The document discusses a project by Agus Nur Hidayat to build predictive models to address customer churn for an online contact lens store. The project involved analyzing the store's customer data to build models that can predict churn, segment churned customers, and determine interventions. This included using conditional inference trees, k-means clustering, and logistic regression. The models provided descriptive insights and a way to predict and understand churn in order to prescribe customer retention strategies. Agus found the project rewarding and it reinforced his interest in extracting insights from raw data to help companies with business decision making.

1. AGUS NUR HIDAYAT
FINALLY FINDING
A SOLUTION TO
‘CUSTOMER CHURN’

2. AGUS NUR HIDAYAT
FINALLY FINDING A SOLUTION TO ‘CUSTOMER CHURN’.
Exploiting Predictive Analytics to
Suggest the Solution for Customer
Churn: Contact Lense Online Store.
Research says that obtaining new
customers costs more money
compared to maintaining the ones
in existence. The measurement to
comprehend how well a company
is maintaining existing customers is
the rate of ‘customer churn’. In other
words, how many of customers stop
transacting with the company within a
particular time-span. Knowing which
customers will churn can facilitate a
pre-emptive intervention. Additionally,
determining customer segmentation
independently can lead to tailored
customer interaction. Thanks to the
availability of raw data owned by the
company, all of those tasks can be
addressed efficiently by the use of
predictive analytics.
Task:
The project involved the consultation and
collaboration with a (confidential) online contact
lenses store, an industrial partner, Streamline
Intelligence and the academic oversight of the
UCL Computer Science Facility.
We posed the following research questions:
1. How do you build a model that can predict
churned customers?
2. How can you determine the causal rules
of significant features that trigger churned
customers?
3. How could you decide the interventions
towards the churned customers?
And used the Predictive Analytic Models:
Conditional Inference Tree (churn prediction),
K-Means Clustering (churned customers’
segmentation), Logistic Regression
(evaluating models).
Review:
The project led to
descriptive insights from
the exploration of data,
creating a business and
statistical definition of
churn which was not
previously available from
the raw data.
It produced a classification
model for churn prediction,
a clustering model for
the segmentation of
churned customers and
an interpretation of all
the models which could
help prescribe customer
interventions.

3. What makes this project unique?
The client had many different
products within its online range,
all of which had different rates of
churn. The challenge was to create
a unified model which could be
applied across the whole business.
We selected variables to use in the
model and ran a range of predictive
analytic techniques to see which
was the best predictor. The aim
was to create models that are easy
for the client to understand in the
context of their own business.
What was the moment you
realised you wanted to do
what you are doing today?
I worked as a software engineer
before, in a position which involved
building database systems. The raw
data is often used by companies
for operational purposes without
ever realizing its potential use as a
strategic asset. I wanted to learn
how to extract the insights from
this raw data and how to interpret
it properly to provide insights that
are actually helpful to the company
in terms of their business decision-
making process.
What has been a highlight so far?
My decision to study overseas at
UCL has been really important.
Not only has it strengthened my
skill set with a Master’s degree
from a top university but meeting
so many new people from different
backgrounds has definitely widened
my perspective.
What have you learnt
along the way?
Stop wasting your time by blaming
yourself after failing. If you don’t
fail, you don’t learn. Rather than
being gloomy, learn from your
mistakes and be a better version of
yourself. And if opportunity doesn’t
knock, build a door.
What excites you about the
opportunities with data today
and in the future
Reliable data can help prevent
the cloudy business assumptions
that can mislead both customers
and the actual employees of
the company. In my opinion, the
satisfaction of customers and
employees are two key factors that
drive the success of a company.
Rather than solely trying to develop
sophisticated algorithms to improve
the results of of learning models,
we should also explore how the
models themselves can be applied
to and impact business decision-
making.
////
QA
WE SAT DOWN WITH AGUS AND ASKED HIM A FEW
QUESTIONS ABOUT HIS PROJECT AND ASK WHAT HE
THINKS THE FUTURE HOLDS FOR HIMSELF.
“Meeting so
many new
people from
different
backgrounds
widened my
perspective”