Who at your target accounts used to work at your customers? A warm-path map for 40 accounts, built in an afternoon for about $2

Every outbound team possesses a roster of prospective accounts and a catalog of existing customers. However, many overlook the valuable connections between these two groups. These connections reside in the individuals who previously worked at customers and are now employed at targets. They possess firsthand knowledge of the product and the individual who initiated the relationship, making them the most accessible warm introductions.

Experienced sales teams recognize this strategy and manually execute it by cross-referencing target accounts with past employers, a process that consumes a day and becomes outdated within the next quarter. Aiming to streamline this process, I constructed a comprehensive map for 40 target accounts in a single run, complete with evidence for each individual (customer, role, departure date), viable email contacts, and a preliminary pitch.

This operation was executed on October 10, 2026, and the results are as follows: the entire process required three Actor executions, a brief waiting period, and approximately $2 in expenses. Please note that I am the creator of the b2bsearch Actors utilized in this endeavor, which are available on Apify. I employ them due to their cost-effective pricing structure: payment is based on the number of individuals retrieved, with a miss leaving the row free, and a count is provided at no cost, enabling you to gauge the map's size before incurring any charges.

The dataset comprises a database containing over 800 million professional profiles, each with an associated position history. Importantly, no web scraping occurs during the execution, and no unauthorized access is made. The scenario entails targeting fintech companies. Among the 40 targets, ten have already established customer relationships: Stripe, Klarna, N26, Revolut, Wise, Adyen, Checkout, Mollie, SumUp, and GoCardless.

Forty accounts are targeted across the UK, Germany, the US, the Netherlands, and France, including Monzo, Starling, Chime, Robinhood, Coinbase, Plaid, Brex, Ramp, Mercury, Affirm, SoFi, Marqeta, Rapyd, Payoneer, Airwallex, Tide, Qonto, Pleo, Spendesk, bunq, Trade Republic, Scalable Capital, Bitpanda, Kraken, PayPal, Zopa, Curve, Freetrade, Moneybox, Block, Toast, BILL, Melio, Tipalti, Navan, Moss, Payhawk, Solaris, and Mambu.

The objective is to identify individuals who currently work at one of the forty accounts and have previously worked at one of the ten targets, with their departure occurring in 2023 or later. To accomplish this, I leveraged the LinkedIn People Search Actor, which offers simultaneous filtering capabilities based on company domains (current employment) and past employer domains (previous employment), with an additional filter for leaving the past employer after the year 2023.

Individuals who only transferred within the customer organization are automatically excluded, as are those who received promotions, as such transitions do not constitute valid matches. Step 0 involves conducting a preliminary count prior to making any purchases (free of charge). By utilizing the Actor's "count" mode, I retrieved a list of potential matches without accruing any additional costs.

To gain further insights, I set the mode to "people" and enabled the "profileDetail" parameter to include the full career card for each individual. This adjustment incurred a minimal cost of $0.96, as each row corresponds to a single person and is priced at $0.0032. A total of 300 rows were generated within 103 seconds, with a cost of $0.96.

Each row contains position data, including the company, title, start date, and end date. I verified the attribution of individuals to specific customers by matching the company name of their most recent position against the list of target customers. Out of the 300 individuals, 238 were successfully attributed to a specific customer.

For the remaining 62 individuals, their prior employment spanned beyond the most recent five positions, necessitating the use of the "Profile Lookup" feature without the "compact" mode to retrieve complete profile information. This process resulted in 54 individuals being successfully attributed to a specific customer, with a total cost of $0.17.

In total, 292 out of 300 individuals were successfully linked to a specific customer, along with the corresponding role they held and the month they left the organization.