OPTICUITY CRMRequest an Assessment
Portfolio

See What Better CRM Data Looks Like.

Explore three industry-specific demonstrations showing how Opticuity CRM identifies CRM data problems, organizes approved corrections, enriches incomplete records, and verifies improvements through structured before-and-after audits.

Each demonstration uses fictional business data to illustrate our proposed approach, reporting structure, and cleanup workflow.

Industry demonstrations

Three simulated CRM audits.

All figures are fictional and provided for workflow demonstration. The illustrative health index is a hypothetical weighted demonstration score. It is not a percentage of clean records and not an independent performance benchmark.

B2B SaaSHubSpot-style CRM

Simulated Demonstration — Fictional Data

Northstar Cloud

Revenue Reliability Restoration

When Your CRM Data Is Unreliable, So Is Your Pipeline Visibility.

Explore a simulated SaaS CRM audit showing how duplicate contacts, incomplete records, inconsistent lifecycle stages, and stale opportunities can complicate revenue operations.

Dataset
1,500 contacts · 300 companies · 250 deals
Main challenge
The Pipeline Was Full of Activity. The Data Was Harder to Trust.
Illustrative health index (hypothetical, scale 0–100)
Initial audit58/100
Post-cleanup audit91/100
Fictional illustrative health index: chart data, scale 0–100
AuditHypothetical index
Initial audit58/100
Post-cleanup audit91/100

Important findings (fictional counts, before → after)

  • Duplicate contact candidates126 → 18
  • Missing required contact fields315 → 48
  • Stale open deals (60+ days)71 → 19

Approved merges, entered values, and reassigned owners reduced the simulated exceptions; 18 duplicate candidates stay held for further review.

Explore SaaS Case Study
Commercial Real EstateSalesforce-style CRE CRM

Simulated Demonstration — Fictional Data

Meridian Commercial Partners

Relationship Database Restoration

Your Brokerage's Most Valuable Relationships Shouldn't Be Buried in Disorganized Records.

Explore a simulated commercial real estate CRM audit showing how outdated contact information, incomplete property associations, and fragmented broker relationships can make a brokerage database difficult to use.

Dataset
1,000 contacts · 400 companies/entities · 200 properties · 150 deals
Main challenge
Years of Relationship Data. Too Many Disconnected Records.
Illustrative health index (hypothetical, scale 0–100)
Initial audit54/100
Post-cleanup audit88/100
Fictional illustrative health index: chart data, scale 0–100
AuditHypothetical index
Initial audit54/100
Post-cleanup audit88/100

Important findings (fictional counts, before → after)

  • Duplicate contact candidates88 → 15
  • Contacts missing critical fields240 → 45
  • Unverified owner/property links74 → 22

Validated links and assigned brokers reduced the simulated exceptions; ambiguous ownership cases stay preserved for review rather than inferred.

Explore CRE Case Study
Recruiting & Executive SearchBullhorn-style ATS/CRM

Simulated Demonstration — Fictional Data

Summit Talent Advisors

Candidate Database Recovery

Your Next Qualified Candidate May Already Be in Your Database.

Explore a simulated recruiting database audit showing how duplicate profiles, missing skill information, inconsistent statuses, and incomplete submission records can make existing talent harder to find.

Dataset
2,500 candidates · 150 client companies · 200 job orders · 400 submissions
Main challenge
The Candidates Were in the Database. Finding the Right Records Was the Challenge.
Illustrative health index (hypothetical, scale 0–100)
Initial audit49/100
Post-cleanup audit89/100
Fictional illustrative health index: chart data, scale 0–100
AuditHypothetical index
Initial audit49/100
Post-cleanup audit89/100

Important findings (fictional counts, before → after)

  • Duplicate candidate candidates210 → 35
  • Profiles missing searchable fields620 → 105
  • Candidates without assigned recruiter188 → 23

Approved merges, entered skill fields, and assigned owners reduced the simulated exceptions; 35 profiles stay held for identity review.

Explore Recruiting Case Study
Inspectable examples

Earlier simulated demonstrations.

No customer records or client information appear here. These show structure and method, not client outcomes.

Simulated DemonstrationSimulated dataset · Example Audit Preview

CRM Contact Data Audit — Demonstration Project

A simulated demonstration of a structured CRM contact data audit. The scenario illustrates how duplicate candidates, missing information, inconsistent properties, and other quality questions can be organized for review.

Industry
Cross-industry (not industry-specific)
Platform
HubSpot
Dataset
1,500 simulated contact records
Inspect example
Simulated DemonstrationIllustrative template · no live dataset

Pipeline Review — Demonstration Template

A placeholder case-study format showing the questions a pipeline review can document. It contains no client records, findings, or claimed outcomes.

Industry
Cross-industry (not industry-specific)
Platform
CRM-agnostic template
Dataset
No live dataset
Inspect example
Planned industry demonstrations

Templates, not yet performed.

These describe simulated scenarios still to be built. They contain no findings, counts, charts, or downloads.

Simulated DemonstrationTemplate · not performed

Commercial Real Estate Database Audit — Planned Demonstration

A planned demonstration focused on property associations, owner relationships, and contact data quality. No audit has been performed and no findings exist yet.

Industry
Commercial Real Estate
Platform
To be confirmed
Dataset
1,000 contacts · 400 organizations/entities · 200 properties · 150 transactions
View planned template
Simulated DemonstrationTemplate · not performed

Recruiting Candidate Database Audit — Planned Demonstration

A planned demonstration focused on duplicate candidates, profile completeness, and submission histories. No audit has been performed and no findings exist yet.

Industry
Recruiting & Executive Search
Platform
To be confirmed (ATS)
Dataset
2,500 candidates · 150 client companies · 200 job orders · 400 submissions
View planned template
Simulated DemonstrationTemplate · not performed

B2B SaaS CRM Audit — Planned Demonstration

A planned demonstration focused on lifecycle inconsistencies, duplicates, lead routing, and pipeline quality. No audit has been performed and no findings exist yet.

Industry
B2B SaaS
Platform
To be confirmed
Dataset
1,500 contacts · 300 companies · 250 deals
View planned template
Ongoing maintenance · optional monthly service

Cleaning Your CRM Is the First Step. Maintaining It Is the Next.

CRM records change over time. New contacts, updated relationships, incomplete fields, and evolving pipelines can introduce fresh data-quality issues after an initial cleanup.

Monthly maintenance can continue both tracks: a monthly CRM Data Audit and a separate monthly Pipeline Audit, each with its own Health Report, plus approved data entry, pipeline corrections, enrichment, and exception tracking within an agreed scope.

Monthly CRM Audit

Identify new data-quality issues and review changes since the previous assessment.

Monthly Health Report

Receive an organized summary of current findings, recurring exceptions, and maintenance activity.

Offline Data Backup

Maintain an agreed encrypted offline export of supported CRM data under client-approved storage and retention procedures.

Ongoing Data Entry & Enrichment

Maintain approved CRM fields and enrich eligible records using available, verifiable information.

Optional monthly service following the initial cleanup. This is a proposal only: no monthly maintenance and no offline backups were actually performed in these simulated projects.

Explore Monthly CRM Maintenance
Next step

See What Your CRM Is Missing.

Every CRM has its own structure, challenges, and priorities. Start with a data and pipeline health assessment to better understand what needs attention and what improvements may be appropriate.

Request a CRM Data & Pipeline Health Assessment Explore Our Services

Portfolio examples use fictional data. Actual findings, recommendations, and results depend on the client's CRM environment and agreed scope.