- Capacity has launched its AI Analytics Assistant, a new way for contact center teams to get instant insights from their interaction data without exporting a single CSV.
- Ask a question in plain language, get a chart. Pin it to a dashboard. Send it to leadership on a schedule.
- Built for the people who need real data insights: CCOs, heads of contact center, ops leaders and QA managers.
- Close the gap between having data and being able to act on it with AI Analytics Assistant.
Most contact center teams aren’t short on data. They’re short on time to do anything with it.
Thousands of interactions happen every week across voice, chat, SMS and email. Every one of them contains crucial CX signals: what’s driving escalations, where automation is breaking down, which agents need coaching, what customers are frustrated about.
Right now, understanding these interactions means building a report. Or exporting a CSV. Or asking someone on the analytics team to pull something together and waiting two days for it to come back. By the time insights land, the weekly business review has already happened. The QBR deck has already been assembled by hand. The moment to act has passed.
That’s the problem Capacity’s AI Analytics Assistant was built to solve.
What is Capacity’s AI Analytics Assistant?
Capacity’s AI Analytics Assistant is a new enhancement inside the Capacity platform that lets anyone on your team ask questions about your interaction data and get immediate, visual insights.
Connected to your existing interaction history, the AI Analytics Assistant creates customizable and visual reports the moment you ask. From there, you can pin any output to a shared dashboard, turn it into an executive-ready presentation or schedule it to land in leadership’s inbox automatically.
The idea is straightforward: your data should work for you, not the other way around. Instead of manually exporting or compiling spreadsheets, you get answers and actionable insights, the moment you need them.
How does AI Analytics Assistant work?
Ask a question. Get a chart.
Type questions you’d ask any LLM, like “What are the top contact reasons driving escalations this week?” or “How has our containment rate changed month over month?”
The AI Analytics assistant will then generate a visual and insightful report on the fly, drawn from your actual interaction data.
From there, you decide how to leverage your insights:
- Pin reports to a dashboard. Any output can be pinned to a shared dashboard, so your team has a live view of the metrics that matter most, like containment rates, deflection trends or sentiment by channel.
- Turn insights into an executive presentation. Dashboards convert directly into shareable presentation views, executive-ready. Export as a PDF or share a live link.
- Run reports on a schedule. Reports and dashboards can be set to deliver automatically to stakeholders on a weekly or monthly cadence. Insights arrive in leadership’s inbox without them ever having to ask.
What kinds of questions can you ask?
You can ask the AI Analytics Assistant any question about your data, then use the answers to drive real, strategic decisions.
Here are some examples of what your team can ask:
- What are the top contact reasons driving escalations this week?
- How has our containment rate changed month over month?
- Which agents have the highest handle times on billing calls?
- Where are customers experiencing the most friction in our IVA?
- What’s our deflection rate, and what’s it costing us per quarter?
- Which chat topics are most associated with negative sentiment?
With AI Analytics Assistant, CX leaders can easily see gaps in their employee or customer support channels and how to improve. From first contact resolution to deflection rate, your interaction data can be actionable and helpful.
What does better contact center analytics do for your business?
Faster insights from contact center analytics have a direct impact on the metrics that drive contact center performance, customer satisfaction and overall revenue and costs.
Average handle time (AHT). When agents and supervisors can quickly see which interactions take the longest and why, they can pinpoint better solutions, like coaching, knowledge base updates or process changes. Shaving even 30 seconds off a high-volume contact type can translate to significant cost savings at scale.
First contact resolution (FCR). Low FCR is often a symptom of a knowledge gap or a routing problem. The AI Analytics Assistant can surface these patterns from your conversations, which would otherwise take weeks to identify manually, and give you what you need to reduce repeat contacts.
Deflection rate. Understanding which questions customers ask most, and which can be easily deflected from escalation, is one of the best ways to make your support strategy more effective and more cost-efficient.
CSAT and sentiment trends. Sentiment analysis across chat, voice and SMS lets QA managers and CX leaders track how customers are feeling across channels, in near real time.
Why contact center analytics has been so hard, and what changes now
The challenge has never really been the data itself. Contact centers have always generated more interaction data than they could analyze. The bottleneck has been the infrastructure required to do anything with it: dedicated BI tools, analyst headcount, weeks of dashboard configuration, manual export workflows that produce insights too slowly to act on.
That infrastructure made analytics something the analytics team did, not something a QA manager could do on a Tuesday afternoon, or a head of operations could do five minutes before a leadership meeting.
The AI Analytics Assistant changes the access model. Contact center data becomes something anyone on the team can query, visualize and share on demand, without a technical background, without waiting for someone else to build the report.
A QA manager can spot an emerging escalation trend before it hits the weekly review. An ops leader can pull a cost-per-contact breakdown the morning of a business case meeting. A CCO can walk into a board presentation with scheduled, auto-generated reports that reflect current data, not last month’s export.
This is what closing the gap between data and action actually looks like in practice. Not faster reporting, but reporting that doesn’t require a reporting team.
Want to get more insights from your data?
The data that support teams need to really boost their strategy has always been there. What’s been missing is a quick, easy and insightful way to analyze it.
The AI Analytics Assistant makes CX data something anyone can use, in the moment they need it. Ask a question, get a chart, share a dashboard, schedule a report.
Your data has the answers. Now you can actually ask for it.
with AI Analytics Assistant
FAQs
Capacity AI Analytics Assistant is an enhanced way of viewing and analyzing interaction data within the Capacity platform. Using plain language, users can ask questions and receive in-depth answers on support trends, metrics and gaps. They can also use AI Analytics Assistant to create custom dashboards or executive-ready reports.
No, once enabled, you can start asking questions of your data immediately.
Yes, AI Analytics Assistant can help you prepare in-depth reports on your interaction data to show to executives, heads of operations or QA managers. Using the data, users can not only show how well AI agents are performing but also where and how to improve.
