ServiceNow Store | UX research case study
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 led research 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 ✨

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.

Identified 4 elements of buyer trust and the two partner apps most often lacked.

Refocused the team's top priority on Discovery and Evaluation after the survey signaled 64% drop-offs.

Shifted Store's roadmap from near-term fixes to a long-term vision.

Created a shared evidence base across departments.

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 70+ survey responses and analyzed the final 45 legitimate ones to build a drop-off funnel.
  • Designed and led in-depth interviews with 7 participants.
  • Mapped the customer app discovery journey across 10 stages.
  • Synthesized findings into the Trust Equation and a North Star vision.
  • Delivered final recommendations to 20+ stakeholders.

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 buying journey to inform 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

Following customers from first search to final decision

Customers arrive ready to solve a specific business problem, but weak search, navigation, and filtering stall them early. Even when they find the right app, they can't get the quick information they need.

  1. 1.5 monthsStudy 1Pinpointing where customers dropped off
  2. 1.5 monthsStudy 2Mapping the app buying journey
  3. FinalOutcomeNorth Star for Store Discovery

👆 Select a study to see how it unfolded.

📊 Study 1 | Diagnostic | 1.5 months

Pinpointing where customers dropped off

I took over this survey after launch. It drew 70+ responses on the Store site, which I cleaned to 45 from customers and partners. I grouped every response and mapped it onto a five-stage buying funnel to see where people dropped off and why.

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

Most drop-off happened early, during discovery and evaluation. Satisfaction rose once customers installed an app. The signal pointed to Store's journey, not partner app quality.

📏 Reading these numbers as directionalWith 45 self-reported responses, I treated these results as signals of where to look, not proof. Study 2 tested them.

🧭 The rationale

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

⚠️ The challenge

The researcher who launched the survey went on leave right after launch, leaving me to lead my first full-scale survey analysis on my own.

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.