ServiceNow Store

| Role

Lead UX Researcher @ ServiceNow

| Skills

Survey Analysis
Data Processing
Journey Mapping
AI-Assisted Research Operations

| Timeline

3 months

| Tools

Great Question
Qualtrics
BuildTools1
Figma

Note: some internal names, participant details, and figures have been generalized below to respect ongoing confidentiality.

ServiceNow Store: Uncovering Where App Buying Breaks Down to Shape the Future of Discovery

I conducted survey analysis and led follow-up discovery research end to end for ServiceNow's App Marketplace, turning a survey about customer sentiment on partner apps into the research foundation for how Store should work.

CASE SNAPSHOT

Context

ServiceNow Store

Sole researcher, started project January 2026

What I did

Funnel and Discovery Research

Covering partner app drop-offs, trust signals, and the customer discovery journey

How

Survey Analysis and Journey Mapping

Survey was launched on the Store site, followed by interviews with Store customers

MONTHS

45+

SURVEY RESPONSES

INTERVIEW SESSIONS

KEY INSIGHTS

TAKEAWAY

Low AI adoption on Developer Portal wasn't an AI problem, it was a foundational discovery and orientation problem that had to be solved first. That reframe redirected investment away from quick UI fixes and toward the foundational portal improvements.

Jump to challenges

Jump to insights

Jump to process

Overview

ServiceNow Store is a cloud-based enterprise marketplace where companies can browse, try, and deploy both free and paid applications, integrations, and AI agents to extend platform capabilities

Over 3,000+ listings featuring a mix of native ServiceNow products and partner applications

(built by third-party independent software vendors)

My Role

Lead UX Researcher

Owning end-to-end research for ServiceNow Store

Designed and led 7 in-depth interviews (structured Q&A plus a Claude-assisted journey-map co-building activity)

Mapped the customer discovery journey across 5 stages, surfacing the specific friction points driving early drop-off

Executed comprehensive survey analysis, managing data collection, synthesis, deck building, and stakeholder readouts

Research Timeline

PARTNER APP ADOPTION BARRIERS

APP DISCOVERY JOURNEY

Dec 2025

Feb 2026

Mar 2026

May 2026

Survey fielded on Store (N=45)

Study 1 findings finalized

Interviews begin

Study 2 findings finalized

Research Impact

REFRAMING

shifting Store's improvement priorities from partner-app quality to discovery and trust

PRIORITIZE

As consolidation efforts kick-off behind the scenes, I made discovery (not installation or pricing) the team's top investment priority.

FRAMEWORK

Built the Trust Equation, a reusable model of what makes a partner app credible enough to consider and buy.

DIRECTION

Set the open questions and scope for the next phase: concept testing a redesigned discovery experience.

Store research process

Research Approach

Problem

Partner apps make up of over 50% of Store's catalog yet adoption rates remain stubbornly low, and nobody knew why.

Goal

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

Objectives

Assess how customers feel about partner apps
Pinpoint the exact stage where customers drop out of the buying journey
Understand the barriers holding people back at each stage
Map how people discover and evaluate apps outside of Store
Identify the trust signals customers need before deploying an app

Why this matters

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

→ Provide niche solutions that fill native gaps in the platform

→ Enables customers to solve more business problems

→ Act as an external, zero-cost marketing and sales force

→ Lead to larger ServiceNow contracts

Research Methods

Part 1: Partner App Barriers Survey

Survey Analysis

Launched directly on the Store site, the short survey targeted customers and partners across four roles (Instance Admins, Technical Architects, Developers, App Admins).

I started by gathering, cleaning, and grouping the 45 survey responses and then mapped them onto the five-stage procurement funnel (Discovery, Request, Evaluate, Install, Renew) to analyze where and why customers dropped off.

Results from n=45 survey

Partner App Drop-Off Funnel

The survey format allowed the team to scale across many respondents quickly and be placed directly in the product (on the Store site) where the behavior was happening. This became Part 1 of a three-part study series, with the Customer App Discovery Journey research (Part 2) designed specifically to go deep on the early-funnel stages, where we were seeing the most drop-offs.

Part 2: App Discovery Journey Map

Behavioral Interview

Journey Mapping

I conducted 60 min interviews with 5 customers and 2 solution consultants to get at the reasoning and emotional arc that surveys can't capture, spanning three different levels of organizational influence across five industries.

I paired the Q&A with a live, Claude-assisted journey-mapping activity in each session so participants could co-build their actual discovery process with me in real time.

Participant Overview

Proposed North Star for Store

The survey could quantify where customers dropped off, but interviews with actual customers explained why in depth, since it focused on perception and intent, not behavior. This study turned a vague, high-drop-off problem (early-funnel discovery) into specific, actionable direction for Store's roadmap.

How I used AI

EXPERIMENTING WITH AI

I developed an interactive interview methodology where AI transcribes and maps customer conversations in real time, empowering participants to actively drag, drop, and correct their own journey maps to visually validate their experience.

Upon further feedback from my team, I enhanced the tool to generate real-time probing questions for the researcher and aggregate multiple journey assets to identify recurring patterns.

✔ Provided an instant visual reference

✔ Eliminated note-taking workload

✔ Improved data accuracy and validation

✔ Increased participant engagement

✔ Accelerated cross-session synthesis

IMPACT

What started as a tool for mapping the app buying process grew into a cross-functional capability as more teams adopted it. Driven by my team's advocacy, I was asked to reconfigure the tool for general use, present it to leadership—including UX Directors and the SVP of Product Experience—and demo it org-wide.

The journey mapper became a highly scalable qualitative synthesis engine that reduced time-to-insight from days to hours and scaled across four distinct departments, resulting it being added directly into our Research Operations AI toolkit.

Key Insights

Store insights

Insight #1

Intent-Based Search Removes Guesswork

Customers arrive at Store knowing the specific problem they're trying to solve, not the name or category.

Store's current search doesn't recognize that, so customers turn to where they can describe their problem in plain language (Google, AI, etc.)

Recommendation: Categorize apps by the customer's problem statement and adopt intent-based search that matches on use case, instance, and role (AI assistant)

Insight #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.

Recommendation: Make trust built-in with workflow screenshots, short walkthrough videos, AI-generated summaries, and highlighting vendor longevity/support

Insight #3

Equip SCs to Champion Partner Apps

Solution Consultants (SCs), who customers rely on as a trusted internal source, are not enabled or incentivized to surface partner apps.

Internal SCs often rely on informal, ad hoc methods (asking colleagues, piecemeal searching).

Recommendation: Give SCs a structured, searchable internal resource on partner apps, such as an "Internal Enablement" brief for niche use cases

Insight #4

Cost Transparency Promotes Confidence

Comparing apps and cross-checking entitlements are manual, time-consuming processes.

Customers lack visibility into total cost of ownership (acquisition, installation, maintenance) with partner apps, leading to abandonment.

Recommendation: Build a comparison tool into Store and surface hidden costs upfront, so customers can rule out unaffordable options early

Challenges I faced

Store challenges

⚙️ Tackling a new UX research method

I inherited Study 1 under unique constraints when the previous researcher left the team after launching the survey. Stepping up to lead my first end-to-end analysis from scratch was a massive learning curve; with no handoff on the original design logic, I had to quickly deconstruct the survey's architecture while learning the ropes of survey analysis.

Steps I took to overcome this challenge:

→ Met with stakeholders to understand the decisions they needed to make, so 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 doing 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

Result: Taking on this analysis from scratch gave me hands-on experience with data cleaning and taught me how to balance tight deadlines with gathering enough responses to reach a minimum sample size. The experience made me a more confident and adaptable researcher, and it shaped how I've approached every study since.

⚙️ Connecting silos during org changes

Despite working in a cross-functional environment, engineering silos and fragmented communication resulted in deployment of changes without design or research knowledge. This disconnect stalled our progress and highlighted the need to build better stakeholder relationships and unite on a shared vision.

Steps I took to overcome this challenge:

→ Conduct wide stakeholder interviews to understand each team's goals and where friction existed between departments

→ Got acquainted with engineering team by getting to know the team, how they worked, and what pressures shaped their decisions

→ Looped teams early in the process by sending interview session invites, recordings, and socializing research across organizations

→ Create a shared repository that brought all relevant data pieces in one place, giving the team one source of truth to reference

Result: Engineering became a closer partner and started openly sharing the challenges and constraints they faced. Stakeholders began individually reaching out to discuss findings and strategy. With that context, we reframed our roadmap and shifted from near-term design fixes toward long-term vision work.

Final Report

Reflections

This project taught me to grow past just running studies well, into owning a research program end to end, adapting when I inherited a study with no one else driving it, building my own tools under pressure, and turning findings into a strategy leadership could act on.

One of my proudest achievements was building an AI assistant that could co-build journey maps live with participants and synthesize findings across multiple sessions. What started as a tool for a single study grew into something bigger.

After presenting it to UX Directors and the SVP of Product Experience and demoing it org-wide to research and design teams, I generalized it for use across any study, and it's now part of the UX Research Operations toolkit.