Analytics Driven

Recruitment

By: Aaron BlackDirector of Admissions,

MBU

About me

This isn’t about

Google (analytics)

This isn’t (just)

about data

It’s about Discovery

Tell them what I’m going to tell them

Why analytics?What is analytics?Where does it fit?How do you do it?

The importance of Analytics (a business perspective)

Analytics trumps intuitionAnalytics is a differentiatorThe first responsibility of a leader is to define

reality.—Max DePree, Leadership Is an ArtYou’re here aren’t you?

Six Sigma:Get rid of

anything (any process etc.) that does not add value to the end user.

It’s about discovering a recruitment model that results in the right number of the right students…and does it efficiently.

How much are you spending to recruit one student? How many more could you recruit with a more efficient model?

Marketing Recruiting COA

Your Recruitment M

odel

Your Recruitment Model: how do you know its reaching its full potential?

Recruitment Model

Without analysis our recruitment model is just our best guess.

Political

EconomicEthicsRegulation

Technology

Environment

Soci-cultural

Competition

Demographic

Macro-environment & instabilityThings can get complicated

"the science of analysis". A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data…unless there are data involved in the process, it would not be considered analytics.

AnalyticsFrom Wikipedia, the free encyclopedia

Where does analytics fit into SEM?

MeetingGoals

Tactics

Strategies

Enrollment InfrastructureStructure, Staffing, Skills, Systems, Service

Data Collection and Analysis

Clear Mission and Goals

Typical starting

pointStarting point for

long term success

1. To improve retention 2. To build relationships with high schools and community colleges3. To target admissions efforts and predict enrollments4. To recommend changes to admissions policy5. To examine issues of how best to accommodate growth6. To improve the educational experience of students7. To identify needs of unique student groups8. To project and plan for student enrollment behavior9. To determine financial aid policies10. To assess student outcomes

Analytics uses for SEM

• Analytics– Passive/Vanity metrics: Best for when you know cause and

effect relationships well. Do you really know what actions you took in the past that drove those inquiries and applicants to you, and do you really know which actions to take next?

– Actionable metrics: Imagine you add a new feature to your website, and you do it using an A/B split-test in which 50% of customers see the new feature and the other 50% don’t. A few days later, you take a look at the number of applicants from each set of visitors, noticing that group B has 20% higher application rate. Think of all the decisions you can make: obviously, roll out the feature to 100% of your customers; continue to experiment with more features like this one; and realize that you’ve probably learned something that’s particular valuable to your prospects.

•Capacity Study•Preferred New Student Profile •Primary Market Penetration •Price Elasticity•Un-met Need Gap•Student Need/Support Alignment

Practical Ways to use Passive Data

DFW ratesTravel planningACT rankingFAFSA positionSegmented funnelsPredictive modelingStrategic Scholarship DecisionsE-mail open rates

Practical Ways to use Passive Data

Continued…

Limitations of Passive Analytics

• Passive: Isn’t necessarily actionable• Unless you know cause-effect relationships

well it only allows guesses.• It relies on drawing conclusions from

correlations• Many decisions in recruitment based on

intuition but developing accurate intuition takes experience and time.

“Correlation does not imply causation!”-Passive Data (limitations)-

your funnel is trying to tell you something

We make plans based on guesses and passive data.

Accurate Intuition takes time and means we either rely on our predecessors models (outdated?) or adopt someone else’s model (not OUR perfect recruitment model).

Data sometimes hard to obtain and accuracy can sometimes be questionable.

The goal of your research should be to reduce waste

and make current processes more effective.

It’s about discovering your

perfect recruitment model.

Life (enrollment) is an experiment…but we treat it like a guess.

Reality

Plan

“Everybody has a plan until they get hit”.

-Mike-

"the science of analysis". A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data…unless there are data involved in the process, it would not be considered analytics.

AnalyticsFrom Wikipedia, the free encyclopedia

An experiment is a methodical procedure carried out with the goal of verifying, falsifying, or establishing the accuracy of a hypothesis.

Experimentation is the step in the scientific method that helps people decide between two or more competing explanations – or hypotheses. These hypotheses suggest reasons to explain a phenomenon, or predict the results of an action.

ExperimentFrom Wikipedia, the free encyclopedia

They tell us we need to DO SOMETHING about something…but offer no clue about what that something is that we need to do.

Funnels are like status updates

Aaron Black

Using existing data helps us identify weak areas and generate hypotheses (guesses) about why things are that way. Further, it allows us to generate additional hypotheses (guesses) on what a solution might be. It lets us guess.

A radical idea about recruitment analytics

• "Thirty years from now the big university campuses will be relics….. (Residential) Universities won't survive. It's as large a change as when we first got the printed book.“ -Peter Drucker Forbes, June 16, 1997

Powering up your insight

Become active about experimentation

The key isn’t data, the key is agility

driven by discovery.

Agility: ability to make strategic changes (quickly),

based on truth.

Agility…because what good is data if you can’t use it to make changes?

What’s needed then is a framework for conducting

research with the aim being a perfected

recruitment model.

TEST YOUR RECRUITING MODEL

HOW?

• Split-tests: most actionable of all metrics, because they explicitly refute or confirm a specific hypothesis.

• Funnel metrics & cohort analysis: Example: SPD vs Individual Visit and funnel progress

• Keyword & web traffic metrics: What keyword entrances result in the most applications?

Split Test

Split Test

Split Test

How Obama raised $60 million by running a simple experiment

The Winner: 2,880,000 more sign ups + avg. gift of $21 =

$60 million more

“The value of an idea lies in using it.”