# How Data Powers Agent Productivity

DevFeed: [How Data Powers Agent Productivity](<https://devfeed.tech/articles/how-data-powers-agent-productivity-30517.md>)

Original publisher: [Read original article](<https://medium.com/helpshift-engineering/how-data-powers-agent-productivity-f310f414872d?source=rss----3229f31ca4f4---4>)

Author: Poorva Patil

Published: 2025-10-06T04:22:31Z

Content type: article

Language: en

Sources: [Helpshift](<https://devfeed.tech/sources/helpshift.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [apache-spark](<https://devfeed.tech/tags/apache-spark.md>), [big-data](<https://devfeed.tech/tags/big-data.md>), [customers](<https://devfeed.tech/tags/customers.md>), [data](<https://devfeed.tech/tags/data.md>), [data-analysis](<https://devfeed.tech/tags/data-analysis.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [support](<https://devfeed.tech/tags/support.md>)

## AI overview

This article describes how a customer support team developed custom metrics to measure agent productivity more accurately. The metrics aim to distinguish productive work from idle or merely available time and support staffing, scheduling, and performance decisions.

## Source excerpt

As a data engineer, I used to see metrics as just numbers on a dashboard -- until I realized they're the lens through which customers view and run their operations. In customer support, for example, agent productivity metrics aren't just figures, they're actionable insights that drive efficiency, shape staffing decisions, and directly impact customer satisfaction. These aren't just charts -- they help customers understand the value we provide, how well things are working, and what decisions to make next. Realizing this changed how I think about building analytics. ➡💡The Question That Shifted Our Perspective In customer support, how well the team works really matters. It affects how much the company spends, how happy the customers are, and how the team feels about their work. Support managers often ask: Are we staffed correctly for the volume we're handling? Are agents spending their time productively? How can we optimize scheduling and performance? When we began our Agent Workforce Management project, we already had a few standard metrics in place like online time, login time, and available time. These told us when agents were present -- but not what they were actually doing. Customers weren't asking "Are our agents online?" They were asking "How productive are our agents?" And truthfully, we didn't have a good answer. There was no visibility into how much time was being spent on real work versus idle time. No way to differentiate between being "available" and being "productive". This made it hard for teams to identify gaps, support high performers, or spot patterns that needed attention. This project was all about answering that question in the right way. 🔍📊 Custom Metrics We Built We designed a set of new metrics that give a clearer picture of how agents spend their time. These metrics give us a deeper understanding of how time is actually being spent, helping us move beyond assumptions and focus on what really drives productivity. Engagement Metrics These show how