Case Studies / B2B SaaS Β· Analytics

How an Enterprise Analytics Platform Personalized 99% of Sessions and Identified 20,000+ Companies with Abmatic AI

πŸ“Š Results at a Glance
  • βœ“99% of Sessions Personalized near-total personalization coverage through audience segments
  • βœ“210,000+ Identified Visitor Sessions surfaced from otherwise-anonymous website traffic
  • βœ“35% Enterprise Accounts a high share of identified visitors from companies with 5,000+ employees
Personalization Β· Q2 2026live
99%
Personalized
210K+
Sessions
20K+
Companies
AprMayJun

Over roughly three months, Abmatic AI personalized 99% of sessions through audience segments while identifying more than 210,000 sessions and over 20,000 unique companies.

The Challenge

Before Abmatic AI, the company faced several roadblocks acting on its traffic:

  • Anonymous Buyer Traffic β†’ Substantial inbound interest, but no view of which companies were behind it.
  • Limited Personalization Coverage β†’ Most visitors saw the same experience, leaving personalization largely untapped.
  • No Enterprise Visibility β†’ No way to identify enterprise accounts among the broader visitor base.
  • Generic Experiences β†’ The same messaging for every visitor, regardless of company size or stage.
Website traffictoday
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Unknown
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Unknown
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Unknown
CRM
MAP
Ads
Analytics
Personalization engineonline
Enterprise account detected on /platform. Matched to the analytics buyer segment.
Maximize personalization coverage.
Done. 99% of sessions now personalized through segments; 20,000+ companies identified.
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The Solution

Company Identification, Segmentation, and Near-Total Personalization

The company used Abmatic AI to deanonymize traffic at the company level, group identified visitors into audience segments, and personalize nearly every session with agentic workflows. Key areas of impact included:

1

Near-Total Personalization Coverage

  • βœ“Personalized 99% of sessions through segment-based experiences.
  • βœ“Extended tailored messaging to nearly every identified visitor.
  • βœ“Tailored experiences by company size and buying stage.
2

Company-Level Identification (Deanonymization)

  • βœ“Resolved more than 210,000 sessions to identified companies.
  • βœ“Surfaced over 20,000 unique companies from anonymous traffic.
  • βœ“Flagged 22% returning visitors, recognizing repeat research from the same accounts.
3

Enterprise Segmentation and Account Scoring

  • βœ“Identified that 35% of identified visitors were enterprise accounts (5,000+ employees).
  • βœ“Grouped identified visitors into audience segments by company size and fit.
  • βœ“Scored accounts so the team could prioritize the highest-fit prospects.
4

Agentic Workflows

  • βœ“Used agentic workflows to automate segmenting and personalization as new accounts were identified.
  • βœ“Sustained near-total personalization coverage as traffic grew.
The Impact

Near-Total Personalization, with a High Enterprise Share

Coverage Β· Q2 2026identified
210K+
Sessions
20K+
Companies
99%
Personalized
AprMayJun

99% of Sessions Personalized β†’

Near-total personalization coverage through audience segments.

210,000+ Identified Sessions β†’

Anonymous website traffic resolved to identified companies.

20,000+ Companies Identified β†’

A working list of accounts surfaced from anonymous visits.

35% Enterprise Accounts β†’

Share of identified visitors from companies with 5,000+ employees.

Near-total personalization coverage, plus a high enterprise share of identified accounts.

Over roughly three months, Abmatic AI personalized 99% of sessions through audience segments while identifying more than 210,000 sessions and over 20,000 unique companies for the company. 35% of identified visitors were enterprise accounts (5,000+ employees) and 22% were returning visitors, with agentic workflows enabled.

Identification and engagement metrics, Q2 2026 (Apr to Jun), approximately 3 months

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Case Study: B2B SaaS Analytics | Abmatic AI