Candidate Journey Drop-Off Mapping for Mobile-First Hiring

If your mobile candidates are dropping off, you likely have an un-instrumented SLA breach hiding inside your funnel, not a sourcing problem. Scenario we see in war rooms: a high-volume role launches, applies spike, then interview completion craters. Recruiters blame "ghosting". Engineering blames the coding test. Security flags rising fraud risk. People Analytics cannot reconcile the numbers because each vendor reports a different denominator and none of the transitions are timestamped end-to-end. Operational risk shows up fast: time-to-offer stretches because candidates stall at the same unowned transition (usually document upload, video capture permissions, or a mobile-unfriendly assessment). Review queues pile up because exceptions are handled in Slack or email, not in an SLA-bound queue. Legal exposure follows: if legal asked you to prove who approved this candidate, can you retrieve it? When drop-off causes rework, teams create shadow workflows to "save" candidates. Shadow workflows are integrity liabilities because decisions happen outside ATS-anchored audit trails. Cost is two-sided: cycle-time waste from repeatedly re-inviting candidates and rescheduling, and mis-hire risk when teams bypass checks to hit headcount dates. Industry surveys routinely show identity fraud is not rare in hiring. Checkr reports 31% of hiring managers say they have interviewed a candidate who later turned out to be using a false identity. Pindrop reports 1 in 6 applicants to remote roles showed signs of fraud in one real-world pipeline. The fix is not "reduce steps." The fix is to map your candidate journey as an instrumented workflow so you can pinpoint exact drop-off points by device and step, then remove friction while keeping identity gating and evidence capture intact.

Legacy hiring stacks fail at drop-off diagnosis because they were built as disconnected forms, not as a controlled access workflow. Market failure pattern: ATS holds the record, but critical steps run elsewhere (scheduling, ID checks, interviews, assessments). Each tool emits its own partial timestamps, often without a shared candidate identifier, and almost never with a unified event log that captures retries, permission denials, device constraints, or reviewer actions. What this creates operationally: - Sequential checks that slow everything down. Vendors run in a waterfall because no one orchestrates parallelized checks instead of waterfall workflows. - No immutable event log across steps. When a candidate drops, you do not know whether it was friction, fraud gating, or a mobile failure. - No unified evidence packs. A decision without evidence is not audit-ready, and a drop-off without evidence is not diagnosable. - No review-bound SLAs. Exceptions get handled by whoever sees the email first, which breaks consistency and creates audit liabilities. - No standardized rubric storage across interview and assessment tools. Scoring drifts, and you cannot correlate friction with decision quality. End result: People Analytics inherits a reconciliation project, not a funnel. You can compute "drop-off" but you cannot explain it, defend it, or fix it.

Assign ownership before you instrument anything. Drop-off persists when every step is "owned by the tool." Use this operating split (adjust per org): - Recruiting Ops owns workflow orchestration, step sequencing, candidate comms templates, and SLA configuration. They are accountable for throughput and queue health. - Security owns access control and audit policy: identity gating rules, step-up verification thresholds, retention controls, and who can view identity artifacts. - Hiring Manager owns evidence-based scoring: rubric discipline, calibration, and adherence to structured evaluation. - People Analytics owns measurement: event taxonomy, time-to-event dashboards, segmentation, and drop-off root cause reporting. Automated vs manual review boundaries: - Automate: reminders, retries, scheduling, risk-tier routing, and evidence pack assembly. - Manual review: identity exceptions, fraud flags requiring adjudication, and rubric overrides. Sources of truth: - ATS remains the system of record for candidate status and decisions. - Verification service is the system of record for identity events and integrity signals, but must write back timestamps and outcomes to the ATS. - Interview and assessment systems must write back rubrics, scores, and completion events to preserve data continuity.

Recommendation: map the candidate journey as an instrumented workflow with identity gates, event-based triggers, and time-to-event analytics segmented by mobile context. This is the model: - Identity verification before access. Do not grant interview or assessment access until the candidate clears the appropriate identity gate for the role. Use step-up verification to avoid over-friction for low-risk roles. - Event-based triggers. Every transition emits a timestamped event (invite sent, link opened, permission denied, doc upload started, doc upload failed, liveness passed, interview started, assessment submitted). Triggers route candidates to the next step or to an SLA-bound review queue. - Automated evidence capture. Store artifacts and decisions as an evidence pack: what was checked, when, by whom, what the outcome was, and what exception path was used. - Analytics dashboards. People Analytics needs segmented risk dashboards: drop-off by step, by device, by network type, by geography, by time-of-day, plus integrity flags and manual review time. - Standardized rubrics. Hiring Manager scores must be captured in a tamper-resistant feedback flow and stored with timestamps to correlate friction with quality outcomes (pass rates, offer acceptance, post-start performance proxies where available). Answer-first insight: time delays cluster at moments where identity is unverified. Your mapping should specifically test whether mobile friction is forcing late identity checks, creating both drop-off and fraud exposure.

IntegrityLens AI acts as the ATS-anchored control plane that instruments each funnel step with identity gating, timestamps, and evidence packs so you can reduce friction without creating integrity gaps. - Biometric identity verification (liveness, document authentication, face match) to gate interview and assessment access with role-based step-up verification. - Workflow orchestration with configurable SLAs, automated triggers, and ATS write-back so the ATS remains the single source of truth. - Fraud prevention signals (deepfake detection, proxy interview detection, behavioral telemetry) to route candidates into review-bound queues instead of blanket friction for everyone. - Immutable evidence packs with timestamped logs and reviewer notes so decisions and exceptions are defensible. - Zero-retention biometrics architecture to reduce identity-data handling risk while preserving pass-fail outcomes and timestamps for audit.

• Removing identity gates to "improve completion" and then reintroducing checks after an offer. That shifts fraud detection later, where the cost of reversal and legal exposure is higher. - Handling mobile failures by manually emailing alternate links or accepting "screenshots" as proof. Manual review without evidence creates audit liabilities and breaks chain-of-custody. - Running vendors without ATS write-back and then rebuilding funnel status in spreadsheets. If it is not logged, it is not defensible, and it makes root cause analysis impossible.