MLSEV. Use Case: Online and Offline World in the Retail Sector

BigML, Inc
Online and Oﬄine World in the
Retail Sector
Friends or Enemies?
Miguel Blanco
Data Scientist, Good Rebels
Team work: Mar Castaño & Sergio Vázquez
2

BigML, Inc #MLSEV: Online and Ofﬂine World in the Retail Sector: Friends or Enemies? / Good Rebels 3
Who are we?
Good Rebels is a digital
strategy and creative company.
 
We help our clients to innovate at the intersection of
people, brands & technology.
We engage customers daily with our unique 
Smart Social methodology.

4 
OFFICES
MEXICO CITY
60 
CLIENTS
30 
CAPABILITIES
9M€ 
TURNOVER
120 
PEOPLE
BARCELONA
MADRID
BRIGHTON
+
++
Who are we?

PERFORMANCE
INTELLIGENCE
Developing deep insights
and strategy that help our
clients put people at the
heart of everything they do.
EXPERIENCE
Creating culturally relevant
products, services and
consumer experiences.
Delivering and
measuring real business
impact.
Accelerating change,
empowering people and
transforming organizations.
ENABLEMENT
I E
P E
CREATIVITYDATA
TECHNOLOGY
PROJECT
MANAGEMENT
Who are we?

BigML, Inc #MLSEV: Online and Ofﬂine World in the Retail Sector: Friends or Enemies? / Good Rebels
•Big company in the retail sector.
•76 stores in Spain.
•Powerful e-commerce.
•It buys and sells products to its customers.
6
Our case

How to improve the e-commerce
performance through digital marketing using
offline information
7
Our question

•E-commerce:
•Info about the client (location).
•Info about the transaction (price).
•Info about the purchased item.
•Where the item is stored.
8
Our data
•Stores:
•Info about the visits to the store.
•Info about the opinion of the
store (Google Reviews).
•Location.
•Zipcodes level.
•Info about the location.
•Population.
•Average spend per home.
•Average income per home.
•Background.
Online world
Offline world
External data: geographic and
demographic

Our tools
Python
Tableau
Geographic
information system
(GIS)
BigML

Our tools … as a kitchen
Python
Tableau
Geographic
information system
(GIS)
BigML

Our project
To cluster the territory to optimize
the digital marketing strategy

Data exploration
Around 12k zipcodes
in Spain.
Sorry Canaries.
45M people

Data exploration
Disparity in zipcodes sizes.
But we focus on the
background.

Data exploration
With GIS, we have
calculated the distance to
the nearest store

Data exploration
With GIS, we have the
distance to the
nearest store

Data exploration
We focus on the
assumption of 20 km
as a limit

Data exploration
Around 25M people
have a store in less
than 20 km.

Data exploration

First discovery
Awareness problem
in places far away from a store

First action
First and easy segmentation by
distance to the nearest store

Two groups
25
Population Customers
Total estimated income E-commerce bill

We attack each group separately
Places far away
from the store
Places near
the store
Info about the place.
The e-commerce.
Demographic.
The e-commerce.
Demographic.
+
Info about the store
Two groups

First big group
Let’s begin with those
far from the store

Places far from a store
28

Population Customers
Total estimated income E-commerce bill
Two groups, back again

Places near the store
Let’s move to those that are
near the store
Places near
the store
The e-commerce.
Demographic.
+
Info about the store

What about the store?
We’ll use the
Google Reviews

More than 10k reviews
From the 76 stores
Rating from 1 to 5
And text

Were we talking about zipcodes or stores?
45

Conclussions
•Awareness problem in zipcodes far from the store.
•Info to improve campaigns by location.
•Mechanisms to cross-selling between e-
commerce and store.
•Associate territory characteristics to customers
(to e-mail strategy).
•Reviews analyzed to help stores to identify
strengths and weakness.
51

Take aways
•All machine learning projects need work.
•You need to work hard on the data: sources, format, …
•You need expertise on the field.
•You need the basics on math and stats.
• But the most difficult part can be simplified.
52

Questions?
Now…
or to miguel.blanco@goodrebels.com
53

MLSEV. Use Case: Online and Offline World in the Retail Sector

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MLSEV. Use Case: Online and Offline World in the Retail Sector