Let Big Data Psychographics Win You Battles

Introduction

Psychologist Michal Kosinski developed a method to analyze people in minute detail based on their Facebook activity, a tool that propelled Donald Trump to victory, against all odds

Post Trump’s victory, Cambridge Analytica, a little-known London based firm sent out a press release: “We are thrilled that our revolutionary approach to data-driven communication has played such an integral part in President-elect Trump’s extraordinary win”. This company wasn’t just integral to Trump’s online campaign, but to the UK’s Brexit campaign as well.

How dangerous is big data?

If you have not spent the last five years living on another planet, you would certainly be familiar with the term Big Data. Big Data means, in essence, that everything we do, both on and offline, leaves digital traces. Traces that pertain to every purchase we make with our credit cards, every search we type into Google, every movement we make when our mobile phone is in our pocket, every “like” on Facebook.

For a long time, it was not entirely clear what use this data could have—except, perhaps, that we might find ads for high blood pressure remedies just after we’ve Googled “reduce blood pressure.”

Big Five”

Psychometrics, sometimes also called psychographics, is a science that focuses on measuring psychological traits, such as personality. In the 1980s, model was developed in an endeavour to assess human beings based on five personality traits, known as the “Big Five.” These are:

1. Openness (how open you are to new experiences?)
2. Conscientiousness (how much of a perfectionist are you?)
3. Extroversion (how sociable are you?)
4. Agreeableness (how considerate and cooperative you are?) and
5. Neuroticism (are you easily upset?).

Based on these dimensions—they are also known as OCEAN, an acronym for openness, conscientiousness, extroversion, agreeableness, neuroticism. They can help us make a relatively accurate assessment of the kind of person in front of us, their needs and fears, and how they are likely to behave.

While the “Big Five” did become a standard technique of psychometrics, the problem all this while was data collection, imploring people to fill complicated, highly personal questionnaires. Then came the Internet. And Facebook.

Our smartphone is a vast psychological questionnaire that we are constantly filling out, both consciously and unconsciously.

Michal Kosinski’s MyPersonality app on Facebook enabled users to fill out different psychometric questionnaires, including a handful of psychological questions from the Big Five personality questionnaire (“I panic easily,” “I contradict others”). Based on the evaluation, users received a “personality profile”—individual Big Five values—and could opt-in to share their Facebook profile data with the researchers.

Starting from a online quiz based questionnaire, the psychologists calculated the personal Big Five values of respondents. The results were then compared the results with all sorts of other online data from the subjects: what they “liked,” shared or posted on Facebook, or what gender, age, place of residence they specified, for example. This enabled the researchers to connect the dots and make correlations.

The insights

The killer skill is perhaps in linking two seemingly irrelevant or inconsequential pieces which when combined result in a pot of gold.

Remarkably reliable deductions could be drawn from simple online actions.

For example

1. Men who “liked” the cosmetics brand MAC were slightly more likely to be gay
2. One of the best indicators for heterosexuality “liked” Wu-Tang Clan.
3. Followers of Lady Gaga were most probably extroverts,
4. Those who “liked” philosophy tended to be introverts.

While each piece of such information is too weak to produce a reliable prediction, when thousands of individual data points are aggregated, the resulting predictions become really accurate.

In course of time, as the models were refined, the results become enticing. In 2012, Kosinski proved that on the basis of an average of 68 Facebook “likes” by a user, it was possible to predict their skin color (with 95% accuracy), their sexual orientation (88% accuracy), and their affiliation to the Democratic or Republican party (85 percent). But it didn’t stop there. Intelligence, religious affiliation, as well as alcohol, cigarette and drug use, could all be determined. From the data it was even possible to deduce whether someone’s parents were divorced.

Before long, Kosinski research was able to evaluate a person better than the average work colleague, merely on the basis of ten Facebook “likes.” 70 “likes” were enough to outdo what a person’s friends knew, 150 what their parents knew, and 300 “likes” what their partner knew. More “likes” could even surpass what a person thought they knew about themselves.

But it was not just about “likes” or even Facebook: They could now ascribe Big Five values based purely on how many profile pictures a person has on Facebook, or how many contacts they have (a good indicator of extraversion).

We also reveal something about ourselves even when we’re not online. For example, the motion sensor on our phone reveals how quickly we move and how far we travel (this correlates with emotional instability).

The key is when the data on psychological profiles can be used the other way round to search for audience segmentation, rather is far more targeted than mere demographics like age or gender but rather specific personality based psychographics  – specific profiles: all anxious fathers, all angry introverts, for example—or maybe even all undecided Democrats, as Trump used. You may watch Cambridge Analytica CEO Alexander Nix’s video on YouTube:  “The Power of Big Data and Psychographics” at https://youtu.be/n8Dd5aVXLCc

Same policy, two windows

Here is a case in point for Individually Targeted Communication Vs Mass Communication

In the USA – The right to keep and bear arms in the United States is a fundamental right, protected by the Second Amendment to the United States Constitution. The Act reads “A well regulated Militia, being necessary to the security of a free State, the right of the people to keep and bear Arms, shall not be infringed.”

So as compared to mass communication traditional approaches, new tools and platforms available today allow for dissemination of messages that can resonate much more personally with each individual.

Since not everyone shares a vision of this paradise, two differing messages prepared are presented. One if you’re concerned about burglary, the other if you appreciate ‘heritage’.

In Trump’s campaign, over 20 custom models were built. The models assessed voters based on policy issues, candidate preference; likelihood of voting early, and most significantly, identified the voters who were most likely to be persuaded.

How Big Data Brings Big Changes to Recruiting

IT’S NO SECRET that companies — particularly small businesses and start-ups — face challenges when it comes to finding and hiring the best possible talent.

The age of the internet has made a wealth of information available on potential candidates, ranging from Facebook to LinkedIn to G+ profiles. There’s no shortage of workforce analytics and applicant tracking systems designed for recruiting purposes, and many are great at gathering and aggregating “transactional information”. The trick isn’t merely in collecting the data–it’s in interpreting it, and understanding the importance (or lack thereof of) each data point. Today, recruiters need to be able to understand big data, which boils down to discovery, visualization and insight, assigned appropriate weights to each data point in your model

However, you can’t just “data mine” your way to the right candidate; remember that personal interaction and communication provide perhaps more important data than massive amounts of publicly available data. Did a candidate respond to an email? Show up for an interview? And of course there are the personal referrals and references, which should carry a lot of weight in a matching algorithm.

In other words, recruiting is moving toward an E2E transaction model, thereby helping people and companies reach their full potential.

And isn’t that exactly where recruiting should be focused?

Case in point- Leveraging Consumer Data Profile in Recruitment

 

Passive coverage

The data points covers almost every consumer, and not just those who were active on a particular job site. Thus you can effectively identify and assessing the so-called passive prospect, which includes the 80% of the working population that is not actively seeking a current opening. You can use it to find individuals who don’t want to be found on LinkedIn/social media. It can also be micro-targeted to find college students and women that have recently dropped out of the workforce.

Income

CTC info is not found on LinkedIn or even in resumes. However, since people disclose that while applying for a credit card, it is possible to figure out how much a prospect is currently getting paid and in many cases, their pay history. With it, you can also determine if your firm can afford to hire a particular prospect. You can also use frequent income increases as an indication that this individual is a top performer who is frequently rewarded by their current firm.

Predictors of an upcoming job search

You can use it to accurately predict future behaviour. For example, your algorithm can capture the tenure for which a target has been in a job (compared to previous jobs), their rate of income growth etc, you can estimate the likelihood of them considering a new job or internal move in the immediate future.

Indicators of learning and a top performer

Consumer data can tell you how individuals spend their money, including whether they spend their money on Learning & Development. This information could include subscriptions to journals, associations that they join, and conferences and seminars that they attend.

In short, consumer data has the capability of providing almost everything you need in direct sourcing in order to convince currently top-performing employed individuals (who are relatively happy where they are) that they should consider the one and only other opportunity that they are aware of, and that is the opportunity to work at your firm.

Scenario

Consider this scenario.  You are seeking to hire a rank holder/first-attempt Chartered Accountant from FMCG from the 2007-09 batch.  Your prospect is not active on job boards since she is already earning 31.5 lacs p.a. in the finance function of your competitor, the XYZ Co and quite satisfied as of now. But somehow you find her on LinkedIn. What if you also got a consumer data profile that went a step ahead and revealed that.

1. Her current CTC is INR 9 lacs p.a. as compared to your base case budget for this role i.e. she is quite affordable as a hire
2. She lived within commuting distance of your facility in Powai, Mumbai
3. Although she is not actively looking out for a change now, there was a 90% chance that she would begin looking for a new job in the next 6 months
4. She was likely a top performer at her firm because of her pattern of CTC increases.
5. She subscribed to finance journals, was pursuing International CFA and was member of Bombay Chartered Accountants society, having recently attended two conferences on GST and IFRS

Awesome, isn’t it J

Case in Point – Eliminating bias

After four months of rejection by employers, a man named Kim did one simple thing that changed his luck. He added “Mr.” to the front of his name on his resume. And then he got a job.

Take a look at these studies: Hays created two resumes, one for Susan and one for Simon. The resumes were actually the same, just named differently. They then asked hiring managers to evaluate the candidates based on their attributes and suitability. In large companies (> 500 employees), 62% of hiring managers said they’d likely interview Simon, while only 56% wanted to meet Susan. The more experienced ones (hiring > 20 times p.a.) had the same bias: 65% of them preferred to interview Simon.

 

Using a data-driven approach can help decreasing bias in organizations. It can be range from hiding the name/gender during initial screening from hiring managers to the E2E transaction models, described above.

Summing Up

At the end of the day, big data — when used properly — is a good thing for everyone involved. Recruiters can save time, companies will get positions filled by the right candidates more quickly, and candidates will be matched with the jobs of their dreams.

(2029 words)

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