An AI Strategy for improving the Kellogg Club experience by identifying and addressing key pain points.

1. AI Driven Strategy to Enhance
the Kellogg Club Experience
Our Team
Ashwath Krishnan
MMM ’20
Curtis Sylliaasen
EWMBA ’21
Parul Purwar
MBA’21
Vaishnavi Yeruva
MSAI’20

2. “Last week I spent an hour flagging and choosing
between conflicting events. WHEN DO I EVER HAVE
AN HOUR TO THIS ?"
Heba Ansari MMM’20
“I Literally have to maintain a diary to track all the events
that I want to go to“
Tuhina Kapur 2Y’ 20
“Would be great to get a digest of new events added so I
don't have to manually search”
Survey Respondent
“Needs to be easier to weed out what doesn't relate
to you and your interests. There is so much, yes, but I
waste a lot of time weeding through stuff that has
little bearing on my goals”
Survey Respondent
“It'd be great to be auto-notified of events for the clubs of
which I'm a member a week ahead of time through the app.
Right now, I rely almost entirely on communications in club
slack channels for upcoming events and just use the app to
register”
Survey Respondent
“Would be awesome to select interests and have
notifications when events have been entered on
Campus Groups/CMS that meet those interests”
Survey Respondent
Quotes from surveyed Kellogg students about their
club event experience

3. We surveyed 120 full time students at Kellogg regarding their event experiences with the current event
management system at Kellogg
87%
Said that they missed
between 2 and 10 events
in the fall quarter due to
conflicts
56%
Felt very stressed about
scheduling and prioritizing
events and spent at least 30
mins each week selecting
between conflicting events
75%
Said that finding accurate
information from a
conflicted event was a
challenge
The Kellogg Club events experience is sub-optimal

4. 4
A real example of event conflicts taken from
Campus Groups

5. PROBLEM: The multitude of Kellogg club events
creates scheduling, budgeting and diversity issues
5
4623
77
Aug OctSep
109
Jan
345
Nov Dec Feb Mar
102
Apr May Jun Jul
210
62
150 164 187
11
School BodyRecreation / ActivitiesCareer ImpactAffinity
# of Club Events segmented by type of event1
188
97
100
29
8
228
190
126
100-200<20 20-50 50-100 200-500 >500
22
650
15
424
183
78
8
5310
5
74
33
51
62
37
5
8 7
# of Event Attendees segmented by type of event2
On average, 7-8 events occur every day at Kellogg in
predominantly 2 slots (12 – 1 PM) and (5 – 6 PM)
This creates scheduling conflicts forcing students to
choose between multiple desirable events
Events end up having low attendance due to conflicting
schedules, resulting in poor utilization of club resources
and KSA budgets.
Students who seek continuity prioritize attending
similar events from the same clubs with the same
people as before, creating a diversity problem. Students
are unable to explore different clubs/events resulting in
them becoming more “exclusive”
SCHEDULING
BUDGETING
DIVERSITY

6. 6
Objective: Use an AI driven solution to enable Kellogg clubs to do the following
Reduce Conflict: Assign a predicted attendee score to events based on predicted
attendees based on prior events attended, interests and other demographic data.
Disable scheduling conflicts of events with similar attendee scores
1
O Understand Diversity: Scoring mechanism and network maps to understand the
diversity of event attendees using demographic, financial and other data
2
SCOPE: How might we use AI and analytics to
solve these problems?
O Enable Exploration: Building a recommendation engine for students and
highlighting upcoming events that they may enjoy based on prior event data
3a
O Improve Outreach: Using the inclusivity score, scheduling and recommendation
algorithms to aid clubs in reaching out to desired students
3b

7. 7
Campus Group Data:
• Event Details – Tags, Club Name,
Date, Time, Event Location and
Target Group
• Attendee Data – Registered,
Attended, unique attendee id
• Feedback Data – Did the attendee
like the event ? Would they
recommend it?
Event ID 478499 463693 426633
Event Name AMA x IBC x GCC x SEA Al umni MixerAMA x IBC x GCC x SEA Al umni MixerBattle of the Bands
Attendee_Event_Key AMA x IBC x GCC x SEA Al umni MixerX99AMA x IBC x GCC x SEA Al umni MixerX98Battle of the Bands X97
AttendeeMaskedID X99 X98 X97
Registered
Attended
Event Type Social Social Social
Club Type Affinity Affinity Recreation / Activities
Group Name As ian Management As s ociationAs ian Management As s ociationKellogg Bands
Group Acronym AMA AMA KelloggB
Group Type Full-time Clubs Full-time Clubs Full-time Clubs
Start Date 5/4/2019 0:00 5/4/2019 0:00 5/10/2019 0:00
Month-Year 5-2019 5-2019 5-2019
Start Time 2:00:00 PM 2:00:00 PM 8:00:00 PM
End Date 5/4/2019 0:00 5/4/2019 0:00 5/11/2019 0:00
End Time 5:00:00 PM 5:00:00 PM 1:00:00 AM
Event Location Whis key Thief Tavern Whis key Thief Tavern Vic Theatre
Event Tags Networking,Happy Hour,SocialNetworking,Happy Hour,Social
Student Data: Mapped to attendee id
• Career – Earlier industry, desired
industry
• Demographic – Age, Gender,
Nationality, Ethnicity, Sexual
Orientation, languages, interests
• Kellogg - Section, Graduation Year,
Program
• Financial – Data from Kellogg
Financial aid for financial inclusivity
Program 1Y 2Y
Class 2020 2021
Gender Male Female
CitizenshipStatus Permanent ResidentPermanent Resident
CountryOfCitizenship1ZAF ZAF
Section Cash Cows Hedgehogs
Industry Technology Finance
Function Strategy Venture Capital
Scheduling Data
• Class Schedule
• Recruiting Events (dataset from
CMC, Big employers)
• Room availability – Data from study
buddy
STARTING POINT: What do we have available to
create a dataset ?

8. 8
Nearest Neighbors: Compute the similarity score between
event attendees to understand the diversity of attendees and
to evaluate diversity metrics of each club or club event.
3
Association Maps: Mine association rules about event
attendees to understand group dynamics across members
from different backgrounds, ethnicities etc.
1
APPROACH: What AI/ML algorithms
could be used to solve the problems?
Collaborative Filtering: Make event discovery easier by
building a recommendation engine based on past events that
a member has attended, diversity metrics, interests and goals.
2
Predictive Modeling: Build a ML model to predict who attends
an event based on clubs(prior event registration, attendance,
event feedback, demographic data etc.)
4

9. 9
Avoid scheduling conflicts Suggest diversity
improvement measures
Increase cross club events Improve event discovery
At the time of event
creation, the club
admin provides a few
basic inputs
The model predicts
which Kellogg students
are expected to
register and attend
If the same group of
people are also
registered/expected to
attend another
dissimilar concurrent
event, the model
recommends either to
reschedule or merge
Two clubs schedule
two highly similar
events within a few
days of each other
The model detects two
events with a high
degree of similarity of
content and attendees
The model notifies
both clubs of an
opportunity to
collaborate and save
club funds or KSA funds
Model determines that
a diversity score based
on demographic,
financial and industry
data of previous event
attendees
The model performs
scenario analysis and
determines which knob
to turn to improve
club/event diversity
The model
recommends potential
marketing targets for
club admins to target
for their next event to
improve the club/event
diversity score
Campus groups users
rate prior events and list
interests which feeds
the model
Model sorts their feed
from predictions based
on their past activity and
interests. Notifications
pushed when new events
they like are posted
Students register for a
main event and a
“secondary event”, thus
feeding the model to
indicate a conflict
Club Executive Interface Student Interface
BENEFITS: How do the outcomes of this solution
look like?

10. 10Copyright or confidentiality statement.
NEXT STEPS: How do we proceed from here?
Engage stakeholders and Secure buy in
KSA <-> Kellogg Financial Aid <-> KIS <-> Campus groups
Consolidate database and standardize inputs
Establish standards to tag and segregate data
Evaluate models via automated algorithms
Use automation tools such as Data robot to find best model