Map the Market, Make the Hire

A practical framework shows how to turn labor market information into a ranked shortlist of cities before a search even opens. Rather than guessing where a role will be easiest to fill, the approach pulls supply, demand, compensation, demographic, and education data for a target occupation and builds a spreadsheet that scores every market on two dimensions: how much talent is available relative to job postings, and how far local pay sits from the national median.

The method walks through a real example: a licensed sales professional role at an insurance company that was newly opened to full remote work. Using a market-data tool's advanced geography search, an occupation and keyword set are chosen to pull the closest matching data, then exported into a spreadsheet covering the top hundred U.S. markets.

From there, several columns are built out:

• A variance column comparing a role's salary midpoint against each market's median compensation, then ranked so markets offering the biggest pay advantage rise to the top.
• A talent delta column subtracting job postings from available talent in each market, ranked the same way to surface where competition for candidates is lowest.
• A combined rank sum that averages the compensation and talent rankings, producing a single sourcing priority score for every market.

Sorting by that final score produces a top ten list of cities worth prioritizing first, along with a way to check how competitive specific required locations are, such as existing talent hubs, even when they fall outside the top tier. In the example, two hub cities land inside the top ten while others show a meaningful pay gap worth flagging to the business, whether that means allowing remote flexibility or adjusting the salary range.

Beyond the mechanics, the value of the exercise is arriving at conversations with hiring managers already holding a recommendation rather than just a problem. Cost of living is treated as a separate consideration from salary competitiveness, and the same scoring logic can be rebuilt using data pulled from other sourcing and labor market platforms, not only the one demonstrated.