Conversational Analytics for Customer-Facing Applications
Conversational Analytics for Customer-Facing Applications
By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku ·
Conversational analytics transforms customer-facing applications by enabling natural language data queries that deliver governed, instant insights. Platforms like Kaelio connect to existing data stacks and semantic layers to ensure metric consistency, allowing teams to ask questions in plain English while maintaining robust access permissions and enterprise-grade security.
Key Facts
• Gartner projects conversational AI will reduce contact center labor costs by $80 billion by 2026, with 10% of agent interactions automated
• 55% of companies currently use AI for alert triage, with 60% of SOC workloads expected to be AI-driven within three years
• Sentiment-driven models achieve 85-92% churn prediction accuracy, while proactive usage monitoring reduces churn by 20-40%
• The dbt Semantic Layer eliminates duplicate coding and ensures metrics refresh everywhere when definitions change
• Row-level security in BigQuery provides fine-grained access control at no additional cost
• Forrester profiles 37 vendors in the conversational AI platforms landscape, signaling significant market maturity
Modern SaaS teams are turning to conversational analytics to surface insights in seconds rather than days. By letting people query data in plain English without breaking governance, the practice unlocks faster decisions for every customer-facing application.
What Is Conversational Analytics, and Why Should SaaS Teams Care?
Conversational analytics lets employees ask questions in everyday language and receive governed, auditable answers from company data. Rather than writing SQL or waiting on a data team, a RevOps manager can type "What was our net revenue retention last quarter?" and get a trustworthy result within seconds.
At its core, conversational analytics in platforms like BigQuery enables users to chat with data agents using natural language. This capability empowers users to "go beyond static dashboards and ask data-related questions in regular, natural (conversational) language, even with little or no expertise in business intelligence," according to Google Cloud's Looker documentation.
Why does this matter for customer-facing applications? As IDC's Dave Schubmehl notes, "As communication becomes ever more important, conversational analytics and intelligence is becoming a 'must have' for organizations." These tools help understand customer issues and generate significant return on investment.
For Series A and B SaaS companies, this translates to faster answers for growth teams, RevOps, sales, and customer success without bottlenecking on engineering or data resources.
Why Is the Market Rushing Toward Conversational Insights?
The momentum behind conversational AI in customer-facing contexts is driven by hard numbers and analyst validation.
Gartner projects that by 2026, conversational AI deployments within contact centers will reduce labor costs by $80 billion. The same research estimates that one in 10 agent interactions will be automated by 2026, up from roughly 1.6% of interactions today.
Forrester's research reinforces the trend, stating that "You can use conversational AI for customer service to improve customer service, reduce costs for the organization, and create better agent experiences." Their Conversational AI Platforms Landscape report from Q4 2025 profiles 37 vendors in this space, indicating significant market activity.
The shift is also fueled by improvements in LLM capabilities. Forrester observes that "large language models (LLMs) and generative AI (genAI) enable chatbots and interactive virtual assistants (IVAs) that are smarter, more useful, and more conversational than before." These AI capabilities also enable faster application delivery versus traditional methods.
IDC's Conversational AI Tools and Technologies program confirms that organizations are recognizing ROI from applications such as chatbots, AI assistants, and copilots. For SaaS founders, this signals that conversational analytics has moved from experimental to expected.
How Do Data Agents, Semantic Layers, and Metric Consistency Work Together?
Delivering reliable conversational analytics requires three technical components working in concert: data agents, semantic layers, and metric consistency mechanisms.
Developers can use the Conversational Analytics API to build an AI-powered chat interface that answers questions about structured data. The API uses natural language to answer questions about data in BigQuery, Looker, and various SQL databases, providing business information and access to tools such as SQL, Python, and visualization libraries.
The semantic layer ensures that when two people ask "What's our MRR?" they get the same answer. As dbt's documentation states, "The primary value of the dbt Semantic Layer is to centralize and bring consistency to your metrics across your organization."
Row-level security adds another critical layer. It "lets you filter data and enables access to specific rows in a table based on qualifying user conditions," according to Google Cloud's BigQuery documentation. This extends the principle of least privilege by enabling fine-grained access control.
Ensuring One Source of Truth with the dbt Semantic Layer
Metric drift is a common pain point. Sales says revenue is $2.1M, finance says $1.9M. The dbt Semantic Layer addresses this by allowing data teams to define metrics on top of existing models.
Key benefits include:
Integration with BI tools like Tableau, Google Sheets, and Power BI for seamless metric queries
Support for major data platforms including Snowflake, BigQuery, Databricks, and Redshift
Centralized definitions that avoid duplicate code and ensure easy access in downstream tools
For teams using Kaelio, the platform connects directly to a company's existing data stack, including warehouses, transformation tools, and semantic layers. This means metric definitions already established in dbt or similar tools remain the source of truth while Kaelio adds a conversational interface on top.
How Do You Move from Pilot to Production Without Sacrificing Governance?
Moving from a proof-of-concept to enterprise-grade deployment requires attention to compliance, security, and workflow integration.
For organizations handling protected health information, HIPAA compliance is non-negotiable. The Health Insurance Portability and Accountability Act "governs the security and privacy of individuals' protected health information (PHI)," as outlined in Kustomer's HIPAA documentation. Platforms serving healthcare customers must sign Business Associate Agreements and implement specific security configurations.
Governance at scale also requires automation. A recent study found that 55% of companies use AI for alert triage and investigation, with projections that 60% of SOC workloads will be completed by AI within three years. The number of SOC 2 reports including confidentiality as an in-scope category increased from 34% to 64% between 2023 and 2024, reflecting growing enterprise demand for auditable systems.
Kaelio addresses these requirements through SOC 2 and HIPAA compliance, generating governed SQL that respects permissions, row-level security, and masking.
Why Does Row-Level Security Matter for Sensitive Data?
When customer success managers query account data, they should see only their assigned accounts. Row-level security enforces this automatically.
Row-level security "extends the principle of least privilege by enabling fine-grained access control to a subset of data in a BigQuery table," according to Google Cloud documentation. These policies act as filters to hide or display certain rows depending on whether a user is in an allowed list.
Practical implementation requires specific IAM permissions for creating policies, including bigquery.rowAccessPolicies.create and bigquery.tables.getData. The good news: row-level security is included with BigQuery at no additional cost.
Kaelio inherits these permissions, roles, and policies from connected systems, ensuring that the conversational interface never exposes data beyond what a user is authorized to access.
Where Do Conversational Insights Deliver ROI?
Conversational analytics drives measurable value across several customer-facing use cases:
Contact center optimization: The sentiment analysis feature analyzes messages during conversations to determine emotional intent. Sentiment scores ranging from -1 to 1 help teams identify which interactions need escalation or coaching.
Churn prediction: Companies that monitor usage closely and intervene proactively typically see 20-40% reductions in churn compared to those relying only on reactive support. Sentiment-driven models can achieve 85-92% churn prediction accuracy, enabling CS teams to intervene before accounts go dark.
Voice of customer analysis: Cresta AI Analyst answers natural language questions with explanations and evidence from conversations, providing insight into business strategy and CX operations. Philip Kolterman of Brinks Home shared: "With legacy analysis, getting to the nuance required manual review of transcripts. I'm thrilled to see AI Analyst come online... it can analyze calls for patterns that represent the real experience."
Revenue operations: Real-time access to pipeline, territory performance, and forecast accuracy helps sales leaders make decisions without waiting for weekly reports.
For teams using Kaelio, these insights flow directly from plain-English questions in Slack or the platform interface, with full transparency into how answers are computed.
Which Platform Fits Best, and Where Kaelio Wins
The conversational analytics market includes established players and newer entrants, each with distinct strengths.
Cresta focuses on contact center use cases, with users noting it "would best be utilized if your customer service team is large, services a high volume of customers, and the conversations with those customers are very detailed and long." However, comparative reviews suggest room for improvement in certain areas.
Gong excels at sales conversation intelligence, with users praising it as "one of the best if not the best tools we have in our tech stack in terms of ROI and ease of use." However, Gong is narrower in scope than general analytics platforms.
Databricks offers the Genie API for natural language data querying, providing Conversation APIs and Management APIs. A well-structured Genie space requires well-annotated data, user testing, and company-specific context. The API returns a maximum of 5,000 rows per query result.
Kaelio differentiates through several factors:
Accuracy: The platform prioritizes correctness by grounding every answer in existing data models and metric definitions
Transparency: Users see lineage, sources, and assumptions behind every result
Enterprise readiness: SOC 2 and HIPAA compliance, plus deployment options including customer VPC or on-premises
Model agnosticism: Works with any LLM provider based on customer requirements
Existing stack integration: Connects to dbt, LookML, MetricFlow, and other semantic layers without requiring replacement
Avoiding Common Pitfalls and Maximizing Adoption
Forrester's research warns that "Organizations are turning to conversational AI (CAI) to improve support experiences, but adoption is often harder than expected." Success depends on avoiding these common failure modes:
Misaligned business goals: Success depends on aligning CAI initiatives with clear business goals like reducing resolution time and improving satisfaction. Without measurable objectives, pilots stall.
Poor planning: Organizations must align the right teams and answer the right questions to avoid future pitfalls and roadblocks. This includes involving data engineering, security, and business stakeholders from the start.
Ignoring data quality: Databricks engineers found that by enforcing rules early in the development cycle, they significantly reduced database pitfalls. One database handled a 4X traffic increase while consuming fewer resources due to optimized efficiency driven by proactive scoring.
Alert fatigue: False positives erode trust and lead teams to ignore warnings. Effective monitoring requires not just detecting problems but responding consistently.
Skipping governance: Selection should reflect current needs and available resources, plus plans for future expansion. Building without governance creates technical debt that compounds.
Kaelio addresses these challenges through its feedback loop: as users ask questions, the platform captures where definitions are unclear or metrics are duplicated, helping data teams improve documentation and governance over time.
Start the Conversation With Your Data and Your Customers
Conversational analytics has moved from experimental to essential. IDC notes that vendors need to understand rapidly evolving buyer needs and differentiate themselves in a crowded market. For SaaS teams, this means the tools exist to give every stakeholder, from RevOps to customer success to the CEO, direct access to trustworthy answers.
The technical foundation matters: semantic layers that enforce metric consistency, row-level security that protects sensitive data, and compliance certifications that satisfy enterprise requirements. But the real value emerges when business users can ask questions in plain English and trust what comes back.
Kaelio brings together these capabilities in a platform designed for both startup speed and enterprise rigor. If your team is ready to replace dashboard sprawl and Slack threads with instant, governed answers, explore Kaelio to see how conversational analytics can work with your existing data stack.
About the Author
Former AI CTO with 15+ years of experience in data engineering and analytics.
Frequently Asked Questions
What is conversational analytics?
Conversational analytics allows users to query data in natural language and receive governed, auditable answers. It enables faster decision-making by letting employees ask questions in everyday language without needing SQL or data team intervention.
How does Kaelio integrate with existing data stacks?
Kaelio connects directly to a company's existing data stack, including data warehouses, transformation tools, and semantic layers. It uses established metric definitions and adds a conversational interface, ensuring consistency and governance.
Why is row-level security important in conversational analytics?
Row-level security ensures that users only access data they are authorized to see, enforcing the principle of least privilege. It provides fine-grained access control, crucial for maintaining data privacy and compliance in sensitive environments.
What are the benefits of using conversational analytics in customer-facing applications?
Conversational analytics provides faster insights, reduces reliance on data teams, and improves decision-making in customer-facing applications. It enhances customer service, optimizes contact centers, and aids in churn prediction by allowing real-time data interaction.
How does Kaelio ensure data governance and compliance?
Kaelio ensures data governance and compliance through SOC 2 and HIPAA certifications, generating governed SQL that respects permissions and security protocols. It integrates with existing systems to maintain data integrity and compliance.
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