Nancy Nguyen
UX research case study
Company: ServiceNowProduct: Store

ServiceNow Store: Building Trust in an Enterprise App Marketplace

Partner apps make up a significant portion of ServiceNow's app marketplace, yet customers rarely adopt them, and nobody knew why.

I analyzed a survey, then followed it with journey-mapping interviews to find where app buying breaks down and what trust signals customers value to inform the long-term vision for Store.

🔒 Some internal names, participant details, and figures have been generalized to respect confidentiality.
ServiceNow Store Explore page on a desktop monitor, on a warm yellow background

Role

Lead UX Researcher

Timeline

3 months

Skills

  • Survey analysis
  • Data processing
  • Journey mapping
  • AI-assisted research ops

Tools

Great Question
Qualtrics
Figma
Miro

Results at a glance ✨

The Strategic Shift: From Partner App Quality to Discovery and Trust

Customers default to native apps because nothing in the ecosystem advocates for partner apps at the right moment. Store needs a context-aware AI native model to close that gap.

Reframed Store's improvement priorities from partner app quality to discovery and trust, as survey signals suggested the product delivered once customers got there (57% satisfied at installation) while the buying journey did not (31% dissatisfied at evaluation).

Made Discovery the team's top investment priority after a directional survey signal showed 64% of respondents dropping off in the early stages, compared with only 11% after purchase, then validated why through interviews.

Presented findings to 20+ stakeholders, elevating unified navigation to a high priority across ecosystem initiatives and moving SSO login for Store into progress.

Built the Trust Equation, a reusable model of what makes a partner app credible enough to buy, and logged the remaining recommendations as actionable product tickets.

Built a Claude journey mapper that reduced time-to-insight from days to hours; eventually added to the department's research ops toolkit.

My role

Lead UX Researcher
Owning end-to-end research for ServiceNow Store, from research planning and execution to analysis and reporting

Nancy Nguyen at the ServiceNow office
  • Processed and evaluated a partner app procurement journey survey to identify critical friction areas and inform prioritization.
  • Designed and led 7 in-depth interviews (structured Q&A plus a Claude-assisted journey-map co-building activity) with customers and internal Solution Consultants across five industries.
  • Mapped the customer discovery journey across 10 stages, surfacing the specific friction points and trust signals driving early drop-off.
  • Synthesized findings into the Trust Equation and a North Star vision for a trusted, AI-assisted discovery hub for buyers and sellers.
  • Delivered readouts to 20+ stakeholders, turning recommendations into prioritized initiatives and actionable product tickets.

🌱 Why I took this on

I stepped in to lead Store research when another researcher went on leave, inheriting a survey that was already in the field. Rather than simply finish the analysis, I saw a chance to own a research program end to end.

The timing mattered too. My team was leading an org-wide effort to consolidate ServiceNow's digital properties, and Store's place in that future was still open.

How I used AI

AI as a research partner in the room

🎯 What it deliveredWhat started as a tool for one study grew into a capability used across departments
Days to hours

Reduction in time-to-insight, with journey maps built live during each session

Speed
Across teams

Reconfigured for the Solution Architect team to map solution blueprints and for field marketing to map event timelines, then added to the department's research ops toolkit

Adoption
Leadership

Presented to UX Directors and the SVP of Experience; showcased to the research and design organization

Visibility

🤝 Partnering with AI

I developed an interview method where AI transcribes and maps customer conversations in real time, and participants drag, drop, and correct their own journey maps.

After feedback from my team, I added real-time probing questions for the researcher and a way to merge journey maps across sessions to spot recurring patterns. I also used Claude as a thinking partner while learning survey analysis on the job.

  • Provided an instant visual reference
  • Eliminated the note-taking workload
  • Improved data accuracy and participant engagement
  • Accelerated cross-session synthesis
  • Weighed trade-offs of different data cleaning thresholds
  • Drafted theme categories from raw blocker text
The journey mapper tool: a live transcript on the left, a current-state journey map with color-coded elements on the right, and suggested probing questions
The journey mapper: live transcript, color-coded journey elements, and suggested probes. Participant details are blurred.

Background

An enterprise marketplace for extending the platform

ServiceNow Store is a cloud-based enterprise marketplace where companies browse, try, and deploy free and paid applications, integrations, and AI agents.

📖 What is a partner app?An application built by a third-party independent software vendor (ISV) and distributed through Store, alongside ServiceNow's own native apps.
store.servicenow.com
ServiceNow Store homepage with a featured banner and a row of featured partner apps
The Store homepage, where native and partner apps are featured side by side.

Why it mattered

For ServiceNow, a thriving partner ecosystem is a primary driver of long-term business growth and customer retention. Partner apps:

  • Provide niche solutions that fill native gaps in the platform
  • Enable customers to solve more business problems
  • Act as an external, zero-cost marketing and sales force
  • Lead to larger ServiceNow contracts

Research approach

Defining the Challenge

Problem

Low

partner app adoption, even though partner apps make up a significant portion of Store's catalog. Nobody knew why.

Goal

Understand customer sentiment toward partner apps and the app discovery journey, from first exposure to purchase, to inform Store's product roadmap.

Research questions and how I answered them

  1. How do customers feel about partner apps?
  2. At what exact stage do customers drop out of the buying journey?
  3. What barriers hold people back at each stage?
Study 1 (Diagnostic)Directional and attitudinal
  • In-product survey (N=45)
  • Funnel mapping
  • Blocker analysis
  1. How do people discover and evaluate apps outside of Store?
  2. What trust signals do customers need before deploying an app?
Study 2 (Generative)Generative and behavioral
  • Behavioral interviews
  • Live journey co-building
  • Ideal-state mapping

Research journey

Mapping the Partner App Buying Journey

How a 2-part research strategy moved Store from guessing at partner app quality to a validated, discovery-first vision.

  1. 1.5 monthsStudy 1Partner App Adoption Barriers
  2. 1.5 monthsStudy 2App Discovery Journey
  3. FinalOutcomeNorth Star for Store Discovery

👆 Select a study to see how it unfolded.

📊 Study 1 | Diagnostic | 1.5 months

Partner App Adoption Barriers

I inherited this study after the survey was already in the field, with no handoff on its original design logic. The short survey ran directly on the Store site and reached 45 customers and partners across four roles: Instance Admins, Technical Architects, Developers, and App Admins. I cleaned and grouped every response, then mapped them onto the five-stage procurement funnel (Discovery, Request, Evaluate, Install, Renew) to see where customers dropped off and why.

🔬 Research methods
  • In-product survey (N=45)
  • Funnel mapping
  • Blocker analysis
  • Satisfaction comparison
💡 The turning point

The survey pointed to the early stages, while customers were discovering and evaluating apps, as the place partner apps were being lost. Directionally, early stages lost 64% of respondents, while installation and renewal lost only 11%, and satisfaction climbed once customers actually installed an app. The signal suggested the problem was Store's journey, not partner app quality.

📏 Reading these numbers as directionalWith 45 responses, this survey can't be generalized statistically across Store's customer base, and it captured self-reported perception and intent rather than behavior. I kept the analysis at the funnel-stage level instead of slicing it into small subgroups, and treated the results as a signal of where to look, not proof. Study 2 was designed to validate them and explain why.

Where to invest first, stage by stage

Highest priority

🔍 Discovery

Very frustrating to separate Partner and ServiceNow apps.

Barrier: partner apps blend in as noise and lack trust signals, and nearly half of respondents reported struggling to find apps across channels.

High priority

🧪 Evaluation

Reviews [on Store] are never many and ... can really skew how an app is looked at.

Barrier: widely used sources aren't seen as useful. Respondents rated trials the most useful, yet many never got that far.

Medium priority

📝 Request

Barrier: unclear request workflows, permission confusion, and costs that aren't shown upfront cause some people to abandon before they even try.

Low priority

✅ Install and renew

Barrier: conversion improves after purchase, and satisfaction is relatively high once an app is installed.

🧭 The rationale

Why a survey inside Store

Placing the survey directly in the product let us reach many respondents quickly, right where the buying behavior was happening.

Why a funnel

Mapping every response to a stage turned scattered feedback into a clear picture of where customers stopped and what blocked them at each step.

Why interviews came next

A small survey shows where to look, not why. I used its early-stage signal as a starting hypothesis and designed Study 2 to validate and explain it in depth.

Partner app adoption funnel for 45 respondents: 73% discovery, 51% request, 36% evaluate, 31% install, 24% renew
A directional read of 45 responses: Discover, Request, and Trial showed the steepest drop-offs, and conversion improved after purchase.
Priority map across the five stages, with Discovery marked highest priority
The recommendation to leadership, informed by the survey signal: fix Discovery first.

Key insights

What customers need to trust and buy partner apps

Four insights, each tied to an action the team took.

1

Intent-based search removes guesswork

Customers arrive at Store knowing the problem they're trying to solve, not an app's name or category. Store's search doesn't recognize that, so customers turn to places where they can describe their problem in plain language, like Google or AI tools.

✅ Outcome

Unified navigation became a high priority across ecosystem initiatives and fed my team's wider site consolidation work. Intent-based search was logged as actionable product tickets.

2

Trust signals enable credibility

Customers need four things before they'll buy: functional proof, ServiceNow or peer endorsement, vendor credibility, and self-serve information. Partner apps most commonly fail on functional proof and endorsement.

✅ Outcome

Recommendations like workflow screenshots, short walkthrough videos, and visible vendor support were logged as actionable product tickets, and SSO login for Store moved into progress.

3

Equip Solution Consultants to champion partner apps

Customers rely on Solution Consultants as a trusted internal source, but consultants aren't enabled or incentivized to surface partner apps. They rely on informal methods, like asking colleagues or searching piecemeal.

✅ Outcome

A structured, searchable internal brief on partner apps for niche use cases was logged as an actionable product ticket.

4

Cost transparency promotes confidence

Comparing apps and cross-checking entitlements are manual, time-consuming processes. Without visibility into total cost of ownership (acquisition, installation, and maintenance), customers abandon partner apps.

✅ Outcome

A comparison tool that surfaces hidden costs upfront was logged as an actionable product ticket, so customers can rule out unaffordable options early.

Challenges

What made this hard

⚠️ The challenge

I inherited Study 1 when the previous researcher went on leave, just after the survey launched. With no handoff on the original design logic, I had to lead my first end-to-end survey analysis from scratch, deconstructing the survey's structure while learning the ropes of survey analysis.

Steps I took to overcome it

  • Met with stakeholders to understand the decisions they needed to make, so the analysis stayed tied to team OKRs.
  • Audited the survey itself (design, question wording, segmentation, and outcomes) to understand exactly what the data could and couldn't tell me.
  • Reached out to researchers with survey experience and ran feedback sessions to pressure-test my approach.
  • Used Claude as a thinking partner, working through the trade-offs of different cleaning thresholds and drafting theme categories from the raw blocker text.
ResultTaking on this analysis from scratch gave me hands-on experience with data cleaning and taught me to balance tight deadlines with reaching a minimum sample size. It made me a more confident, adaptable researcher, and it shaped how I've approached every study since.

Reflection

This project marked my shift from tactical user research to driving high-level product direction.

Picking up a study midstream and carrying it through to strategy taught me two lessons:

Lesson 1

🔗 Let each study set up the next

The survey told us where customers dropped off, but not why. By treating its findings as directional and designing the interviews to go deep on the early stages, I turned a vague drop-off problem into specific, actionable direction. Sequencing methods deliberately made each study stronger than it would have been on its own.

Lesson 2

🧭 Study the ecosystem, not just the product

The biggest barriers didn't live on Store's pages. They lived in Google searches, YouTube demos, peer conversations, and sales incentives. Looking beyond the product surface explained why customers defaulted to native apps, and it's why the North Star centers on a context-aware AI model rather than UI fixes alone.

Store doesn't need to be the only place customers discover apps. It needs to be the smartest one, and research is how we learn what "smart" means to the people using it.