Platform Analytics Foundation Research
Interviews • Survey • Jobs-to-be-Done Foundational Research
Overview
As ServiceNow's Platform Analytics moved from classic reporting toward a unified Analytics Workspace; however, one question sat underneath every roadmap decision: who actually relies on business analytics, and why?
This project, was one of the first I took on, when joining ServiceNow, and it is set out to answer that: not for one feature, but for the discipline as a whole. It's probably one the research projects I'm proudest of at ServiceNow: it didn't just answer a stakeholder ask, it built the vocabulary and baseline knowledge still relevant, and in use today, for users, for journeys, for needs, years after the studies closed - and even after the explosion of AI tools in company's workflows.
Note: In this page, specific results are are described directionally, instead, in line with ServiceNow's confidentiality commitments. I'm happy to go deeper in conversation.
Team
• 1 Staff UX Researcher (yours truly)
• Multiple Product Managers, Product Design, and Engineering stakeholders across the company
My Contributions
• Led User Research end-to-end, across studies:
• Reframing the initial asks into structured research questions;
• Planning, running, and analysing connected studies;
• Synthesising results into user groups, journeys, and needs
• Sharing results and driving their adoption across teams
Timeline
• Q2 - 2023
• Q2 - 2024
(Due to competing priorities with other projects, these were NOT done back-to-back)
The Process
For this research project, I followed a 6-stage process: Kickoff, Planning, Preparing, Running, Analysing, and Sharing. In the next sections, I'll provide a summary of each stage. As with any exploratory research, some stages ran in parallel or looped back on each other rather than strictly in sequence.
Kickoff
This project didn't start from a blank page. It continued research already begun by the Design team, as such, the kickoff stage focused on:
Scope: was this about analytics creation only, or the full ecosystem, including consumers of data analytics?
Stakeholders: Research, Design, Product, and Engineering across Platform Analytics (and other relevant BUs).
What we already knew: a body of unfinished interviews existed and needed review before deciding what new data was needed.
Why this mattered: Platform Analytics needed to retain and grow its creator base, while also bringing new, less technical types of users into its new environments.
Planning
I turned the kickoff discussion into a formal Research Plan, structured around three main goals:
Identify the patterns in who uses business analytics solutions, and what they're trying to accomplish;
Determine the context in which people reach for analytics;
Identify the tasks people perform to get there, and where ServiceNow could help.
Research Questions
The plan organised around questions like:
What do people who use business analytics solutions have in common?
What responsibilities do they hold, and how do they interact with each other?
Where do analytics solutions fit into their day-to-day work, and when does that relationship begin?
What's stopping people from fully achieving their goals with these tools?
Methodology
Similar to other studies I've done before (but perhaps more explicitly), this was framed as Jobs-to-be-Done (JTBD) centric, exploratory research, run in two stages:
Stage 1: Understanding the problem space
A combination of desk research (auditing and re-analysing existing, unfinished interview data, as well as relevant external research) and moderated 1:1 user interviews, to explore participants' responsibilities, goals, and processes when creating or consuming analytics.
Stage 2: Validating and prioritising insights
A quantitative survey, built on Study 1's findings, to measure the importance and satisfaction of specific analytics-related activities (segmented by role, seniority, and company profile) in order to surface which needs were genuinely unserved.
Preparing
During this stage, we created all the resources needed to run the accepted research methodology.
For Study 1, I prepared a semi-structured interview script, (alongside all operational resources -e.g., screener, communications, etc...) focused on participants' real recent experiences with analytics - not hypotheticals - including recruitment criteria across multiple role types and ways of working (building, analysing, planning, fixing).
For Study 2, I designed a structured survey, covering: screening and segmentation questions (such as role, seniority, company size, ServiceNow usage), and a main survey measuring motivations, and the importance/satisfaction of both exploration and creation activities, with follow-up questions on tool selection, prioritisation, and system performance.
Running
As the name suggests, this phase involves the running of the user interviews sessions and, later, the publishing of the survey.
Stage 1: User Interviews
I ran 4 sets of moderated 1:1 interviews with different segments of people who build, analyse, plan from, or fix issues using business analytics solutions. Deliberately, these were not limited to ServiceNow customers, since the goal was to understand analytics users broadly, not just our own product's performance.
Stage 2: Survey
The survey reached a large, statistically meaningful sample (ommitted due to NDA purposes) of ServiceNow customers, spanning company sizes from small business to very large enterprise, and covering the full range of roles identified in Study 1.
Analysing
Stage 1: User Interviews (Defining users and journeys)
This stage was run almost in parallel to the Running stage - after all, analysis begins at the end of the first session.
Through thematic analysis of the interviews, I characterized four core user groups based on how people engage with analytics — from those who build solutions for others, to those who only ever consume them. Each group maps to concrete roles (e.g. Data Analyst, Business Analyst, Process Analyst, Product Manager), which I used to propose new and updated persona documentation for the company.
I also mapped "The Analytics Cycle" — a journey model describing how people move between exploring existing analytics and building new solutions, each broken into distinct stages. This became the shared language used to talk about where in the journey a problem, or opportunity, actually sits.
Stage 2: Survey (Prioritising needs)
I used an importance x satisfaction framework to identify where the biggest gaps existed - i.e., activities that people rated as highly important, but were not currently satisfied with. Segmenting these let us see which needs were universal versus specific to a type of user, giving us a prioritised, evidence-based view of where to focus next.
Sharing
Results were shared as two full reports (one per study) plus continuous updates to stakeholders throughout, so teams didn't have to wait for a final readout to start acting on emerging signals.
Beyond the reports, this research fed directly into the company's Persona library, giving Design and Product a shared, reusable reference for who they're building for (not just a one-time deck). User Journeys, groups, and roles were also made available company wide... and used to decorate our office :)
Outcomes
This was foundational research in the truest sense! The user groups, the journey model, and the framework it produced are still reference points the team uses today, well after the original studies closed, and well after the explosion of AI in company's workflows.
Concretely, this research:
Defined the personas now used across Platform Analytics' Design and Product teams
Mapped the journeys (The Analytics Cycle) that continue to frame how the team discusses user problems
Prioritised the jobs and needs that shaped subsequent roadmap and research investment: including proposals for continued, "next generation" foundational research to keep this knowledge current as the product evolves (which started happening later in 2026).
It's rare for foundational research to still be load-bearing years later. This project is the reason I keep making the case for doing it properly the first time.
© Tiago Gonçalves, 2026