In this final article of our three-part series on the build versus buy decision for contact center analytics, we’ll explore the full economic picture behind this critical technology choice. For technology leaders, few decisions carry more long-term implications than evaluating whether to develop analytics capabilities internally or partner with specialized providers. The visible costs often mask the true economics at play.
Throughout this series, we’ve moved beyond surface-level comparisons to examine the full lifecycle impact of your analytics approach—from development resources to maintenance burdens, from adoption challenges to opportunity costs. This concluding piece will showcase implementation strategies and success stories that illuminate the path forward for forward-thinking organizations.
“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.”
That quote from a contact center leader at a Fortune 500 company sums up the journey many organizations experience when building analytics in-house. While having powerful analytics is no longer optional in today’s contact centers, deciding whether to build or buy these tools is far from straightforward.
Why the DIY Approach Is So Tempting
The appeal of building your own contact center analytics is easy to understand. Your organization has data analysts. You have access to your data. You have specific requirements. How hard could it be?
The initial phase typically goes well enough to create a false sense of confidence. Your team connects to APIs, extracts call logs, and builds basic dashboards with relative ease. This early momentum makes building in-house feel like the right call.
But that starting point? It’s just the tip of the iceberg.
The Complexity Hiding Beneath the Surface
The fundamental misunderstanding that derails many in-house projects is the belief that simply extracting data is the main challenge. Transforming raw contact center data into actionable insights requires specialized knowledge across multiple domains.
Your average data team might be stellar at building dashboards, but contact center data presents unique challenges:
Scattered data sources: Call events, queue interactions, IVR flows, agent states, and routing logic all live in different places and formats
Non-standard metrics: Even “simple” metrics like abandonment rate are calculated differently across platforms and business contexts
System integrations: Most contact centers use multiple systems (CRM, workforce management, quality monitoring) that need to be stitched together
Real-time requirements: Operational dashboards need near-instant processing of streaming data, fundamentally different from historical reporting
What begins as “just a few dashboards” quickly snowballs into a significant engineering project requiring specialized expertise you might not have in-house.
When the Data Team Becomes the Bottleneck
Let’s say your technical team successfully completes the project and builds functional analytics. A new problem quickly emerges: frontline users can’t self-serve.
Your contact center supervisors and managers – those who require insights most urgently – likely lack the technical skills to modify reports or create new views independently. As a result, they submit tickets to your data team, causing a bottleneck that significantly slows decision-making:
- The supervisor spots an issue and needs answers
- The request goes to the data team
- Request sits in the queue with other priorities
- Development finally gets scheduled (weeks later)
- The solution gets built and tested
- New report rolls out
By the time the answers arrive, the operational moment has already passed. This friction results in decreased system usage, regardless of how technically impressive your solution may be.
The Never-Ending Maintenance Nightmare
The most significant hidden cost isn’t in the initial build; it’s in what comes afterward. Contact centers never remain static. Platform upgrades often introduce new data structures, business reorganizations disrupt established reporting hierarchies, and queue configurations shift constantly to meet changing demands. Meanwhile, new communication channels emerge, expanding the data landscape, and executives frequently pivot to focus on different metrics.
Each of these changes impacts your analytics system, requiring continuous updates to data models, recalibration of calculations, adjustments to dashboards, and revisions to documentation. In reality, you haven’t just built a product but committed to an ongoing relationship, and it’s anything but casual.
Industry data shows that maintaining these systems typically consumes 40-60% of total analytics costs over five years. These resources could otherwise fuel innovation or improve customer-facing initiatives rather than being spent just to keep the lights on.
Misusing Your Best Technical Talent
Here’s another hidden cost: building and maintaining analytics systems wastes your technical talent.
Your data scientists and engineers are valuable resources hired to drive innovation, create predictive models, and deliver competitive advantages. When these professionals spend significant time maintaining operational dashboards and handling basic reporting requests, your organization loses in three ways:
- Wasted money: You’re paying high salaries for routine reporting work
- Missed opportunities: Strategic initiatives get delayed while your best minds fix broken dashboards
- Talent drain: Technical professionals looking for growth get frustrated with mundane maintenance
This misalignment between skills and responsibilities creates immediate inefficiency and long-term strategic disadvantage.
The Steep Price of Getting It Wrong
The most dangerous hidden cost arises when your homegrown analytics generate incorrect insights. Even minor calculation errors or misinterpretations of the data can trigger significant operational missteps, such as staffing models built on flawed projections, performance management driven by inaccurate metrics, resource allocation based on incomplete analysis, and strategic decisions guided by misleading trends.
When these errors take root, the consequences extend beyond immediate disruptions. They erode trust in the data itself. Once users question the insights’ reliability, adoption declines sharply, rendering even the most advanced analytics system ineffective.
Preventing these pitfalls requires more than good intentions. It demands robust validation methods, consistent calculation practices, and vigilant maintenance of data integrity—all within a constantly evolving environment. Unfortunately, these capabilities often require specialized expertise that many internal teams do not possess.
Why Specialized Solutions Often Win
Given these challenges, specialized contact center analytics solutions typically deliver better outcomes at a lower total cost by providing:
- Domain expertise: They embed industry best practices and proper metrics developed across thousands of implementations
- Speed to value: Pre-built integrations and standard dashboards deliver insights in days rather than months
- User empowerment: Intuitive interfaces let frontline supervisors explore data without technical help
- Resource optimization: Your data team can focus on strategic analysis rather than building basic reports
- Continuous evolution: Vendors invest in ongoing development, incorporating new capabilities as the industry evolves
These advantages come not just from technology but also from a concentrated focus and accumulated expertise in contact center analytics.
When Building Might Make Sense
Despite everything, there are legitimate scenarios where building internally might be appropriate:
- When analytics is your core business: Building custom capabilities might make sense if your products integrate with contact centers and offer analytics as a primary value proposition.
- Unusual regulatory requirements: Organizations with specialized compliance needs that preclude third-party solutions might require custom development.
- Complete data stack ownership: If you built your contact center platform and have deep analytics expertise and operational knowledge, you might find success in-house.
But these situations are rare exceptions. Most organizations benefit from focusing on their core business while leveraging specialized analytics.
Making the Smart Choice
When deciding whether to build or buy contact center analytics, ask yourself:
Is analytics development truly at the core of your competitive advantage?
Organizations rarely compete based on having better internal dashboards. Advantage typically comes from service quality and customer experience, which are enabled by analytics rather than the analytics themselves.
Can you commit to dedicated resources indefinitely?
Building is never a one-time project but a permanent commitment. Be honest about whether you can allocate technical resources long-term.
Are your needs really that unique?
Many organizations overestimate their uniqueness. Vendors working with thousands of contact centers have typically solved most requirements, often more elegantly than internal teams.
What could your technical team be doing instead?
Resources dedicated to analytics cannot simultaneously work on customer-facing innovation. This opportunity cost must be considered.
Focus Where It Matters
The build-vs-buy decision ultimately comes down to focus and strategic resource allocation.
Organizations succeed by concentrating on their core mission – delivering exceptional customer service, optimizing efficiency, and driving business outcomes. Analytics should enable this mission, not distract from it.
When internal teams build analytics solutions, they are responsible for a complex technical domain with significant hidden costs, diverting resources from initiatives that might create a genuine competitive advantage.
The smartest 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 the customer experience, this focused approach reduces costs while creating a strategic advantage through better, faster decisions based on reliable data.
Ultimately, analytics only matter when they drive action. Action happens when the right people have access to insights at the moment they need them most.
Explore the Build vs. Buy Series
Part 1: The Technical Reality of Contact Center Analytics: What IT Leaders Need to Know
Part 2: Transforming Contact Center Operations Through Advanced Analytics
Download the eBook: Build vs. Buy: Making the Right Call on Contact Center Analytics
