As the competition for qualified talent increases, and as candidates act more like consumers in using numerous touch points for decision making, talent acquisition teams should be asking themselves:
How do we gather data on interested candidates, and how can we use it?
How do we offer candidates a compelling relationship with our employer brand at all stages of the job search process and use targeted content to provide value to these candidates?
How do we build rich candidate profiles that will help us target the right candidates with the right content, and then make data-driven decisions about which candidates to hire?
This white paper will help you answer these questions and more by examining the example posed by best of the best in customer experience and data intelligence: Amazon.
Consider what Amazon knows about consumers from a raw data perspective. First, Amazon obviously knows what a consumer buys and when they buy it: so when a customer buys running shoes, a hiking backpack and chia seeds in a month, Amazon knows with some certainty that the customer is trying to get into better shape or might be taking an outdoor trip. With those combination of factors, they thus know some of the customer’s likes and dislikes, their hobbies and interests, and ultimately what they like buying and at what price. They also know what they’ve looked at and not bought, what they’ve compared and what they might have put in their cart but decided against. It’s incredibly valuable data.
On top of buying and searching insights, Amazon also knows financial, geographical and logistical information about every consumer that visits its site. So why isn’t this creepy or intrusive? In this white paper, we examine a few of the many things Amazon gets right, and then teach you to apply these insights to your collection and usage of candidate data.