Latest developments

Highlights

Duplicates Finder now records manual cleanup changes

Duplicates Finder already produced a Changes File for automatic merges. But when you opened a duplicate group and edited a row, deleted a row, or merged records manually, those changes could be missing from the file.

The Changes File now records those manual operations too, with before-and-after values for edited rows and the destination row for merged records. If you clean a CRM list by hand, you can download a complete log and use it to reproduce the changes in your CRM.

Improvements

Confidence scores for company and LinkedIn page matching

The Company Domain/Website and LinkedIn Company Page Matcher now returns a Confidence output when you start from a company domain or website.

A result is marked high confidence when the website listed on the matched LinkedIn company page agrees with the domain you supplied; otherwise it is marked low confidence. You can accept strong matches automatically and send uncertain ones to review instead of treating every returned page as equally reliable.

LinkedIn Search Scraper can add verified work emails

The LinkedIn Search Scraper can now look for a verified work email while it imports people from a LinkedIn or Sales Navigator search.

Previously, the source returned profile and company fields only. You can now enable Include Emails and map the email output into your collection—for example, turn a Sales Navigator search for French SaaS founders into a lead list with profile data, company data, and verified work emails in one run. Email lookup remains optional and uses additional credits only when enabled.

More reliable column management and CSV imports

Reordering columns from Manage properties now behaves predictably: drag a column to its new position and the table keeps that order.

We also fixed CSV headers that could not be used safely as Datablist property identifiers. A column such as 2026 Revenue now imports correctly while keeping its original name in the table, instead of creating a broken column.

Stoppable enrichment runs

Clicking Stop could end the current request while follow-up batches from a multi-step source continued to be queued. Supported sources now check the stop request before scheduling their next batch.

For example, if a source is processing a long result set in several batches, stopping the run prevents the remaining batches from being added while keeping the rows already imported.

See how to merge duplicate leads without losing data.

Highlights

Claim 500 free credits to try enrichment workflows

Eligible Free plan accounts can now claim 500 free credits from the Datablist sidebar. That creates enough room to test credit-based enrichments on real sample data, inspect the returned fields, and decide which workflow is worth scaling before buying more credits.

After verifying an eligible business email, the first qualifying account for that company domain can activate the credits and use them across the enrichment catalogue. The claim screen explains the eligibility checks before anything is added to the balance.

Improvements

AI Agent - Site Scraper keeps page clutter out of list extraction

Cookie banners, hidden page elements, and pagination controls could distract AI Agent - Site Scraper from the records you actually wanted. The scraper now removes more of that page clutter and is explicitly instructed to extract every record from the current page before identifying the next page.

For example, on a paginated property or business directory, the current page's listings are collected before the workflow continues to the next URL; the cookie notice is not treated as listing content. Optional numeric fields can also remain empty when a page does not provide them instead of breaking that record.

Find enrichments by the data they need or return

Enrichment search used to rely mainly on names and descriptions. It now searches visible input and output labels too, and highlights the matching terms so you can see why a result belongs in the list.

Search for phone, for example, and Datablist can surface an enrichment because Phone Number is one of its outputs even when phone is not in the title. You can also hide Premium entries or narrow the catalogue by category before opening a result.

Keep working in Datablist while a data source runs

Starting a data source used to keep progress closely tied to its configuration screen. Data-source imports now move into Datablist's task panel, where their progress follows you while you navigate to another collection or workflow.

Start Instagram Followers/Following Scraper, Google Maps Scraper, or another supported source, then continue preparing a different collection. The task card shows how many rows have been created, reports an empty result or error, and gives you the relevant next action—such as stopping the run, changing the configuration, or trying again.

See what happened in each enrichment run at a glance

The enrichment run history now tells a clearer story without making you open every run. Each entry distinguishes Queued, Processing, Success, Stopped, No matches, Error, Need Credits, and Stopping, and shows useful counts and credits alongside the result.

If a 1,000-row run processes 850 records and skips 150, those numbers are visible on the run card. An error can open its logs, a credit warning points to the next billing action, and a stopped run no longer looks like an unexplained failure.

For a practical directory workflow, see our AI web scraping guide.

Highlights

New: Waterfall Personal Email Finder

Finding a personal email from a professional profile used to require piecing together several tools or exporting the lead elsewhere. Waterfall Personal Email Finder gives this job its own Datablist enrichment: provide a LinkedIn profile URL and let the waterfall continue until it finds a personal address.

For example, you can start with a collection of event speakers, map their LinkedIn URLs, and add a Personal Email column for the addresses found. You pay only when the enrichment returns an email, rather than for every lookup attempt.

Improvements

Count distinct values in number columns

Number columns now offer Distinct values count in the calculation panel. Previously, this calculation was available for text data but not for numeric fields, so answering the same question often required converting the column or exporting it.

For example, select a numeric Customer ID column to see how many different customers are represented, even when some IDs appear on several rows. The count is calculated from the current selection without changing the stored values.

Keep the source profile attached to Instagram follower imports

When Instagram Followers/Following Scraper combined several source profiles, the result did not always retain every useful form of the account that each row came from. Results now include the source profile's numeric ID, username, and profile URL alongside the discovered follower or following account.

For example, import followers from three brand accounts into one collection, then group the rows by Follower of (Username) or open Follower of (ProfileUrl) to check the source. You no longer need to infer the origin from the order of the import or keep separate collections for each account.

Negative values stay negative when text becomes a number

Converting text into a Number column could drop a leading minus sign, turning a value such as -500 into 500. Datablist now recognizes negative values while still handling surrounding text and either dot or comma decimal separators.

A cell containing The adjustment is -999.99 now becomes -999.99, while -500,99 is read correctly when the collection uses a comma decimal separator. This keeps balances, changes, temperatures, and other signed values on the right side of zero.

New: Google Places/Maps Search enrichment

Google Maps searches can now enrich the rows already in a collection. Google Places/Maps Search accepts a query built from your existing columns and returns up to five matching places, with fields such as name, address, category, rating, phone, website, and coordinates.

For example, use Coffee roaster in {{City}} on a list of cities to add local businesses beside each row. This is different from starting a new collection with Google Maps Scraper: the enrichment keeps the search connected to the record that produced it.

Separate role and company descriptions in LinkedIn People Profile Scraper

A person's description of their current role and their employer's company description answer different questions, but they could previously be mixed together in LinkedIn People Profile Scraper results. The scraper now exposes them separately and also carries role descriptions into additional experience entries.

You can keep Description (Current or last experience) for what the person did and map Company description (Current or last experience) for what the company does. That makes it easier to write role-specific outreach without confusing an employer summary with the person's own responsibilities.

Six new templates for lead-list research and cleanup

Common list-cleaning and research jobs no longer need to start from a blank AI prompt. We added Correct person name spelling, Extract seniority from job title, Decision maker flag from job title, Business Number Extractor, Normalize First Name, Last Name, and Find LinkedIn Company Pages with Google Search to the template catalogue.

For example, a lead list with marie dupont and VP Sales can normalize the name, derive the seniority, and flag the role as a decision maker with three ready-made templates. Each one already defines the task and expected outputs, so you only need to map your columns and review the results.

Learn how waterfall enrichment combines lookup paths, or follow the guide to finding emails from LinkedIn profiles.

Highlights

New: Website Status & Parked Domain Checker

A successful connection does not always mean a company website is still active: expired domains can redirect elsewhere or display a parked-domain page that looks technically online. Website Status & Parked Domain Checker separates those outcomes for every URL in your collection.

Run it on a list of company websites to classify each one as Online, Redirected, Parked, or Unreachable. You can also keep the HTTP status, final URL, page title, and error message, then filter dead or redirected domains before the list reaches an enrichment or outreach workflow.

Improvements

AI Agent - Site Scraper processes paginated lists one page at a time

Paginated pages require two separate jobs: extract every record on the current page, then identify where the next page starts. AI Agent - Site Scraper now keeps those steps separate, returns the immediate next-page URL and pagination details, and removes hidden page elements before extraction.

On a business directory, for example, the scraper collects the full visible result list from page 4, records page 5 as the next step, and avoids jumping ahead or treating hidden interface text as a business record. The source can then continue page by page until no next URL is found.

Add every enrichment output column in one click

Enrichments with many outputs used to make you create or map each destination column separately. The output step now offers Add all outputs to collection, with a matching shortcut for any outputs still missing after you configure some fields manually.

For example, if an enrichment returns a company name, website, industry, country, and description, one click prepares all five destination columns. See the enrichment configuration guide for input and output mapping. You can still review individual mappings, reuse existing columns, or ignore fields before starting the run.

Choose how much education history LinkedIn People Profile Scraper returns

LinkedIn People Profile Scraper now includes structured education history instead of stopping at professional experience and profile details. You can request one, three, or five education entries depending on how much background the workflow needs.

For each returned education, map the school, degree, field of study, start date, and end date into separate columns. A recruiting list, for example, can keep the latest school only, while alumni research can request a longer education history without parsing profile text by hand.

Duplicate a collection without rebuilding its enrichments

Duplicating a collection could copy its columns and rows, but saved enrichment configurations and their run-status columns had to be recreated on the copy. The duplicate dialog now includes Clone Enrichment Configurations.

Use it when a collection is your starting point for every campaign: copy the schema, saved enrichments, mappings, filters, schedule settings, and status columns into a fresh collection, with or without the existing rows. The new collection is ready for its next dataset instead of another configuration session.

Duplicates Finder recognizes subdomains on multi-part domains

The Ignore Subdomain option in Duplicates Finder could misread domains with country suffixes because it relied on the position of the dots. It now identifies the registrable domain correctly while preserving the URL path used for comparison.

For example, www.example.co.uk/contact and fr.example.co.uk/contact can match as the same website when Ignore Subdomain is enabled. Without the option, the two hostnames remain distinct, so you can still keep regional sites separate when that matters.

Edit your billing address and VAT number in Datablist

Paid accounts can now review and update their billing identity directly from the Billing page. The form includes the contact name, address lines, city, state, postal code, country, and VAT number, with required-field checks before saving.

If your company moves office or receives a new VAT number, you can correct those details without leaving Datablist or asking support to make the change. A success message confirms when the new billing address has been saved.

New template: Company enrichment from company name

Starting with company names alone normally means building several searches and deciding which result is official. The new Company enrichment from company name template prepares that research workflow for you and favors an empty result over an uncertain match.

Map a Company Name column and optionally add country, industry, or a short description to disambiguate similar businesses. The template can return the official website, LinkedIn company page, industry, company description, country, and a short explanation of where the result was found.

Search long property lists by column name

Managing columns in a wide collection used to mean scrolling through the complete property list. Manage properties now has a search field that filters column names as you type and shows a clear no-results state when nothing matches.

In a collection with dozens of enrichment outputs, search linkedin company to find the relevant columns before hiding, editing, or selecting them for deletion. Clear the search to return to the full draggable list and reorder columns as before.

See the number of unique values before reviewing the list

The distinct-values calculation could show the values and their frequencies, but it did not state the total number of unique values clearly. The results now open with a simple count before the detailed breakdown.

Run the calculation on a Country column and Datablist can tell you that the selection contains 18 unique countries before listing each country and its frequency. You get the answer immediately while keeping the detailed distribution available underneath.

See how to count distinct values in a CSV column and find company websites from names.

This month, we made two improvements to existing features

Let’s get in!

New Features and Improvements

Improvements

Multiple JSON Sources

You can now import from multiple remote JSON files at once in the data source: "Import from a remote CSV or JSON file".

Why we built it: Makes it easier to combine data from different JSON endpoints into a single dataset, saving you time and simplifying your data import workflows.

Form-Data Support for API Calls

We added "form-data" as a content type for API calls.

Why we built it: Expands your API integration capabilities, allowing you to work with endpoints that require form-data format for file uploads and multipart requests.