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The goal of marketing is to capture attention and convey a message. But doing so has become increasingly difficult given the flood of data available in the market and the volume of stimuli we encounter daily.
Companies are constantly vying for the focus of new constituents. Millions of dollars are spent every year -- or, in some cases, every minute -- to achieve this goal. This is why viral marketing is such a boon for marketers.
Being "viral" is defined as circulating quickly from person to person via the Web. This is much different from having a high number of impressions or visibility; those are typically paid for and have standard conversion rates. When content is shared between friends, three factors greatly augment the success of the campaign.
- You have an audience's complete attention.
- You have a referral from someone your audience member trusts.
- You don't have to spend as much money to promote it.
Although it's impossible to predict whether something will become viral before it's posted, predictive marketing analytics provide ways to make calculated predictions that increase the likelihood of capturing attention. Facebook, among others, uses predictive analytics to better understand when to suggest babysitters, cleaning services, or even chocolates. Has it been four years since you posted about the birth of your first child? You might receive ads for quality day care facilities in your area. Is your anniversary coming up? Those ads might be filled with places to get flowers and chocolate.
Marketing has been doing this on a much larger scale for years. The sales cycle of holidays provides a general idea of what a community will want at certain times. What makes predictive marketing analytics powerful is taking that same approach and bringing it to an individual level. Gone are the days of being a number in the faceless masses. Now, marketing can treat us as individuals with our own preferences.
What predictive marketing analytics can offer
Beyond the relatively simple algorithms used to determine ads for anniversaries, kids, and life events are more robust predictive marketing analytics. Do you think Facebook can predict your next relationship? It can.
Carlos Diuk-Wasser wrote "The Formation of Love," which talks about the typical interactions that Facebook users go through in a new relationship prior to declaring "In a relationship" on Facebook. Carlos writes, "We studied the group of people who changed their status from 'Single' to 'In a relationship' and also stated an anniversary date as the start of their relationship. During the 100 days before the relationship starts, we observe a slow but steady increase in the number of timeline posts shared between the future couple. When the relationship starts ('Day zero'), posts begin to decrease. We observe a peak of 1.67 posts per day 12 days before the relationship begins, and a lowest point of 1.53 posts per day 85 days into the relationship. Presumably, couples decide to spend more time together, courtship is off, and online interactions give way to more interactions in the physical world."
By monitoring how people interact, social media can infer the upcoming milestones of our ever-evolving social lives and advertise accordingly to our presumed wants and desires. A vast wealth of data is being compiled every day, and new commonalities are being discovered and tested. Every day, we accumulate terabytes of new, raw data and find new ways to exploit it.
The most recent focus of analytics can improve companies' hiring processes. Predictive talent analytics use data mining to foretell the suitability of candidates in specific job roles. Companies use the key performance indicators for top-performing employees and match those qualities to prospective hires. This can reduce employment turnover rates within a company and make employees much more profitable after hiring.
This kind of information can greatly reduce the acquisition period during the hiring process. Companies are struggling to maintain their hiring pipeline with qualified individuals. They look for experienced talent as well as personal skills that show a positive balance of intelligence and emotional quotients. As companies grow and adapt to new trends, the demands of the positions change as well. The struggle to place the correct person in the proper position is evident by the growing number of unfilled jobs. That number continues to climb year over year. As of July, that number reached 5.8 million in the U.S. alone. That's 5.8 million jobs where a company is having difficulty filling the job due to unqualified candidates.
HR is pushing to adopt the intelligence behind predictive analytics. The adoption rate is currently slow given lack of training and lack of confidence in the algorithms used. According to Matt Ferguson, CEO of CareerBuilder, "only 1 in 7 HR departments regularly use big data in recruitment strategies and half never do." The companies that experience the most success are combining their recruitment process with analytics rather than recruiting by standard methods alone.
As the science in this area continues to develop, so will the confidence and standard practices.
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