The story usually begins the same way. 

A company issues a request for proposal for a new contact center as a service (CCaaS) platform and the energy is high. Vendors line up to showcase their newest features, sleek interfaces and cutting‑edge AI capabilities. The sales cycle becomes a whirlwind of demos—routing logic, omnichannel capabilities, workforce optimization, automation add‑ons and a long list of functions meant to transform the customer experience. 

As the business and operations teams evaluate each solution, they meticulously map capabilities back to the stated objectives: reduce handle time, retire aging systems, improve customer satisfaction, modernize the agent experience.

Start with how success will be measured

In our experience, reporting and analytics are often brought into CCaaS selection too late, sometimes after vendor selection. And that delay can quickly expose gaps. 

The team begins evaluating what measures and key performance indicators (KPIs) the new solution natively supports and quickly realizes how difficult it is to align those measures with the original goals and success criteria defined earlier in the cycle. Questions surface: Which legacy metrics have no equivalent on the new platform? How will operational leaders continue to evaluate live agent performance? What happens to long‑established quality metrics that leadership relies on?

Then the automation team weighs in. They uncover missing visibility into bot containment, automation accuracy and handoff efficiency—metrics that had previously been well‑established on the legacy platform. Suddenly, critical components of the business case can’t be directly measured in the new environment without customization, third‑party add‑ons or rebuilding entirely new dashboards. 

These late discoveries can drive up costs, extend timelines and add complexity to an already ambitious implementation. What began as an exciting modernization effort now faces rework, technical debt and stakeholder frustration simply because analytics entered the conversation too late.

This is why we bring reporting and analytics into the discussion from the outset. Before a solution is chosen, before capabilities are compared, before any vendor makes it onto a shortlist, we focus on understanding how success will be measured. 

That includes defining goals and objectives, establishing KPIs and data requirements and agreeing on a return on investment model that can show whether those goals are being achieved. These measures provide the basis for evaluating customer experience, agent performance, automation strategy and business value.

What happens when analytics comes in late

For a leading retailer, tracking and optimizing automation performance ahead of Valentine’s Day, its busiest season, was critical to the move to a new CCaaS platform. However, detailed discussions around reporting expectations did not occur until project kickoff. It quickly became clear that customization would be required to adapt the new platform’s standard reports to the client’s specific needs.

Our CCaaS Solutions and Services team identified the relevant data sources within the new platform, built the logic needed to extract key performance metrics and developed visualizations to identify optimization opportunities. The team met the go-live date, although the additional reporting work required more resources than initially planned.

Using the new reports, our team identified underperformance in the ZIP code collection step after the initial go-live. This insight informed a redesign of the authentication process, reducing reliance on ZIP code verification. Across the wider transformation, call containment increased by 34.9%, reducing live-agent dependency and improving self-service. This shows how the right analytics can identify opportunities for ongoing optimization.

Design for continuous optimization

The engagement also showed the value of defining reporting requirements earlier in the sales cycle. Earlier involvement from our team could have identified measurement gaps sooner and allowed the optimization strategy, project scope, timing and costs to be planned more effectively.

For us, analytics provides the lens through which the wider CCaaS environment can be evaluated. Effective measurement helps organizations understand performance from launch and identify where further optimization is needed. When analytics strategy informs solution design from the beginning, organizations can reduce implementation surprises, maintain measurement continuity from legacy to future-state environments and gain insights that support continuous improvement. 

Ultimately, a successful CCaaS transformation depends on what the solution can do and how effectively its outcomes are measured, governed and optimized over time.