{"id":1451,"date":"2026-01-17T06:00:07","date_gmt":"2026-01-17T06:00:07","guid":{"rendered":"https:\/\/levisohn.com\/portfolio\/?page_id=1451"},"modified":"2026-01-17T06:41:09","modified_gmt":"2026-01-17T06:41:09","slug":"global-nav-case-study","status":"publish","type":"page","link":"https:\/\/levisohn.com\/portfolio\/global-nav-case-study","title":{"rendered":"Global Nav Case Study"},"content":{"rendered":"[et_pb_section admin_label=”section”]\n\t\t\t[et_pb_row admin_label=”row”]\n\t\t\t\t[et_pb_column type=”4_4″][et_pb_text admin_label=”Text”]\n
\n
\n

Redesigning Global Navigation<\/h1>\n
How User Mental Model Research Drove $2.3M in Revenue Impact<\/div>\n
Company:<\/strong> PagerDuty
Role:<\/strong> Principal User Researcher (Lead)
Duration:<\/strong> 4 months<\/div>\n<\/header>\n
\n
22%<\/span>
Trial-to-Paid Conversion Lift<\/span><\/div>\n
$2.3M<\/span>
Additional Annual Revenue<\/span><\/div>\n
47%<\/span>
Faster Time-to-Value<\/span><\/div>\n<\/div>\n<\/div>\n
\n

Context<\/h2>\n

PagerDuty is an incident management platform used by approximately 1.3 million users across 30+ features. Engineers, SREs, and on-call responders rely on it to detect, triage, and resolve production incidents under real operational pressure.<\/p>\n

I initiated this project independently\u2014no executive assigned it. Eight months into my role, I identified a critical problem through analytics exploration: the global navigation had grown organically over eleven years, creating a fundamental mismatch between how the product was structured internally and how customers understood it.<\/p>\n

\n
The Problem<\/div>\n

Using SQL queries against Snowflake and Pendo analytics, I identified converging signals that navigation was a high-leverage problem worth solving:<\/p>\n

    \n
  • 15%<\/strong> feature discovery rate among new users<\/li>\n
  • 7+ days<\/strong> average time to complete initial setup<\/li>\n
  • 22%<\/strong> of support tickets were navigation-related<\/li>\n<\/ul>\n<\/div>\n

    A “CONFIG” menu had become a repository for every new feature\u2014an engineering concept with no connection to user mental models. Navigation was blocking both user success and business growth.<\/p>\n<\/section>\n

    \n

    Research Approach<\/h2>\n

    I scoped the project into three phases with explicit decision checkpoints, designed to reduce uncertainty at points where decisions would become irreversible.<\/p>\n

    \n
    \n
    Phase 1: Discovery<\/div>\n
    Months 1\u20132<\/div>\n
      \n
    • Qualitative interviews (N=24)<\/li>\n
    • Card sorting with think-aloud (N=8 deep sessions)<\/li>\n
    • Competitive analysis<\/li>\n
    • Analytics review<\/li>\n<\/ul>\n<\/div>\n
      \n
      Phase 2: Definition<\/div>\n
      Month 3<\/div>\n
        \n
      • Behavioral segmentation<\/li>\n
      • Tree testing (N=37)<\/li>\n
      • Prototype testing<\/li>\n
      • Stakeholder co-creation<\/li>\n<\/ul>\n<\/div>\n
        \n
        Phase 3: Validation<\/div>\n
        Month 4<\/div>\n
          \n
        • A\/B testing (15,000+ users)<\/li>\n
        • FullStory behavioral analysis<\/li>\n
        • Support ticket analysis<\/li>\n
        • ROI modeling<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/section>\n
          \n

          The Strategic Challenge<\/h2>\n

          Product leaders favored a Jobs-to-be-Done framing (Incidents, Services, People, Analytics, Status), while engineering advocated for an Action-Based structure (Prepare, Respond, Review). A third camp preferred incremental improvement. Prior attempts to reconcile these had failed, and the disagreement was consuming leadership attention.<\/p>\n

          I faced a critical decision: should I select one internal proposal and iterate, create a hybrid structure, or reframe the problem entirely around user mental models?<\/p>\n\n\n\n\n\n\n
          Option<\/th>\nAdvantages<\/th>\nDisadvantages<\/th>\n<\/tr>\n
          A:<\/strong> Select one proposal<\/td>\nFaster decision, clear ownership<\/td>\nRisked reinforcing disagreement<\/td>\n<\/tr>\n
          B:<\/strong> Create hybrid<\/td>\nSatisfied multiple stakeholders<\/td>\nVague labels, no governing logic<\/td>\n<\/tr>\n
          C:<\/strong> Reframe around users<\/td>\nEvidence-based, durable framework<\/td>\nRequired additional research investment<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          I chose Option C. The deciding factor: the organizational cost of continued disagreement exceeded the cost of additional research. This decision shaped the entire project trajectory.<\/p>\n<\/section>\n

           <\/div>\n
          \n

          Key Findings: Four Mental Models<\/h2>\n

          The most consequential methodological decision was pivoting mid-project from quantitative to qualitative methods. After 25 card sorts, I recognized I had clustering patterns without understanding of underlying reasoning. I proposed shifting to 8 in-depth 75-minute sessions with think-aloud protocols\u2014smaller sample, richer insight.<\/p>\n

          \"Card\n
          Participant-centric analysis from card sorting revealed distinct grouping strategies across users<\/div>\n<\/div>\n
          \n

          Critical Insight<\/h4>\n

          The research revealed four behavioral segments with incompatible mental models<\/strong>. Two participants might group identical items while applying completely different logic\u2014the similarity matrix showed agreement; the reasoning diverged. This insight informed product decisions for years afterward.<\/p>\n<\/div>\n

          I also conducted stakeholder research, treating internal partners as study participants. Product management interviews (N=12) surfaced concerns about quantitative proof; engineering interviews (N=8) revealed technical debt anxieties; executive interviews (N=4) focused on ROI and timeline. This allowed me to frame findings in terms each group valued.<\/p>\n<\/section>\n

          \n

          Validation: Tree Testing & A\/B Testing<\/h2>\n

          I conducted segmented tree tests (N=37 across two variants) to evaluate proposed navigation structures against the identified mental models.<\/p>\n

          \"Tree\n
          Tree test results showing first-click success rates across navigation tasks<\/div>\n<\/div>\n

          While tree testing eliminated clearly poor structures, several options tested as “adequate.” We escalated to A\/B testing (15,000+ trial users) to distinguish adequacy from excellence with real behavioral data.<\/p>\n

          \"A\/B\n
          A\/B test results showing conversion and efficiency metrics across variants<\/div>\n<\/div>\n<\/section>\n
          \n

          Post-Launch Impact<\/h2>\n

          I tracked post-launch metrics to understand how the redesigned navigation performed in production.<\/p>\n

          \"Traffic\n
          Feature traffic trends showing increased engagement with key product areas post-launch<\/div>\n<\/div>\n<\/section>\n
           <\/div>\n
          \n

          Outcomes<\/h2>\n

          Rather than selecting the “right” internal model, the team aligned on designing for user jobs-to-be-done informed by the four behavioral segments. The project exceeded all success criteria:<\/p>\n

          \n
          \n
          22%<\/div>\n
          Increase in trial-to-paid conversion<\/div>\n<\/div>\n
          \n
          $2.3M<\/div>\n
          Additional annual recurring revenue<\/div>\n<\/div>\n
          \n
          $450K<\/div>\n
          Reduction in annual support costs<\/div>\n<\/div>\n
          \n
          47%<\/div>\n
          Reduction in time-to-first-value (7.2 \u2192 3.8 days)<\/div>\n<\/div>\n
          \n
          133%<\/div>\n
          Increase in feature discovery<\/div>\n<\/div>\n
          \n
          28%<\/div>\n
          Reduction in navigation-related support tickets<\/div>\n<\/div>\n<\/div>\n

          The organizational impact extended beyond the immediate metrics. As a direct result of these learnings, we codified navigation placement principles and established a lightweight governance group to ensure future changes were evidence-backed.<\/p>\n<\/section>\n

          \n

          Reflections<\/h2>\n

          What Worked Well<\/h3>\n
            \n
          • Early reframing:<\/strong> Shifting the discussion from “which proposal is best” to “what problem users are actually solving” prevented prolonged debate and enabled evidence-based tradeoffs.<\/li>\n
          • Methodological flexibility:<\/strong> Eight deep, contextual sessions revealed more actionable insight than 75 shallow ones would have. Depth mattered more than sample size.<\/li>\n
          • Stakeholders as research subjects:<\/strong> Interviewing PMs, engineers, and executives before presenting findings allowed me to convert potential resistance into advocacy.<\/li>\n<\/ul>\n

            What I Would Do Differently<\/h3>\n
              \n
            • Involve analytics earlier for clearer baselines<\/li>\n
            • Formalize the behavioral segmentation framework sooner for downstream reuse<\/li>\n
            • Front-load contextual inquiry\u2014the four behavioral segments were the project’s most important discovery<\/li>\n
            • Push harder when research evidence conflicted with stakeholder preferences<\/li>\n<\/ul>\n<\/section>\n
              Case study from work conducted November 2019 \u2013 April 2020<\/div>\n<\/div>\n\n\n\n

              <\/p>\n[\/et_pb_text][\/et_pb_column]\n\t\t\t[\/et_pb_row]\n\t\t[\/et_pb_section]","protected":false},"excerpt":{"rendered":"

              [et_pb_section admin_label=”section”] [et_pb_row admin_label=”row”] [et_pb_column type=”4_4″][et_pb_text admin_label=”Text”] Redesigning Global Navigation How User Mental Model Research Drove $2.3M in Revenue Impact […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-1451","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/pages\/1451","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/comments?post=1451"}],"version-history":[{"count":7,"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/pages\/1451\/revisions"}],"predecessor-version":[{"id":1467,"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/pages\/1451\/revisions\/1467"}],"wp:attachment":[{"href":"https:\/\/levisohn.com\/portfolio\/wp-json\/wp\/v2\/media?parent=1451"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}