{"id":3787,"date":"2026-06-04T16:24:07","date_gmt":"2026-06-04T16:24:07","guid":{"rendered":"https:\/\/pintel.ai\/blogs\/?p=3787"},"modified":"2026-06-08T16:30:28","modified_gmt":"2026-06-08T16:30:28","slug":"customer-success-story-payment-software-sales","status":"publish","type":"post","link":"https:\/\/pintel.ai\/blogs\/customer-success-story-payment-software-sales\/","title":{"rendered":"How an $800M+ ARR Payment Software Company Expanded Account Coverage by 36%"},"content":{"rendered":"
<\/div>\n

A leading payment software company wanted to improve account coverage across its target market.<\/p>\n\n\n\n

While the sales team relied heavily on LinkedIn and traditional prospecting databases, a significant portion of qualified accounts remained difficult to identify. Valuable prospect information was scattered across company websites, business directories, maps listings, and other public sources that were not captured in a single platform.<\/p>\n\n\n\n

Here’s how the company used Pintel.AI<\/a> to aggregate multi-source data, uncover hidden opportunities, and expand account coverage by 36%.<\/p>\n\n\n\n

<\/span>Company Snapshot<\/span><\/h2>\n\n\n\n
Company Snapshot<\/th>Details<\/th><\/tr><\/thead>
Industry<\/strong><\/td>Payment Software<\/td><\/tr>
Company Size<\/strong><\/td>$800M+ ARR<\/td><\/tr>
Use Case<\/strong><\/td>Multi-Source Prospect Discovery & Data Aggregation<\/td><\/tr>
Challenge<\/strong><\/td>Limited Coverage from Traditional Data Sources<\/td><\/tr>
Data Sources<\/strong><\/td>LinkedIn, Web, Maps & Proprietary Datasets<\/td><\/tr>
CRM<\/strong><\/td>Salesforce<\/td><\/tr>
Outcome<\/strong><\/td>36% Increase in Account Coverage<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

<\/span>The Challenge: Valuable Prospect Data Was Hidden Across Multiple Sources<\/span><\/h1>\n\n\n\n

The company’s sales team relied primarily on LinkedIn and traditional sales intelligence platforms to identify target accounts.<\/p>\n\n\n\n

However, many qualified businesses were missing from their prospecting workflows because important information existed across company websites, local business listings, maps data, industry directories, and other fragmented sources.<\/p>\n\n\n\n

Manually researching and consolidating this information was slow, difficult to scale, and often resulted in incomplete account coverage.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

<\/span>Key Challenges<\/span><\/h2>\n\n\n\n

\u2713 Heavy reliance on LinkedIn and traditional databases<\/p>\n\n\n\n

\u2713 Limited visibility into the full addressable market<\/p>\n\n\n\n

\u2713 Prospect data scattered across multiple sources<\/p>\n\n\n\n

\u2713 Manual research slowed prospect discovery<\/p>\n\n\n\n

\u2713 Inconsistent and fragmented data quality<\/p>\n\n\n\n

\u2713 Hidden opportunities remained undiscovered<\/p>\n\n\n\n

\u2713 Account coverage was constrained by available research capacity<\/p>\n\n\n\n

<\/span>How Pintel.AI Helped<\/span><\/h1>\n\n\n\n
\"\"<\/figure>\n\n\n\n

<\/span>Multi-Source Data Aggregation<\/span><\/h2>\n\n\n\n

Pintel.AI aggregated prospect data from LinkedIn, company websites, Google Maps, business directories, and proprietary datasets to create a more complete view of target accounts.<\/p>\n\n\n\n

<\/span>Automated Prospect Discovery<\/span><\/h2>\n\n\n\n

Qualified accounts were identified automatically across multiple sources instead of relying on a single database.<\/p>\n\n\n\n

<\/span>Data Cleaning & Mapping<\/span><\/h2>\n\n\n\n

Data from different sources was standardized, normalized, and mapped into a unified structure.<\/p>\n\n\n\n

<\/span>Contact Enrichment<\/span><\/h2>\n\n\n\n

Prospect records were enriched with additional company and contact information to improve outreach readiness.<\/p>\n\n\n\n

<\/span>Automated CRM Sync<\/span><\/h2>\n\n\n\n

Cleaned and enriched records were automatically synced into Salesforce, eliminating manual uploads and data maintenance.<\/p>\n\n\n\n

<\/span>Results Achieved<\/span><\/h1>\n\n\n\n

<\/span>36% Increase in Account Coverage<\/span><\/h2>\n\n\n\n

The sales team gained access to significantly more qualified accounts that were previously unavailable through traditional prospecting sources.<\/p>\n\n\n\n

<\/span>Expanded Total Addressable Market<\/span><\/h2>\n\n\n\n

Additional target accounts were discovered across multiple data sources, increasing market visibility.<\/p>\n\n\n\n

<\/span>Faster Prospect Discovery<\/span><\/h2>\n\n\n\n

Automated workflows replaced slow manual research processes.<\/p>\n\n\n\n

<\/span>Improved Data Quality<\/span><\/h2>\n\n\n\n

Multi-source validation improved data completeness, consistency, and reliability.<\/p>\n\n\n\n

<\/span>Better Prospect Readiness<\/span><\/h2>\n\n\n\n

Enriched company and contact records helped sales teams engage prospects faster.<\/p>\n\n\n\n

<\/span>Customer Quote<\/span><\/h1>\n\n\n\n
\n

“Pintel helped us uncover a significant portion of our market that traditional prospecting tools missed. By aggregating data from multiple sources, we expanded account coverage by 36% while improving data quality across our CRM.”<\/em><\/p>\n<\/blockquote>\n\n\n\n

<\/span>Customer Outcome<\/span><\/h1>\n\n\n\n

What was once a fragmented and manual prospect discovery process became an automated workflow capable of aggregating, validating, enriching, and maintaining prospect data at scale.<\/p>\n\n\n\n

The result was broader market visibility, 36% greater account coverage, cleaner CRM records, and faster prospecting execution without increasing research effort.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

<\/span>What Became Possible with Pintel.AI<\/span><\/h1>\n\n\n\n

\u2713 36% increase in account coverage<\/p>\n\n\n\n

\u2713 Hidden prospects discovered automatically<\/p>\n\n\n\n

\u2713 Multi-source prospect intelligence consolidated<\/p>\n\n\n\n

\u2713 Contact and company data enriched at scale<\/p>\n\n\n\n

\u2713 Cleaner, more accurate CRM records<\/p>\n\n\n\n

\u2713 Faster prospect identification and qualification<\/p>\n\n\n\n

\u2713 Reduced dependence on LinkedIn-only data<\/p>\n\n\n\n

\u2713 Expanded visibility across the addressable market<\/p>\n\n\n\n

\u2713 Sales teams received outreach-ready records faster<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

<\/span>Frequently Asked Questions<\/span><\/h1>\n\n\n\n

<\/span>Why wasn’t LinkedIn enough for prospect discovery?<\/span><\/h3>\n\n\n\n

Many qualified accounts had valuable information available on company websites, directories, maps listings, and other sources that were not fully represented on LinkedIn.<\/p>\n\n\n\n

<\/span>What is multi-source prospect discovery?<\/span><\/h3>\n\n\n\n

Multi-source prospect discovery combines information from multiple public and proprietary sources to identify accounts that traditional databases may miss.<\/p>\n\n\n\n

<\/span>How did Pintel.AI expand account coverage?<\/span><\/h3>\n\n\n\n

Pintel.AI aggregated, validated, and enriched data from multiple sources, helping the company uncover additional qualified accounts.<\/p>\n\n\n\n

<\/span>How did contact enrichment help?<\/span><\/h3>\n\n\n\n

Contact enrichment improved record completeness by adding and validating company and contact information needed for outreach.<\/p>\n\n\n\n

<\/span>Can multi-source data improve CRM quality?<\/span><\/h3>\n\n\n\n

Yes. Combining and validating information from multiple sources helps create more complete and accurate CRM records.<\/p>\n\n\n\n

<\/span>How much did account coverage increase?<\/span><\/h3>\n\n\n\n

The company expanded account coverage by 36%, giving sales teams access to more qualified opportunities.<\/p>\n\n\n\n

<\/span>Can Pintel.AI identify prospects beyond LinkedIn?<\/span><\/h3>\n\n\n\n

Yes. Pintel.AI leverages web intelligence, maps data, directories, proprietary datasets, and other sources to identify prospects beyond traditional platforms.<\/p>\n\n\n\n

<\/span>How did automation improve prospecting efficiency?<\/span><\/h3>\n\n\n\n

Automated discovery, enrichment, and CRM synchronization reduced manual research and helped sales teams focus on engaging prospects faster.<\/p>\n","protected":false},"excerpt":{"rendered":"

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