Using Programmatic Consumer Knowledge for Recruitment Marketing Success

By on May 9, 2019

First, a true confession. I am a numbers nerd. I cut my teeth in the digital realm while working at several consumer companies where I was able to build online programs with success. What I enjoyed was that it was where the user experience intersected the digital world that led to best business outcomes — an increase in sales via web advertising, optimization of search engine capabilities, innovation in pay-per-click (PPC) and call tracking while building a back-end system to measure results.

The recruitment marketing side of the business is taking a page out of the consumer world as talent attraction reaches peak levels of fierce competition due to skills shortages and the lowest level of unemployment rates since the late 1960s.

In 2018, 80% of digital display marketing in the US was done via programmatic advertising (remember, this is in general and not specifically about recruitment marketing)1. Programmatic is designed to replace human negotiations with machine learning and AI-optimization with the goal to increase efficiency and transparency to both the advertiser and the publisher (, and

We are now reaping the results of consumer programmatic behaviors that have bridged into recruitment marketing and now see a wide array of digital channels — display, mobile, video, social and out-of-home channels (seen on digital screens on bus stations and truck stops, shopping malls, billboards, etc.) advertise programmatically. We continue to see growth and a greater return-on-investment when we are able to capture insights from machine learning and apply them to campaigns. Targeting is fine-tuned every day by the review and update of keyword targeting, data targeting, geo-targeting, contextual targeting and retargeting (

A technology company came to us in 2018 and asked us to build a relevant audience for them through programmatic advertising. During Phase 1, we set up the campaign and learned the terminology around the job set that the company was focused on recruiting. There was a lot of testing of keywords, negative keywords, device targeting and changing around bid strategies. Through this analysis, we noticed a high drop off rate and altered the landing page. We gained an invaluable audience profile and persona insights that were leveraged during Phase 2. The learnings of the audience provided enough data and value that the cost-per-conversion during the three-month campaign was understandably high.

As Phase 2 began, we started to review the reactions of the audience in this campaign phase (next three months). We made changes such as an impression cap from 90 viewable impressions to 30 viewable impressions based on the use funnel and analysis of the user that we targeted. Altering the text on the ads and doing A/B testing helped to capture a higher conversion rate and reduced the cost-per-conversion. We kept a close eye on performance and were able to make immediate changes to device and placement targeting. These advancements helped in the cost-per-conversion while also preparing for Phase 3.

Armed with the excellent results from Phase 2, we were determined to provide even better results for Phase3. Further optimization of the ads and the review of the demographics of those who converted, we brought new images into the campaign and added the structured text that worked in Phase 2. The images selected matched the demographics of the audience who were viewing the career site. The new ads had the best click-through rate, cost-per-conversion and lowest cost-per-click.

The continuously improving results are what makes machine learning a top choice for clients. The biggest benefits are that over time, they see the results of reaching a wider online audience with a much more targeted approach, that results in less reliance on a selected group of job boards that clients have used and are familiar with.

Match2One Blog:

Leave a Reply

Your email address will not be published. Required fields are marked *



HTML tags are not allowed.

Contact Us

First name is required.
Last name is required.
Company must be a string.
An email address is required.

San Francisco/San Jose Philadelphia Cleveland Chicago Los Angeles