.

AI can be a force multiplier for conversation analytics, but only when models are explainable, customizable, and grounded in the organization’s own context. Without that, black-box AI becomes just another barrier to making conversation data useful.

Challenge 6: Without Operational Context, Insights Fall Flat

Conversation analytics highlight what customers are saying, but on its own, you only see part of the story. Operational data—things like churn rates in the CRM, annual recurring revenue (ARR) in finance, or average handle time (AHT) in support—shows how the business is performing.

The value comes from joining the two. Without that connection, conversation analytics remains descriptive: you know what customers said, but not how it affected outcomes.

Take cancellations. A dashboard might show a spike in “cancellation” mentions this month, but unless those conversations are linked to actual churn data, you don’t know if it’s noise or a retention problem. Compliance is another example: a risky phrase flagged in a transcript is just text until it’s tied to the accounts carrying $20M in ARR. The same goes for efficiency. Complaints about a confusing process sound like anecdotes until they’re connected to higher AHT and repeat contacts. Even product feedback may have little weight until it maps directly to churn spikes or lost revenue.

This is where many conversation analytics attempts stall. They treat conversation data as if it were separate from the rest of the business. The result is descriptive reporting—what customers said—without the context that shows why it matters or how it impacts outcomes. Dashboards may look polished, but they don’t drive decisions about revenue risk, training priorities, or product investments.

When conversation data and operational data are joined, the picture changes. Suddenly, churn isn’t just a keyword trend—it’s revenue at risk. Compliance isn’t just flagged language—it’s measurable exposure. Efficiency isn’t just customer frustration—it’s higher costs. That context is what makes conversation analytics actionable.

Without this operational data, conversation analytics is just another reporting layer. With it, conversation data becomes a strategic input to the way businesses measure, forecast, and improve performance.

Conversation Analytics Falls Short Without Action

Even when the technical challenges are solved, conversation analytics isn’t an endpoint — it’s a data source. On its own, it produces insights. But those insights don’t change anything until they’re connected to the workflows where people already make decisions.

That means QA workflows, where data sharpens rubrics and highlights process gaps. Coaching programs, where managers use examples pulled from real conversations to guide agent improvement. Compliance reviews, where flagged risks become part of investigations. Product roadmaps, where patterns in customer feedback drive prioritization.

When conversation data powers these workflows, analytics moves from being descriptive to driving measurable change. Without that connection, data is interesting to look at, but disconnected from the levers that move the business.

What Good Conversation Analytics Looks Like

Strong conversation analytics programs share three core elements:

1. Centralized data
All conversations are brought together and joined with operational metrics like churn, ARR, or handle time. Without a unified dataset, analysis stays fragmented.

2. Multiple modes of analysis
No single approach works for every question. Effective programs use:

• Monitoring at scale to cover 100% of conversations and uncover trends missed by sampling.
• Ad-hoc and root-cause analysis to investigate issues like spikes in cancellations or compliance gaps.
  
  
• Role-based reporting workspaces to deliver the right level of insight to teams and individuals.
  
  

3. Action through workflows
Insights don’t sit in dashboards — they’re embedded into the systems where work happens. QA programs, coaching sessions, and pushing data back to the warehouse ensures teams can connect conversation analytics with their own metrics.

Bringing It All Together

Conversation analytics has huge potential, but only when the common challenges are addressed: structuring messy data, moving beyond DIY pipelines, avoiding silos, ensuring explainability, and connecting insights to real business metrics.

If you want a deeper dive into the foundations, start with What Is Conversation Analytics. And if you’re ready to see how conversation analytics can work at scale in your own organization, book a demo with our team.

FAQs on Conversation Analytics

What is conversation analytics?

Conversation analytics is the practice of transforming unstructured customer interactions into structured data that can be measured, compared, and acted on. Instead of sampling a few calls or relying only on surveys, it analyzes conversations at scale across every channel and connects them with business outcomes like churn, revenue, or compliance.It helps organizations uncover patterns, risks, and opportunities hidden in everyday interactions.

How is conversation analytics different from speech analytics or VoC tools?

Speech analytics focuses on spoken words, while VoC tools rely on surveys or feedback forms. Conversation analytics goes deeper by analyzing 100% of conversations across channels and linking them to operational metrics like churn or revenue, providing a more complete view of customer experience and business impact.

Why not just use a data warehouse or analytics stack for conversation data?

Warehouses and analytics platforms like Snowflake or Databricks are powerful for structured data, but they aren’t built to handle unstructured conversations. Transcripts are messy, inconsistent, and require natural language processing to become reliable metrics. Conversation analytics software provides the engines for structuring and analyzing this data, and then pushes the results back into the warehouse so it can be joined with the rest of the business’s metrics.