🔬 UX Research · Data Analytics · Strategic Design

How I Used 1.78M
Agent Sessions to
Redesign a Dashboard

A data driven investigation into agent behaviour analysing login frequency, task prioritisation, and click efficiency to deliver strategic UX recommendations that directly shaped the dashboard roadmap.

1.78M

Sessions Analysed

Teams × 4 Months

85%

Task Completion

Design Changes

01 The Problem

Agents were frustrating our support team. We didn't know why.

The PPH (Pay Per Head) platform's customer service team was handling a recurring surge of support tickets every Sunday night and Monday and system maintenance windows were regularly disrupting agents during their most critical work moments.

No one had mapped when agents actually used the platform, what they did when they got there, or why certain moments felt broken. As Senior Product Designer contractor at Fortem Services, I proposed and led a structured behavioural analytics investigation to answer three core questions.

The 3 Questions We Set Out to Answer

When do agents log in?

What do they do first?

Task Prioritisation Analysis their journey across 3 interactions mapped through Matomo

How efficiently can they do it?

Click Efficiency Analysis average clicks per action and exit behaviour

03 Method & Approach

Data-first. People-second. In that order.

Before interviewing anyone, I started with the data. I knew that qualitative research alone wouldn't be credible with a product and engineering team that was skeptical of "soft" UX feedback. So I anchored everything in numbers, then layered in human context.

Tableau Login Frequency Analysis

Pulled 5 quarters of login data (Q2 2023 → Q2 2024). Analysed login days, hours, and average sessions per agent per day to identify consistent peak patterns across the year.

Stakeholder Interviews CS Team & POs

Took the quantitative peak data to the CS team and Product Owners to ask "why." This revealed the 3 behavioural factors Billing cycles, User routines, and Monday Night Football preparation.

Matomo Task Prioritisation & User Flows

Used Matomo's user flow visualisation to map the 1st, 2nd, and 3rd interactions across 4.7M visits, identifying where agents went and in what sequence after login.

Click Efficiency Analysis

Mapped the average clicks required for each core agent action. Used dashboard-as-starting-point as the baseline. Identified exits (14.68%) as the key area for future investigation.

Synthesis & Recommendations to Product

Compiled all 3 analyses into a structured report and presented to the product leadership team. Led the session and fielded questions from engineering and business stakeholders.

04 Key Findings

What the data revealed

Finding 1 Login Frequency

Agents log in most on Sundays at 21:00 and Mondays at 22:00 consistent across 5 consecutive quarters of data.

Mon

High

Tue

Low

Wed

Low

Thu

High

Fri

Mid

Sat

Mid

Sun

Peak

💳

The Billing Factor

Weekly billing cycle runs Mon–Sun. Agents check figures on Sunday and settle payments by Thursday creating 2 peak login days driven by financial deadlines.

🏈

Monday Night Football Prep

Bookies log in Monday evenings to adjust odds, manage player injuries, and settle weekend accounts before the NFL game kicks off.

Finding 2 Task Prioritisation

Across 4.7M sessions, agents' journeys are remarkably predictable: Dashboard → Bettor Balance → Bettor Details.

1st Click

95.8%

Dashboard (95.76%)

Other (4.24%)

2nd Click

56.6%

Bettor Balance (56.59%)

Dashboard (19.19%)

Other (6.63%)

3rd Click

46.4%

Dashboard (46.40%)

Bettor Details (31.75%)

Bettor Balance (8.90%)

💡

Key Insight: The Dashboard is the control room.

Agents return to the Dashboard between actions (46.4% of 3rd clicks), which means it's being used as a navigation hub not just a summary screen. This validated making quick links a priority recommendation.

Finding 3 Click Efficiency

Of 1,782,473 total dashboard visits, 85.32% of agents proceeded to complete their intended journey. The 14.68% exit rate is the key opportunity area.

Completion Rate

85.3%

Agents successfully proceed from Dashboard to their intended next destination

✓ Positive signal

Exit Rate

14.7%

Agents exit from the Dashboard without completing a task the priority investigation area

→ Needs investigation

Total Sessions

1.78M

Dashboard visits analysed across the full data window, providing statistically significant insight

✓ High confidence data

05 Recommendations

Three changes I drove into the roadmap

These weren't just suggestions I presented these findings to product leadership and advocated for each recommendation specifically, tying them directly back to the data.

Reschedule all maintenance windows

Data shows peak logins are Sunday 21:00 and Monday 22:00. Maintenance should only run between 9:00am–12:00pm on Tuesdays and Wednesdays when usage is lowest. Agents must receive advance notifications.

High Priority

Add quick links to the Dashboard

Since agents return to the Dashboard as a navigation hub (46.4% of 3rd clicks), surface the 3 most-accessed sections Bettor Balance, Bettor Details, and Manage Payment as quick links directly on the dashboard.

High Priority

User interviews to understand exits

The 14.68% exit rate from the dashboard needs qualitative research to understand intent. Some exits may represent satisfied task completions others may signal friction. We need to know which before designing a solution.

Medium Priority

06 · Reflection

What I'd do differently & what I learned

What worked well

Leading with data built credibility

By anchoring every finding in Tableau and Matomo numbers before introducing qualitative context, I was able to earn buy-in from a sceptical engineering and business audience. The data made the "soft" UX recommendations feel rigorous and actionable.

What I'd do differently

Tie recommendations to business metrics earlier

I'd want to estimate the revenue or retention impact of each recommendation upfront e.g. "reducing exits by 5% equates to X fewer support tickets/week." This would have strengthened the prioritisation argument and made the business case more compelling to leadership.

What I learned about leadership

Research is only valuable if it drives action

The research was thorough but the real skill was in presenting it persuasively. I learned to tailor the narrative for each stakeholder audience: engineers heard data, product heard prioritisation rationale, and business heard revenue protection.

Senior-level takeaway

Define the question before collecting the data

The most valuable decision I made was framing 3 specific research questions before opening Tableau. This kept the investigation focused, prevented analysis paralysis, and made it clear when we had enough data to make a confident recommendation.