DataExchange "TalkDataToMe"

Santosh Sharan of Zeer.ai joins John Kosturos on the Data Exchange Podcast to unpack a different approach to intent data. Instead of inferring interest from publisher page views, Zeer.ai reads what buyers say and do across professional communities, social platforms, webinars, podcasts, and video, then uses semantic analysis to work out where each person sits in a buying cycle.

Santosh spent two decades in the data industry, most recently as a key executive at RB2B, with work alongside Apollo, ZoomInfo, HG Insights, and LeadIQ. In this conversation he explains the context graph, why person level resolution tied to a LinkedIn URL beats probabilistic matching, how Zeer.ai data overlaps with established intent providers, and how the data reaches customers through flat files, API, and an MCP server.

Chapters

0:00 Introducing Santosh Sharan and Zeer.ai
 0:52 From ICP targeting to behavior: the lesson from RB2B
 2:24 Offline data, online data, and the rise of signals
 3:55 Targeting people who have the problem today
 4:56 Where traditional intent earns its keep
 6:05 Why Zeer.ai calls itself next generation intent
 6:50 Publisher traffic, agent traffic, and a model under pressure
 7:49 The breadcrumb trail of a real buying journey
 9:20 Context graph: reading stage, not just keywords
 10:14 Sources: communities, social, webinars, podcasts, video
 11:50 Volume growth and the evidence trail behind each record
 12:28 Picking signals without wading through a 50,000 topic taxonomy
 15:01 Overlap testing against established intent providers
 16:17 Layering signals rather than replacing a provider
 18:14 Inside the record: LinkedIn URL as the primary key
 19:05 API and MCP access for deeper queries
 20:09 Event intelligence and a testimonials dataset
 21:20 Synthetic surveys drawn from real conversations
 22:08 Finding advocates, detractors, and churn risk early
 23:50 The AlphaSense parallel for go to market teams
 24:55 Market focus today, plus pilots in real estate and pharmaceuticals
 25:44 Delivery: flat files, API, MCP, and no application layer
 27:06 Licensing: annual, monthly, and usage based
 27:30 Powering platforms, agencies, and other data vendors
 28:16 Why communities remain an untapped signal source
 29:35 Santosh turns the table: what should this category be called
 30:59 Research intent versus mid funnel intent
 32:43 Deterministic person level versus probabilistic matching
 33:55 Closing thoughts and where to find Zeer.ai

Learn more about Zeer.ai at zeer.ai
 Find Zeer.ai on the SpringDB Data Exchange at springdb.io/data-exchange/

#IntentData #GoToMarket #RevOps #SignalData #DataLicensing

MixRank was founded in 2011 as an advertising intelligence platform, went through Y Combinator, and then pivoted several times before landing on the business it runs today: sourcing data at web scale and licensing it in bulk to the companies that build products on top of it.

In this episode, John Kosturos talks with Aswin Shibu, Co-founder and Chief Revenue Officer at MixRank, about how that transition happened. The short version is that MixRank grew tired of depending on third party data vendors that kept getting acquired or shutting down, decided to build the sources itself, and then discovered that other companies wanted to license what it had built.

Aswin breaks down the full portfolio. Employee intelligence tracking who works where and when they move. Curated company profiles with month over month history. Web technology detection across the sites MixRank crawls. App Store and Play Store data covering apps and the SDKs inside them. A job postings data set that adds millions of new records every month. Every object ties back to a single corporate identity, which is what makes the collection function as a graph rather than a pile of files.

He also describes the five categories of customers MixRank serves, from sales and marketing platforms to recruitment technology, investment firms, risk and compliance teams, and a newer group of AI native companies that have moved past seat based platforms and are building internal data applications and custom workflows instead.

The operational detail is where the episode earns its keep. Aswin explains why MixRank has shipped on an hourly cadence for eight years instead of monthly or quarterly dumps, and why that choice raises the service bar in a way the team considers a feature. He covers delivery into Postgres, Snowflake, BigQuery, Redshift, Clickhouse, and object storage, and the unusual decision to run on four owned data centers rather than public cloud, which now lets MixRank offer hosted infrastructure alongside the data license for a portion of its customers.

He is equally clear about the boundaries. MixRank does not do intent data and does not work in offline identity. Those are other companies' areas of expertise, and Aswin says so plainly.

The closing stretch covers vendor durability. MixRank is profitable and has customers who have been with it for eight years or more at consistent pricing. Aswin argues that contract structure, ownership, and financial health belong in the first discovery call with any data vendor, and John adds the diligence questions buyers tend to skip.

MixRank is a SpringDB partner and is listed in the SpringDB vendor directory. If you are building a product or a revenue workflow that needs foundational company and employee data, SpringDB is the route to it: license MixRank data alongside other vetted providers under a single API layer, with flat rate delivery and the ability to layer specialized data on top of the foundation.

WHO SHOULD LISTEN

• Product and platform builders. If you are building sales, marketing, or recruiting software, this episode explains what a foundational company and employee graph gives you and why rebuilding one from scratch is rarely the right use of your engineering time.
• Data buyers and procurement leads. Aswin and John lay out a practical framework for evaluating vendors, covering refresh cadence, contract structure, renewal escalation, and whether a vendor will still exist in a decade.
• Data and platform engineers. The delivery discussion is specific. Hourly streaming feeds, format options, native delivery into common warehouses, and what the initial integration work actually involves.
• Revenue and sales operations teams. If your organization has outgrown seat based data platforms and wants to push data into internal applications and custom account based workflows, the fifth customer category Aswin describes will sound familiar.
• Investment and research analysts. Venture, private equity, hedge fund, and real estate teams will recognize the signals discussed here, including hiring patterns, funding events, and technology adoption as leading indicators of company performance.
• Founders and data entrepreneurs. Anyone weighing whether to source data or license it will get an honest account of what running that infrastructure costs and why MixRank concluded it was a full time business in its own right.

Most identity graphs cannot explain where a single linkage came from. That is exactly how bots end up inside them.

In this episode of the SpringDB Data Exchange, John Kosturos sits down with David Finkelstein, Chief Executive Officer of BDEX, to talk about what separates an identity graph you can trust from one you simply inherited.

BDEX started life as a big data exchange, built to help data companies monetize what they had. The team quickly ran into a problem that changed the company: every supplier anchored identity differently, and without a reliable way to connect those records, nothing downstream worked. Identity stopped being plumbing and became the product.

David explains how BDEX maps online signals, devices, and touch points back to offline consumer records, linking email addresses, mobile IDs, connected TV IDs, and household IP addresses at the person and household level. He also makes a case that should change how you evaluate any graph: the deterministic versus probabilistic question misses the point, because a bot using a stolen identifier can create a link that is perfectly deterministic and completely false. What matters is validation, disqualification, and knowing the origin of every record.

The conversation covers ad fraud and why advertisers stopped treating it as a cost of doing business, the volume of signals behind every identity decision, state privacy regulation, the BDEX hybrid pixel, keyword based intent and why it beats fixed category taxonomies, activation across channels, the weekly graph rebuild, and how the data is delivered.

BDEX is a SpringDB partner, and BDEX audiences are syndicated on the SpringDB Data Marketplace. If you want to evaluate BDEX identity, audience, or intent data, blend it with other consumer and business sources, or host it somewhere that does not punish you for running long lookbacks, SpringDB can help you scope it and put it to work.

WHO SHOULD LISTEN

Advertising and marketing platforms running cross device targeting, who need an identity graph that holds up at the point of match.

Agencies and brands buying audiences, who want to understand how much of a campaign is reaching real people rather than bots.

Data providers holding data tied to a single identifier type, who want to link it to other IDs so it can be activated more widely.

Data and platform engineers evaluating file formats, delivery paths, and refresh cadence before wiring a graph into production.

Go to market and revenue teams connecting consumer identity to business records, so buyers can be reached beyond a single work email address.

Data buyers and procurement teams who want to ask better questions about where a data set came from, how it is validated, and what happens to identifiers that fail those checks.

Guest: David Finkelstein, Chief Executive Officer, BDEX
 Host: John Kosturos, SpringDB

Every Company Is Becoming a Data Company: Inside People Data Labs with Ben Eisenberg

People Data Labs is one of the most widely used workforce data providers in the market, and most end users have never heard of it by name. That is by design.

In this episode of the SpringDB Data Exchange, John Kosturos sits down with Ben Eisenberg, CEO of People Data Labs, to explain how a company with no software product of its own became the data foundation behind platforms across recruiting, sales, marketing, and investment research.

Ben walks through the three joinable datasets that power People Data Labs, person, company, and job postings, all connected on a single persistent company identifier. He explains why not having a software product is a strategic advantage, how job postings quietly reveal hiring signals, budgets, and the technologies a company actually uses, and why every company is becoming a data company as AI lowers the barrier to building in house.

The conversation also covers the practical decisions every data buyer faces: API versus flat file delivery, consumption versus all you can eat pricing, delivery through Snowflake, Databricks, and S3, and how to judge data quality through golden records, record linkage, and benchmarking. Ben closes with an honest look at where global coverage is strongest, where it thins out, and why the use case decides everything.

In this episode:

• Why pure data beats owning a software product
• How person, company, and jobs data become joinable
• The hiring signals hidden in job postings
• API versus flat file delivery, and when to switch
• How data pricing actually works
• How to evaluate data quality and global coverage

About People Data Labs:
 People Data Labs is a workforce data provider that builds high quality person, company, and job posting datasets and delivers them to the platforms and teams that build on top of them. Its core resume dataset covers roughly 800 million profiles, supported by an identity graph of approximately 2.1 billion identities.

The SpringDB Data Exchange brings you conversations with the people building the data economy. Subscribe for a new episode, and learn more at springdb.io.

The K-12 education market is $1 trillion and one of the most recession-resistant B2B verticals in the United States.

In this episode of the SpringDB Data Exchange Podcast, John Kosturos sits down with Peter Long, CEO of MCH Data, to break down how B2B marketers can effectively reach teachers, principals, school districts, and educational institutions with the right data strategy.

What you'll learn:
→ Why general B2B databases fail in the education market
→ How MCH Data monitors 80,000 schools every month and updates 4 million educator records
→ Why 1 in 7 public school teachers moves or leaves every year and what that means for your campaigns
→ The school buying calendar: when to market, when to pull back, and when budgets reset
→ How to segment 400+ educator job titles for precise targeting
→ Email deliverability rules specific to school district networks
→ Commercial models: list licensing, private cloud databases, APIs, and programmatic audiences

Whether you are new to education as a vertical or looking to sharpen your data strategy, this episode is the practical guide you need.

🔗 Learn more about MCH Data: mchdata.com
🔗 Explore education data on SpringDB: springdb.io/data-vendors/mch-data/

#EducationData #B2BMarketing #K12Marketing #EdTech #DataStrategy #SchoolDistrict #ProgrammaticAdvertising #DataExchangePodcast

Healthcare is a $5.3 trillion market and one of the most underutilized opportunities in B2B marketing.

In this episode of the Data Exchange Podcast, John Kosturos sits down with Peter Long, CEO of MCH Data, to break down how B2B marketers can effectively reach doctors, hospitals, nursing homes, and healthcare institutions using the right data and strategy.

What you'll learn:
→ How to segment 240+ medical specialties for targeted campaigns
→ Why healthcare buyers respond differently than standard B2B audiences
→ Email deliverability rules specific to healthcare institutions
→ How programmatic digital advertising works in the healthcare space
→ The person-plus-place linkage problem most databases miss
→ Commercial models: list licensing, APIs, private cloud databases

Whether you're new to healthcare as a vertical or looking to sharpen your data strategy, this episode gives you a practical roadmap.

🔗 Learn more about MCH Data: mchdata.com
🔗 Explore healthcare data on SpringDB: springdb.io/data-vendors/mch-data/

What does it actually take to build and scale a modern data platform?

In this episode of SpringDB Data Exchange, we sit down with Nick Weldon, Co-Founder of 5x5, to break down how today’s leading platforms are powered by identity graphs, data enrichment, and intent data.

Nick shares how 5x5 built a unique data co-op model, where companies collaborate and exchange first-party and behavioral data to create a massive, unified data ecosystem—without relying on traditional data vendors or expensive data licensing.

We go deep into how data platforms are evolving, why enrichment is no longer optional, and how companies can unlock powerful insights by combining multiple data sources into a single identity graph.

🧠 What You’ll Learn
What an identity graph is and how it connects B2B, consumer, device & behavioral data
Why data enrichment is critical for CRM, MarTech & AdTech platforms
How intent data actually works (and why most solutions get it wrong)
The challenges of scaling large data pipelines (petabyte-level)
How modern platforms use data to build better products and experiences
🎯 Who Should Watch
Product leaders building data-driven platforms
Founders exploring data as a competitive advantage
Data engineers & architects working with large-scale datasets
Growth, RevOps, and marketing teams using enrichment & intent data
Anyone curious about how modern data ecosystems actually work
🔗 Full Podcast & More Episodes

👉 https://springdb.io/podcast

In this episode of the Data Exchange Podcast, John sits down with Rohini Kasturi, CEO of HG Insights, to explore how companies can better understand their markets and grow with precision. HG Insights is widely recognized as a leader in technographic intelligence, helping businesses identify the right customers, understand technology adoption, and move upmarket more effectively.

Rohini shares his background and what led him to join HG Insights after first experiencing the platform as a customer. Drawing on decades of experience in technology leadership, he explains why data, AI, and market intelligence are becoming essential tools for modern growth strategies.

The conversation covers how companies can move beyond surface-level market analysis, why understanding what technologies customers actually use matters, and how insights like spend data and buying signals help teams focus on the opportunities that are most likely to convert. Rohini also discusses how HG Insights supports both large enterprises and leaner teams, and how curated data, AI-powered tools, and partnerships are shaping the future of go-to-market execution.

Whether you’re a business leader, sales or marketing professional, or simply curious about how data is changing the way companies grow, this episode offers clear insights and practical takeaways.

Topics include:

Technographics and market intelligence explained simply

How companies identify the right accounts and buyers

Using data and AI to drive smarter growth decisions

The role of curated data and partnerships in scaling

👉 Subscribe for more conversations with leaders shaping the future of data and business growth.