"We thought it would take three months. Eighteen months later, we were still trying to get the real-time dashboard right. By then, requirements had changed twice."
This candid admission from a Fortune 500 contact center leader captures the journey many organizations experience when attempting to build analytics capabilities in house. While analytics are now essential for modern contact centers, the build-vs.-buy decision is more complex than it initially appears.
The appeal of building your contact center analytics is understandable. Your organization has data analysts. You have access to your data. You have specific requirements. How difficult could it be?
Initial progress often creates a false sense of confidence. Your team connects to APIs, extracts call logs, and easily builds basic dashboards. This early momentum makes the in-house approach feel right. But that's merely the beginning of a far more complex journey.
The fundamental misunderstanding that derails many in-house projects is believing that simply extracting data is the main challenge. Turning raw contact center data into actionable insights requires specialized knowledge across multiple domains. Contact center data presents the following unique challenges:
- Scattered data sources: Call events, queue interactions, interactive voice response flows, agent states, and routing logic exist in different places and formats.
- Non-standard metrics: Even simple metrics like abandonment rate are calculated differently across platforms and contexts.
- System integrations: Most contact centers use multiple systems (CRM, workforce management, quality monitoring) that need integration.
- Real-time requirements: Operational dashboards need near-instant processing of streaming data.
What begins as "just a few dashboards" quickly evolves into a significant engineering project requiring expertise that most organizations don't have inhouse. Even after successfully building functional analytics, a new problem emerges: front-line users can't self-serve.
Contact center supervisors and managers—the ones who need insights most urgently—typically lack the technical skills to independently modify reports or create new views. Instead, they submit tickets to your data team, creating bottlenecks that slow decision-making. It involves the following steps:
- Supervisor identifies an issue requiring analysis.
- Request goes to the data team.
- Request waits in the queue with other priorities.
- Development finally gets scheduled (weeks later).
- Solution is built and tested.
- New report rolls out.
By the time answers arrive, the operational moment has passed. This friction decreases system usage, no matter how technically impressive your solution.
The most significant hidden cost isn't even in the initial build; it's in what follows. Contact centers constantly evolve in the following ways:
- Platform upgrades introduce new data structures.
- Business reorganizations affect reporting hierarchies.
- Queue configurations shift regularly.
- New communication channels emerge.
- Leadership priorities change.
Each change impacts your analytics system, requiring data models, calculations, dashboards, and documentation updates. You haven't built a product but committed to an ongoing relationship.
Industry data suggests maintenance typically consumes 40 percent to 60 percent of total analytics costs over five years. Instead of maintaining the status quo, these resources could drive innovation or customer-facing improvements.
Another hidden cost is building and maintaining analytics systems that misuses your best technical talent. Your data scientists and engineers were hired to drive innovation, create predictive models, and deliver competitive advantages. When they spend significant time maintaining operational dashboards and handling basic reporting requests, your organization loses in multiple ways:
- You're paying premium salaries for routine work.
- Strategic initiatives get delayed while your best minds fix broken dashboards.
- Technical professionals seeking growth become frustrated with mundane maintenance.
This misalignment creates immediate inefficiency and long-term strategic disadvantage.
The most dangerous hidden cost emerges, though, when homegrown analytics produce incorrect insights. Minor calculation errors or data misinterpretations lead to significant operational missteps, like the following:
- Staffing models built on flawed projections.
- Performance management using inaccurate metrics.
- Resource allocation based on incomplete analysis.
When these errors occur, you face immediate operational impacts and a more insidious problem: people stop trusting the data altogether.
Given these challenges, specialized contact center analytics solutions typically deliver better outcomes at a lower total cost by providing the following:
- Domain expertise: Embedding industry best practices and proper metrics developed across thousands of implementations.
- Speed to value: Delivering insights in days rather than months through pre-built integrations.
- User empowerment: Enabling front-line supervisors to explore data without technical assistance.
- Resource optimization: Freeing your data team to focus on strategic analysis.
- Continuous evolution: Incorporating new capabilities as the industry evolves.
These advantages come from technology and accumulated expertise, specifically in contact center analytics.
Despite these challenges, building internally might be appropriate in limited scenarios like these:
- When analytics is your core business.
- With unusual regulatory requirements that preclude third-party solutions.
- If you've built your contact center platform and possess both deep analytics expertise and operational knowledge.
But these situations are rare exceptions.
When deciding whether to build or buy contact center analytics, ask the following questions:
- Is analytics development truly central to your competitive advantage?
- Can you commit dedicated resources indefinitely?
- Are your needs genuinely unique?
- What could your technical team accomplish instead?
The most innovative organizations build where they differentiate and buy where others have solved the problem better. They use specialized analytics solutions to empower decision-makers, accelerate insights, and maintain focus on their core business.& In contact centers, where operational agility directly impacts customer experience, this focused approach reduces costs while creating a strategic advantage through better, faster decisions based on reliable data.
After all, analytics only matter when they drive action, and action happens when the right people have insights when they need them most.
Kimberly Rose is marketing director at Brightmetrics.