Client Snapshot
- Industry: Real-Time Data Analytics / Capital Markets Infrastructure
- Stakeholder: CEO
- Engagement type: Competitive intelligence and vertical-specific ABM data
- Relationship status: Active – 2 completed engagements, repeat buyer
The Challenge
Our client, a real-time query engine and data analytics platform built for capital markets, IoT, and enterprise data teams, needed competitive intelligence to fuel displacement campaigns against rival platforms in the real-time analytics and streaming data space.
The real-time data infrastructure market sits at the intersection of capital markets technology and enterprise analytics – a space where buying decisions are made by quantitative engineers, trading desk leads and CTOs who evaluate tools with extreme technical rigor. These buyers don’t respond to generic outbound. They respond to outreach that demonstrates an understanding of the tools they’re currently using and why an alternative might serve them better.
What made this engagement distinctive was the buyer: the CEO himself. When we reached out with a cold email pitching competitor data and asking if he was curious to know more, his response was immediate and open – he wanted to understand how our process worked and how it was priced. No gatekeeping, no sample requests, no multi-week evaluation. The CEO reviewed our competitor counts, assessed our depth of knowledge about his market and made a buying decision based on confidence in our expertise – not a sample file.
This is the rarest kind of data buyer: one who evaluates the vendor, not just the data.
The Solution
We scoped the engagement in two phases – the first focused on competitor displacement, the second on a vertical-specific build driven by insights from the first dataset.
How the deal progressed:
We pitched the CEO directly with competitor customer counts. Rather than requesting samples, he assessed our knowledge of the competitive landscape – how many customers each competitor had, how confident we were in the counts and how the data was structured. Satisfied that we understood his market at a level most data vendors don’t, he moved forward with a purchase.
After deploying the first dataset, the CEO returned with a refined request: a financial services–specific list of companies based in New York – driven by the fact that one of the competitors we’d delivered was primarily Wall Street–centered. This second engagement wasn’t a repeat of the first; it was an extension – the buyer had studied the competitor data and identified a geographic and vertical concentration worth pursuing with dedicated outreach.
Data fields delivered per record: Contact name · Job title · Verified business email · Phone numbers · Company name · Website · Physical address · State · Country · ZIP code · SIC code · Industry · Revenue size · Employee size
The first dataset delivered 1,973 verified companies and 4,121 decision-maker contacts across the competitor’s global customer base. The second dataset of 209 verified companies and 531 decision-maker contacts was a vertical and geo-specific build targeting financial services firms in New York – the epicenter of the Wall Street trading infrastructure that represents the client’s core market.
The Results
The buyer deployed the datasets through their outbound and sales programs, with the first phase fueling broad competitive displacement outreach and the second phase driving a concentrated campaign into New York–based financial services firms – the exact firms where real-time data infrastructure is mission-critical and switching to a superior engine carries the highest potential contract value.
Campaign performance across both engagements
From the verified companies and decision-maker contacts delivered across both datasets, the buyer’s campaigns generated:
- Positive response from 200+ companies across outbound, direct sales, and event-driven touchpoints – with notably higher engagement rates from the New York financial services dataset where the client’s Wall Street heritage gave them immediate credibility
- ~14% of engaged accounts converted into paying customers within the first two quarters, with the highest close rates coming from quantitative finance and trading infrastructure teams where the client’s real-time performance advantages were most relevant
- ~30% of those new customers expanded usage, adding teams, use cases, or upgrading from community to enterprise tiers – becoming repeat revenue accounts
- The remaining engaged accounts entered pipeline and nurture cycles, with several flagged for upcoming budget cycles in Q1 financial planning windows
8911 North Capital of Texas Highway, Suite 4200
Austin, TX 78759
+1 (737) 239-8575
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8911 North Capital of Texas Highway, Suite 4200 Austin, TX 78759
+1 (737) 239-8575
Features
Data Sources
- AI-Powered Data Aggregation
- Real-Time Web Scraping and Crawling
- Machine Learning-Driven Predictive Analytics
- Social Media and Community Intelligence
- IoT and Sensor Data Integration
- Blockchain-Enabled Data Verification
Compliance
1021 E Lincolnway Unit #533 Cheyenne, WY 82001
+1 (256) 737-3949
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Head Office
8911 North Capital of Texas Highway, Suite 4200
Austin, TX 78759
+1 (737) 239-8575
Features
Data Sources
- AI-Powered Data Aggregation
- Real-Time Web Scraping and Crawling
- Machine Learning-Driven Predictive Analytics
- Social Media and Community Intelligence
- IoT and Sensor Data Integration
- Blockchain-Enabled Data Verification
Compliance