Intro
Product Overview
Research & Findings
Problem Statements
Design Solutions
The Outcomes
The Learnings

No reporting at PandaDoc — CSMs pulled data manually, engineers ran ad-hoc queries, every request was manual.

Enterprise clients were manually emailing us for data that should be self-service.

I rewrote the PRD and redesigned the UI — pivot tables, filter system, 60%+ adoption in Q1.

B2B SaaS Data Visualization Self-Service Design Systems ~3 Months

Product
Overview

~3 Months 60%+ Q1 Adoption CSM + Eng Resources Saved 2022

Reporting offers insights into team performance and document workflows across workspaces — accessible to account owners, admins, managers, and users with reporting permissions. It covers document lifecycle stages: creation → sending → completion.

This was PandaDoc's first self-service reporting solution. Role: Product Designer — took over from initial designer, led from UI to release.

Research &
Findings

User Archetypes
The Sales Manager The Account Owner / VP Sales
Role Team lead tracking document lifecycle across reps. Needs operational insight, not raw data. Has Manager role with access to reporting. Executive or ops lead overseeing pipeline health across the whole PandaDoc account. Needs a high-level view, not granular row-level data.
Goals Understand where deals are stalling. Identify reps who need coaching. Get recurring health metrics without requesting them from CS each time. Prove the ROI of PandaDoc internally. Compare period-over-period document performance. Export summary data for board or leadership reporting.
Challenges Previously had to request custom reports from CS — slow and blocking. Raw data exports with no grouping were useless for management decisions. No way to track the same metric week-over-week. No self-service analytics existed — any custom query required engineering time. Couldn't slice data by team, period, or document type without help. No lightweight dashboard for quick status checks.
Skills Comfortable with table-based data tools. Expects filter + group + export as a baseline. Does not need charting unless it adds genuine clarity. Low tolerance for data complexity. Needs charts and summary numbers. Will not use a feature that requires configuration every session.
Research Method
Interview Re-Synthesis

Re-synthesis of 12 user interviews already conducted by a previous designer.

UserVoice Analysis

Analysis of 50+ UserVoice feature requests tagged to Reporting.

CSM Interviews

Interviews with CSMs responsible for 50+ enterprise organizations.

Validation Interviews

4 additional user interviews conducted to validate the audience pivot hypothesis.

Key Findings
FindingSourceImpact
Initial scope prioritized a visual dashboard — built for data analysts, not managers who needed to understand and act on dataInterview re-synthesisPivoted scope: table became primary, dashboard became lightweight overview
Without pivot tables and grouping, the table would show raw data only — useless for managersCSM interviewsScope shifted to table-first with pivot capability
CSMs were spending significant time building custom reports for enterprise clients on requestCSM interviewsConfirmed the business cost of not having self-service
50+ UserVoice requests clustered around filtering, grouping, and saved views — not chartsUserVoice analysisValidated the table-first priority
Users needed dynamic saved reports with relative dates ("last 2 weeks") not fixed date rangesUser interviewsDesigned saved reports with dynamic date presets
User Journey Map

Archetype: The Sales Manager  ·  Two scenarios: Discovery and recurring workflow

Scenario A — Manager discovers reporting for the first time
Stage
01Trigger
02Old Workflow
03Discovery
04First Use
05Insight
User Action
Manager needs to understand pipeline health — which deals are stalling, where reps need coaching
Manager contacts CSM requesting a report
Manager navigates to the new Reporting tab
Manager tries to filter documents by sender, status, time period
Manager identifies that a specific rep's documents consistently stall at "viewed"
Emotion
Blocked
Resigned
Skeptical
Engaged
Empowered
Pain Point (Before)
No reporting in the product. To get any data, the business had to contact their CSM
CSM → PM → EM → dev resources → report produced. Multi-week delay for a single data pull
Initial design prioritized visual dashboard — would have been useless for managers who needed to manipulate data
No filtering, no grouping, no pivot — raw export only
This pattern was previously invisible — required a custom engineering report to surface
Design Response
Self-Service Reporting built to eliminate the CS dependency entirely
Feature removes the entire chain: manager gets data on demand
Research synthesis mid-project shifted primary focus from dashboard to data table with pivot capability
Table designed with show/hide columns, pivot tables, dynamic filters, horizontal scroll for 30+ data points
Filtered, grouped table view makes per-rep, per-status trends immediately visible
Emotional Journey
Scenario B — Manager runs weekly reports as part of workflow
Stage
01Weekly Review
02Team Coaching
03Leadership Report
User Action
Manager opens Reporting to check the week's pipeline activity
Manager filters by rep to prep for a 1:1
Manager exports summary data for a quarterly business review
Emotion
Routine
Focused
Confident
Pain Point (Before)
Had to rebuild the same query every session — no way to save a view
No per-rep filter — data was aggregate only
No summary view — everything required deep table interaction; no clean export
Design Response
Saved reports with dynamic date ranges (last week, last month, last quarter) persist across sessions
Pivot table allows slicing by sender, document type, status, and time period in any combination
Lightweight dashboard tab alongside the table: creation, send, and completion metrics for executive-level snapshots
Emotional Journey
Cautious / Uncertain
Overwhelmed / Stressed
Neutral / In process
Relieved / Confident

Problem
Statements

No solution existed

PandaDoc had no reporting. Medium and large clients couldn't get insights without manual intervention. No competitive edge against tools offering built-in analytics.

﹅﹅

...we send monthly reports to leadership and have to pull the data manually every single time...

﹅﹅ ...our competitor tools have built-in dashboards — we're always asking PandaDoc to send us exports...

CSMs and engineers as manual data pipelines

Clients repeatedly explained their report parameters. CSMs and two engineering teams manually pulled data on a regular basis — an unsustainable drain on resources.

﹅﹅

...every quarter I email the CSM with the same report request and wait days to get it back...

﹅﹅ ...we have a standing monthly request for data exports — it shouldn't work this way...

Raw data only — no UI for non-technical users

For users who couldn't work with raw data exports, the solution had to be self-service and immediately usable. No middle ground existed — it was export or nothing.

﹅﹅

...I got the CSV export and had no idea what to do with it — I'm not a data analyst...

﹅﹅ ...I need to see document performance without opening Excel — a visual dashboard would solve this...

Ivan's challenges joining mid-project

First project at PandaDoc — had to sell ideas without a track record. Designers had less influence over scope. The Design System was one-directional: you used it, you couldn't contribute back to it.

﹅﹅

...you're new here — you'll need to prove this approach works before we commit to it...

﹅﹅ ...design system contributions go through a separate review cycle — it's not a fast process...

Design
Solutions

Dashboard overview

A summary view of the document pipeline: creation → sending → completion. Designed for managers and admins who often span multiple workspaces across different sales teams or departments.

Required

Summary view of the pipeline — document lifecycle: creation → sending → completion. Workspace switcher for multi-workspace managers and admins.

Challenge

Managers and admins often span multiple workspaces — different sales teams, or different departments: Sales, HR, Legal. The dashboard had to surface the right data without overwhelming the view.

Approach

Started with a high-confidence initial solution, planned user interviews to validate and iterate. Used a charts library to avoid over-investing before the solution proved its value.

PandaDoc Reporting — dashboard
Data setup: pivot tables over row settings

The team was struggling trying to let users specify which rows to show from ~25 available data columns. A more powerful solution was needed.

Before

Team was attempting to let users configure which rows to display — about 25 data columns available. Row configuration was complex, unintuitive, and limiting for the use cases reporting needed to serve.

After

Proposed pivot tables instead of row configuration settings — dramatically more powerful and immediately more intuitive. Showed and hid columns rather than rows. Made what users see exactly equal what they export, aligning the visual output to the exported data. Introduced horizontal scroll rules to fit all needed data without limiting visible columns.

Filters that became a design system component

Filters originally blocked the information hierarchy at the top. The repository view confused folder structure at scale. A better approach was needed — one that would scale across the entire product.

Before

Filters blocked the information hierarchy at the top of the view. Repository view confused folder structure at scale, making it hard to navigate large datasets.

After

Designed "revealable filters" — compact when not active, expanded when needed. This early version was later polished, became a design system component, and was adopted across the entire product. List view gave a data number win and improved context menu discoverability.

PandaDoc Reporting — filters, collapsed state PandaDoc Reporting — filters, expanded state

The
Outcomes

60%+ Q1 Adoption

Adopted by 60%+ of target audience in Q1 after release — strong signal that the self-service solution actually served the user need without handholding.

Resources freed

The company saved CSM and engineering resources — reports became fully self-service. Two teams previously running manual data pulls could redirect that time to higher-value work.

Competitive advantage

Gave PandaDoc a meaningful competitive edge — no comparable competitors had self-service reporting at the time of launch. Reporting became a selling point in enterprise deals.

Design system contribution

The revealable filter pattern became a core design system component used across the product. Horizontal scroll rules resolved long-standing data table responsiveness issues product-wide.

The
Learnings

Understanding how a company works is
its own design skill.

This project helped me understand how PandaDoc works and make adjustments in future interactions with product management. I learned the importance of system roles, permissions, and the routines of medium and large enterprise clients — knowledge that informed every project I led afterward.

Joining mid-project means
earning trust fast.

As the first project at PandaDoc, I had no track record to lean on. I had to pitch scope changes — pivot tables, horizontal scroll rules — and convince the team they were worth the investment. Getting those changes shipped taught me how to sell design decisions to product and engineering on a short timeline with limited social capital.