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.