Mindfield Salesforce analytics

Recruitment is a people-focused business, but as data matching technologies evolve, some recruitment companies are starting to manage the process by numbers. Vancouver-based recruitment firm Mindfield is using data analytics both to narrow down candidates for jobs up front, and to hone the ongoing recruitment process.

Mindfield has been using Salesforce for its data analytics since Cameron Laker co-founded the company in December 2006. There are two parts to his Salesforce story. The company uses its features to match potential employees accurately to new jobs.

Mindfield also uses Salesforce as a business intelligence tool to give its corporate clients an understanding of their hiring patterns, helping them to optimise the future hiring process. The idea is to connect the two, so that one process informs the other, in a kind of positive feedback loop.

Collecting data

Mindfield collects multiple data points for job candidates – around 20-30 on average – from a variety of sources.

Around 85% of those data points come from information that the potential employee has provided directly to the company prior to hiring them. This might come in the form of a resume, for example, or a LinkedIn page.

Location is one such data point, so that the firm can evaluate employees for a position based on proximity to the job.

Other information comes in the firm of prior employment, because candidates may be screened for Mindfield clients based on companies that they have worked for in the past.

Still more quantitative data comes from screening calls with recruiters. Laker said they can score responses to interview questions against specific workplace characteritics, such as the ability to be a team player, for example.

Mindfield uses this data to help recruiters match potential employees to jobs that would be most suitable for them, Laker said.

Employment analytics

The analytics side of its service is designed to provide firms with a deeper dive into underlying human resources issues. The firm started to notice similarities in the problems facing client companies, who were using the company to hire staff paid by the hour, Laker explained.

Companies were experiencing anything up to 150% turnover per year (meaning that they would have to replace employees in the same role multiple times).

“It started with basic reporting, but evolved into understanding the workforce,” said Laker.

This is where the other 15% of the data points that Mindfield gathers are important. They come from interviews with line managers after staff leave the company, who can tell Mindfield about the employee’s performance.

The firm wants to be able to report to a corporate client about who they hired, and who did and didn’t work, and why. It can then use this information to refine the screening and scoring process that it uses in recruitment screening calls when hiring new employees for that company. The scoring process will change from client to client, based on what Mindfield finds about a particular client’s experiences with employees.

The discoveries from this process can be revealing, Laker suggested. “We have one customer that we know has a 50% increase in tenure if you hired students in part time jobs. “

Aggregating data

Mindfield is hoping to build upon these analytics capabilities.

“To aggregate that data to provide insights is where we’re going,” Laker said. “What are the engagement levels and pay expectations in Alberta vs Ontario? Even from a sector perspective?,” he continues. “That’s really why we’re collecting that information – it’s that aggregate view.”

Hiring by numbers may be one of the big technological advances over the last few years, but it’s important to include a human element, too. Mindfield says that it uses a mix of people, process and technology to find the right employees for its clients. The real win comes when each of those elements can inform the other.



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