Better UC Dashboards with Power BI & IBCS

From virtual meetings to customer interactions, every call, chat, or meeting carries business insight into efficiency, reliability, and customer experience. Yet, organizations often face a common challenge: too many dashboards, not enough insight.

Every business has unique operational requirements, customer touchpoints, and strategic objectives that demand tailored analytics approaches. With business intelligence platforms like Power BI, the possibilities for creating meaningful insights become virtually limitless—you can connect disparate data sources, build custom visualizations, and design dashboards that align perfectly with your organization's specific needs.

However, to truly maximize the value of these powerful tools, it's essential to follow proven best practices for visual design and data presentation.

Using International Business Communication Standards (IBCS)

The International Business Communication Standards (IBCS) provide a professional framework developed by experts who understand how to transform raw data into clear, actionable insights that drive better decision-making across UC and Contact Center operations.

What is IBCS?

IBCS is a set of internationally recognized guidelines for the design of business communications—particularly dashboards and reports—making information easier to use, understand, and compare. The standards have been battle-tested in Fortune 500 finance, operations, and analytics settings and now power high-trust analytics for UC and Contact Center environments.

IBCS delivers three core benefits that directly address UC and Contact Center challenges:

With remote agents, AI agents, flexible scheduling, and customers expecting seamless omnichannel experiences, the clarity and comparability that IBCS provides is essential for maintaining operational excellence and customer satisfaction in a complex, multi-channel environment.

Power BI and IBCS: Analytics Made Actionable

Power BI is the ideal platform for UC and Contact Center analytics because it connects seamlessly to diverse data sources—from call quality and agent performance to customer satisfaction and workforce scheduling—bringing everything into unified dashboards tailored to your operations.

Its limitless flexibility eliminates the constraints of limited native PBX or contact center reports, which may overlook critical business context and lack comparability across channels.

At the same time, too much freedom can lead to inconsistency and confusion when different teams design dashboards without regulation or guidelines. Applying the International Business Communication Standards (IBCS) framework solves this by giving Power BI a consistent visual language that makes every chart comparable and immediately understandable.

With IBCS, stakeholders gain dashboards that not only look and feel the same across channels but also enable precise comparisons, faster decision-making, and clearer insights—turning Power BI’s infinite options into best‑in‑class analytics that drive real business impact.

Visual Best Practices for Call KPI's

Understanding which metrics drive performance is essential in contact center analytics, but knowing how best to visualize and interpret those metrics is what sets truly high-performing teams apart.

KPI Recipe Examples for Better Contact Center Insights

Each recipe below answers a key business question with the simplest, most effective visualization that adheres to IBCS standards. Use these as your starting point for building actionable dashboards in Power BI.

DIVIDE(
    COUNTROWS(FILTER('Interactions', 'Interactions'[AnswerTimeSeconds] <= [*SLA_Threshold_Sec*] && 'Interactions'[AnsweredStatus] = "Answered")),
    COUNTROWS(FILTER('Interactions', 'Interactions'[OfferedStatus] = "Offered")),
    0
)

SELECTEDVALUE('Targets'[SL_Target_Percent], 0.80)

CALCULATE([ServiceLevel_AC_%], SAMEPERIODLASTYEAR('Date'[Date]))

AVERAGE('Interactions'[QueueTimeSeconds])

SELECTEDVALUE('Targets'[ASA_Target_Seconds], 20)

CALCULATE([ASA_AC_sec], SAMEPERIODLASTYEAR('Date'[Date]))

AVERAGE('Interactions'[HandleTimeSeconds])

SELECTEDVALUE('Targets'[AHT_Target_Seconds], 300)

CALCULATE([AHT_AC_sec], SAMEPERIODLASTYEAR('Date'[Date]))

DIVIDE(
    COUNTROWS(FILTER('Interactions', 'Interactions'[AbandonStatus] = "Abandoned")), 
    COUNTROWS(FILTER('Interactions', 'Interactions'[OfferedStatus] = "Offered")), 
    0
)

SELECTEDVALUE('Targets'[Abandon_Target_Percent], 0.05)

CALCULATE([AbandonRate_AC_%], SAMEPERIODLASTYEAR('Date'[Date]))

COUNTROWS('Interactions')

CALCULATE(
    SUM('ForecastData'[ForecastValue]),
    'ForecastData'[Measure] = "CallVolume"
)

CALCULATE([CallVolume_AC_Count], SAMEPERIODLASTYEAR('Date'[Date]))

DIVIDE(
    COUNTROWS(FILTER('Interactions', 'Interactions'[IsFCR] = TRUE)), 
    COUNTROWS(FILTER('Interactions', 'Interactions'[HandledStatus] = "Handled")), 
    0
)

SELECTEDVALUE('Targets'[FCR_Target_Percent], 0.75)

CALCULATE([FCR_AC_%], SAMEPERIODLASTYEAR('Date'[Date]))

DIVIDE(
    SUM('AgentActivity'[HandleTimeSeconds]) + SUM('AgentActivity'[AvailableTimeSeconds]), 
    SUM('AgentActivity'[ScheduledTimeSeconds]), 
    0
)

SELECTEDVALUE('Targets'[AgentUtil_Target_Percent], 0.85)

CALCULATE([AgentUtilization_AC_%], SAMEPERIODLASTYEAR('Date'[Date]))