Novice poker players often believe that reading people and knowing when to make big bets is the most important part of the game, but poker legend Annie Duke would tell you otherwise.  That’s because primetime poker players use something called “card odds” to tell them when to bet and when to walk away.  In its most basic form, calculating the “card odds” for a given hand is using statistics to calculate the probability of getting a favorable hand based on the cards that you do know.  The pros then compare the ‘card odds’ against the ratio of the amount required to stay in divided by the total amount that can be won. This ratio is called the ‘pot odds.’ For example, if $20 are required to call (keep playing) and there are $80 in the pot. A $20 bet gives you a chance to win $100 (the original pot plus your bet), so the “pot odds” at this moment are $20/$100 or 20%. If your probability of getting a good hand is greater than 20%, you bet.  If the probability is less than 20%, you fold and save your money for the next hand[1].  Players don’t win every hand they play this way, but they win in the long run by consistently not spending money when the odds are against them and placing higher bets when the odds are in their favor.

So what does this have to do with predictive analytics and recruiting? When you’re looking at a long list of prospective and admitted students, it can be hard to determined who’s likely to attend and who is going to have better prospects elsewhere.  There is limited information (known cards) to work with and it’s impossible to know all the factors influencing a student’s decision whether to attend a school.  Sending marketing materials and personalized outreach for every student is one way to ensure that all prospects are covered but is expensive and recruiters only have so much time.  This is akin to betting on every hand, leaving it up to chance, and spending a lot of dollars doing it.  On campus recruiting programs can be more effective but they are expensive and can only reach a limited number of students.  So how do you determine who the sure bets and the long shots and where you should direct your recruiting and marketing efforts?

If you said you’re currently doing this based on gut instinct and trial and error, you’re not alone.  The bad news is the data science is complicated.  There are many academic, geographic, demographic and social media factors that are a lot more complex than the 52 variables you find in a deck of playing cards.  The good news is that Othot’s data science team has that part covered.  We use your historical and current data in addition to our proprietary data sources to predict the odds that an individual will attend allowing you to prioritize your limited recruiting budget on high probability individuals (bet) and avoid wasting marketing efforts (fold) on students who aren’t likely to be engaged. Our “what if” scenarios take it a step farther and allow you to simulate and gauge the impact of marketing efforts and scholarship dollars, giving you a peek at the cards that haven’t been dealt yet and use this to sway more students from “on the fence” to committed.   This translates into more effective recruiting at lower costs for schools and higher quality interactions between recruiters and school representatives for students.  So when you’re ready to stop gambling on mass market outreach and put the power of Othot’s analytics platform behind your recruiting team, contact our team.