PeopleDataLabs connector

Use your PeopleDataLabs data for reporting, automation and AI.

Data Panda brings PeopleDataLabs person and company data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.

PeopleDataLabs

About PeopleDataLabs

The B2B data graph under other people's enrichment.

PeopleDataLabs was founded in 2015 in San Francisco by Sean Thorne, Justin Hartzman and Hossein Azizi. The company raised a $45M Series B led by Craft Ventures in 2022 and built one of the larger open person-data graphs on the market, paired with a company graph and a job-posting feed. The product is shipped as APIs (person enrichment, person identify, person search, company enrichment, company search, autocomplete, IP enrichment, job-posting search, cleaner endpoints) and as a bulk dataset that lands in S3, Snowflake, Databricks, Azure or GCP with delta files for incremental refresh.

What sets PeopleDataLabs apart from the sales-stack neighbours is the audience. ZoomInfo, Apollo and Cognism sell a UI to SDRs. PeopleDataLabs sells a dataset to data engineers, ML platforms and product teams who build the enrichment that other people see. A lot of the contact databases, recruiting tools and ICP scorers your sales team logs into are running on a PDL feed underneath, sometimes with the brand stripped, sometimes co-licensed.

The reason to pull PeopleDataLabs into a warehouse is that the value of a data graph is the lift it produces on your own customer base, not the row count on the vendor's homepage. Coverage rate against your customer list, attribute decay between refreshes, ML-feature uplift on a churn or lead-score model and the build-versus-buy line on a homegrown enrichment service all live in the join between the PDL feed, the CRM, the product database and billing. Inside an API console those numbers stay invisible; in a warehouse they become the number you renew on.

What your PeopleDataLabs data is for

What you get once PeopleDataLabs is connected.

Reporting on a B2B data graph

Coverage, decay and lift on the PeopleDataLabs feed against your own customer and pipeline data.

Coverage rate of the PDL graph against your active customer and account list
Attribute decay (title, employer, email validity) between refresh cycles
Lift on a churn or lead-score model with PDL features added versus baseline

Automation on enrichment events

Let a PDL refresh fire the right downstream action without rekeyed data.

Job-change detection on a champion contact opens a follow-up task on the new employer
Newly enriched company on the target list routes to the right AE in the CRM
Failed match on a key account drops into a manual-review queue instead of disappearing

AI features from a 3 billion-row graph

Use PDL attributes as features in the models the team already runs, then measure the lift.

Lead-score model uses person seniority, function and tenure as features
Churn model adds employer-size band and industry from the company graph
Lookalike scoring on the PDL company graph against your closed-won cohort

Custom apps on PDL data

Internal tools on the PDL feed for RevOps, data engineering and product teams.

Coverage workbench: customer accounts with no PDL match flagged for review
Refresh-spend dashboard tied to downstream model lift, per attribute family
Build-versus-buy view of PDL credit cost against the in-house scraping pipeline it replaces

Use cases

Use cases we deliver with PeopleDataLabs data.

A list of concrete reports, automations and AI features we have built on PeopleDataLabs data. Pick the one that matches your situation.

Customer-base coverageShare of your active customer list that the PDL graph matches.

Attribute decayHow fast title, employer and email-validity attributes go stale per refresh.

Model liftUplift on a churn or lead-score model with PDL features versus baseline.

Build-versus-buyPDL credit cost against an in-house scraping plus normalisation pipeline.

Job-change detectionChampion or buyer contacts whose employer changed since last refresh.

Lookalike scoringNew companies in the PDL graph that resemble your closed-won cohort.

Match-failure reviewKey accounts the PDL match dropped, queued for manual reconciliation.

Bulk versus API spendPer-call API spend against the bulk-licence cost at your query volume.

Job-posting signalsHiring patterns from PDL job-posting data tied to account scoring.

ICP enrichment latencyTime from inbound lead to enriched record landing in the CRM.

Refresh ROIPipeline produced on the contacts touched after each refresh cycle.

Compliance audit trailPer-record source and lawful-basis trail for the data your team uses.

Real business questions

Answers you will finally get.

What share of our customer base does the PDL graph cover?

The PDL feed matched against your customer and account list on email, domain and corporate-family rules, with the unmatched share broken down by industry, size band and region. Tells the team where the graph earns its keep and where it leaves blind spots that have to be filled by another source or by human research.

Are PDL features lifting our churn or lead-score model?

Two model variants on the same training set, one with PDL person and company features, one without, scored on a held-out window with the same metric. Tells the data-science team whether the next refresh is worth the credit cost on the model that pays for it, instead of a vendor case study on someone else's data.

Should we keep the API or move to the bulk dataset?

Per-call API spend trended against your monthly query volume, lined up against the bulk-licence quote at the same volume and the engineering cost of pulling, indexing and refreshing the bulk feed in your warehouse. Surfaces the volume where bulk overtakes API, which is also the volume where the API bill stops being a line item finance ignores.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

PDL credit and licence spend tied to model lift and pipeline produced, not to call volume. Finance can see cost per match, cost per useful attribute and the bulk-versus-API crossover on your actual usage curve.

For sales leaders

Coverage, freshness and job-change detection on the contacts the team works. AEs and SDRs stop opening four tools to figure out whether a record is current, and SDR leadership sees the segments where the graph is thin enough to need a different source.

For operations

Match rate, attribute decay and refresh ROI in one picture. Data engineering owns a feed they can audit per row, per source and per refresh, instead of black-box enrichment that lands inside a SaaS UI.

Ideas

What you can automate with PeopleDataLabs.

PeopleDataLabs

Pair with HubSpot

Land PDL enrichment on HubSpot companies and contacts

PeopleDataLabs person and company attributes sync onto the matching HubSpot company and contact as fields and timeline events, with a job-change flag posting to the deal timeline. Marketing builds workflows that only fire on enriched-and-fresh records, and RevOps trends PDL spend against pipeline produced per segment instead of per credit consumed.

PeopleDataLabs

Pair with Salesforce

Push PDL person and company graph into Salesforce

PeopleDataLabs records land on Salesforce accounts and contacts with seniority, function, employer-size band and last-seen attributes. AEs see whether a record is fresh enough to call before they call it, and RevOps audits coverage on the territory plan instead of trusting a vendor headline number on the company website.

PeopleDataLabs

Apollo.io

Pair with Apollo.io

Run PDL alongside an Apollo deployment

Teams that already pay for an Apollo bundle often add PDL to plug the niche personas Apollo's database leaves thin. We land both feeds on the same person and account, so RevOps can see exactly which segments PDL fills that Apollo misses, and finance can decide whether the second source pays for itself in coverage rather than in vendor-pitch row counts.

PeopleDataLabs

Pair with Customer.io

Segment Customer.io audiences on PDL attributes

PeopleDataLabs person attributes (seniority, function, tenure, employer-size band) land on the matching Customer.io profile and feed segment definitions. Lifecycle marketing can split a campaign by enriched persona instead of by self-reported form fields, and the team can measure conversion lift per attribute family before extending the next refresh contract.

Your existing tools

Your data lands in a warehouse. Your BI tools read from it.

You keep the reporting tool you already have. We connect it to the warehouse where your PeopleDataLabs data lives.

Power BI Microsoft

Fabric Microsoft

Snowflake Data warehouse

BigQuery Google

Tableau Visualisation

Excel Sheets & pivots

Three steps

From PeopleDataLabs to answers in three steps.

Connect securely

OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.

Land in your warehouse

Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.

Reporting, automation, AI

We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.

Two ways to work with us

Pick the track that fits how you work.

Track 01

Self-serve

We set up the foundation. Your team builds on top.

PeopleDataLabs connector configured and running
Warehouse set up in your cloud account
Clean access for your Power BI, Fabric or Tableau team
Documentation on what's in the data model
Sync monitoring so you're warned before reports break

Best fit Teams that already have a BI analyst or data engineer and want to own the build.

Track 02

Done for you

We build the whole thing, end to end.

Everything in Self-serve
Dashboards built to the questions your team actually asks
Automations between your systems
AI workflows scoped to real tasks your team runs
Custom apps where a dashboard does not cut it
Ongoing delivery at a pace that fits your team

Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.

Before you book

Frequently asked questions.

Who owns the data?

You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.

How fresh is the data?

Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.

Do I need a warehouse already?

No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.

Should we use the PDL API or the bulk dataset?

PDL ships both: per-call APIs for person and company enrichment, search and autocomplete, and a bulk data licence that lands in S3, Snowflake, Databricks, Azure or GCP with delta files for incremental refresh. API works while volumes are unpredictable or low; bulk wins once query volume is steady and large enough that per-call cost overtakes the licence quote plus the engineering time to host the feed. We model both against your usage curve so the choice is a number, not a sales conversation.

How does PDL compare to ZoomInfo, Apollo or Cognism in practice?

Different audience, not just a different price point. ZoomInfo, Apollo and Cognism sell a UI to SDRs and AEs, with the database wrapped around it. PDL sells the database to data engineers and ML teams, with no UI. Several tools in the SDR-facing category run on a PDL feed under the hood. Teams that need a workspace for sales reps usually pick one of the UI vendors; teams that need a feed under their own product, scoring model or internal tool usually pick PDL.

GDPR-compliant

Data stays in the EU

You own the warehouse

A first deliverable live in four to six weeks.

We review your PeopleDataLabs setup and the systems around it. Together we pick the first thing worth building.

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