#RecruitWithData: Our Next DDR Chat With Bryan Wempen on Brand Experience
After a great first Twitter chat a few weeks back with Bulls Eye Recruiting’s Will Thomson, the Data-Driven Recruiter is excited to announce our next special guest, Bryan Wempen. On Tuesday, October 6 at 1p.m. (EST), we’ll be discussing the topic of brand experience and its impact on talent acquisition. Follow along and participate with the hashtag #recruitwithdata.
Bryan Wempen is CEO and co-founder of MobiScreenr, a mobile software platform that helps companies screen hourly candidates for friendliness and work ethic. Named a top ranked online influencer in HR by HR Examiner, Bryan is also an author, speaker, and founder of the podcasts DriveThruHR and Thug Metrics. His 2015 books published are titled Note to Self: A Collection of 99 Life Lessons and Dancing with Big Data: Conversations with the Experts.
We had the chance to catch up with Bryan this week and asked him few questions in preparation for the next chat. Below are some highlights from the conversation.
DDR: How do you think the concept of employer branding has evolved in the past ten years?
Bryan Wempen: For me I’ve noticed consumer marketing concepts from creative to social entering into the employment branding frameworks. It’s an exciting and necessary evolution.
DDR: Who’s responsibility do you think employer branding should fall under and why?
BW: Whomever owns the branding should own the employer branding. I don’t feel they should have much division, only additional context to the audience.
DDR: If you had to choose one challenge with measuring the impact of employer branding, what would it by? And why?
BW: The disconnect between company performance, employer performance, and brand performance are vast. In most cases these 3 cornerstones above act like 5th cousins you’ve maybe seen once in your life or have only heard about them from your parents. There are 2 key challenges, high-level to call out with this: 1) performance management is a challenging measurement in general and 2) many companies are limited to a single data intelligence platform to use for this measurement and thus have no standards of what good or bad looks like.
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