BIG DATA AND RETAIL

www.directing.gr – info@directing.gr
D i r e c t i n g
Intelligence in Retail
Big Data Case Study

www.directing.gr – info@directing.gr - philippatosgregory@gmail.com
1. The Challenge............................................................................................................... 3
1.1. Market Context .................................................................................................................... 3
2. The Solution. Directing Intelligence in Business ........................................................... 4
2.1. Customer Centric Positioning................................................................................................ 4
2.2. DATACTIF© Business Intelligence Platform.......................................................................... 5
2.3. DATACTIF RETAIL©. Integrated Applications....................................................................... 6
2.3.1. Machine Learning Application.......................................................................................... 6
2.3.2. Customers Segmentation ................................................................................................ 6
2.3.3. Reporting Application...................................................................................................... 7
2.3.4. Association Rules............................................................................................................ 8
2.3.5. Customers Segmentation History .................................................................................... 9
2.3.6. Customers Behavior Prediction (Churn, LTV and LTC, etc...). .........................................10
2.3.7. Stores Network performance evaluation.......................................................................11
2.3.8. Assortment Evaluation ...................................................................................................11
2.3.9. Intelligent Stock and Waste Reduction Management System..........................................12

1. THE CHALLENGE.
A European Leader Supermarket chain decided to
design and implement a Business Intelligence
Strategy in order to increase competitiveness and
profitability
1.1. Market Context
Many European markets are today characterized as
very mature with declining growth figures,
constantly high unemployment and stagnation of
inflation-adjusted income.
These characteristics, together with an altered
demographic structure in almost all countries, are
changing the consumer demands. Retail industry is
facing a magnitude of challenges that could be
categorized as follow:
Mondialisation. Supply chain and logistics systems
enable retailers to produce, purchase and sell
products worldwide.
Demographic shifts. Demographic shifts (aging
population, increase flow of immigrants, increased
urbanization, etc…) determine essential aspects of
retail as they influence or change consumers’ needs
and demands.
Demographic shifts open up new niche markets and
can require retailers to start new brands, widen or
deepen their product assortment, adapt their pricing
philosophy and service policy and change the
design and layout of their shops and commercial
signage.
Health and wellbeing. Health, safety and wellbeing
will likely become the most important factors in
near future due to cultural reasons but also due to
the increase of ‘lifestyle diseases’ (cancer, diabetes,
heart diseases, asthma, obesity and depression).
Internet of Things. Technology adoption requires
new service models, offered via the internet and
moving beyond selling individual products.

2. THE SOLUTION. DIRECTING INTELLIGENCE IN BUSINESS
2.1. Customer Centric Positioning
Consumers are the ultimate arbiters of enterprise
ability to identify and predict market trends and to
procure and distribute products and services that
represent desired customer value, at the right price
and through the right channels.
Firms must be aligned to consumers’ continually
evolving needs and expectations of value.
As a result, the ability to innovate successfully to
create customer-centric differentiation is critical to
the overall success of the sector and increasingly
decisive in the survival of individual enterprises.
In order to achieve a Customer-Centric framework,
we created a Business Intelligence architectural plan
that analyzes the interferences (input) of all external
factors on customers and the consequences on their
final purchase decision (output).
Above Figure. Business Intelligence architectural Plan

2.2.DATACTIF© Business Intelligence Platform
Based on the above strategy we designed
conceptual, logical and data models and the
adequate data warehouse, after an in depth audit of
business processes and aims, IT infrastructure,
human resources availability and experience,
transactional and other data quality, qualitative and
quantitative researches as well as business scenarios
that should be realized.
We adapted DATACTIF® platform that uses
machine learning methodology and algorithms such
as neural network, fuzzy systems, genetic
algorithms, Support Vector Machines, etc… and its
applications : Customers Segmentation, Customers
Segmentation History, Association of heterogenous
information, Business Scenarios Evaluation and
results Prediction, Prediction of customers future
behavior, Suppliers Evaluation and Stores Network
evaluation and future profitability prediction.
Knowledge visualization in accordance to human
abilities is the most important step in data modeling.
We created DATACTIF RETAIL Reporting Tools
in order to present a multi level, combined view
allowing to the end user to create its own reporting
DATACTIF RETAIL® as end result, allows real
time, direct, substantive assessment of enterprise
corporate knowledge through visualization offered
by and at all levels.

2.3.DATACTIF RETAIL©. Integrated Applications
2.3.1 . Machine Learning Application
Machine Learning Application performs training
of existing algorithms in DATACTIF's System, for
every new data set. It creates new entities in the data
warehouse as well as metadata and updates all
related applications.
The time period for a new training is defined by the
user, who can execute this task without a prior
knowledge of programming or statistics due to its
user friendly interface.
2.3.2 . Customers Segmentation
Customers Segmentation based on purchase
behaviour, is in the heart of a Customer Centric
Business Intelligence platform.
The biggest problem with segmentation concerning
data, is that a supermarket has a huge, continuously
changing number of product codes (new products,
seasonal products, one off codes due to promotions
but different from those using for the same products
the rest of the year, etc…) that makes any
segmentation based on purchase behaviour almost
impossible. In the other hand using only categories
of products make decision makers loose information
that only products detailed description offers.
We designed a software able to normalize
automatically products with detailed description (by
incorporating for example all promotions to the core
product) andwe used as data, customers annual
transactions and unsupervised learning (Self
Organized Map).
We selected the 25 clusters solution (5 X 5) as the
ideal dimension regarding scientific integrity and
business usage effectiveness

2011 Segmentation. 25 distinctive Clusters
Features extracted values allows us to examine each
cluster separately, finding how and why it was
formed as in Figure 1 (Cluster 11 made of families
with babies, that prefer biological products).
Figure 1. Behavioral Segmentation. How clusters
are formed (cluster 11 in this figure)
By classifying clusters based on data such as :
clusters sales, gross profit, etc... we obtained the
economical impact of each cluster on enterprise
profitability.
Figure 2.Economic impact of Cluster 11
2.3.3 . Reporting Application
DATACTIF® reporting module offers an analytical
approach to each cluster or combination of clusters
about social and demographic details, store
preference and other information contained to data
warehouse.

2.3.4 . Association Rules
In the context of a Customer Centric knowledge
model, association rules allows to relate clusters
with any kind of information provided from both
internal, such as promotional campaigns evaluation,
or external data such as qualitative researches and
specially data from social media
2.3.4.1 i_Social Network Analyzer
Using i_Social Network Analyzer we were able to
identify communities on social networks, how they
evolve in relation with SM Corporate Brand,
Promotional Offers and Social activities.
2.3.4.2 i_analyzer. Text Mining Suite
Using i_analyzer we could analyze comments in
social media but also comments and texts coming
from emails, complaints, etc... identifying patterns
and associations between texts providing logical
meaning able to be used in social and commercial
actions. This way we could create Life Style
Segmentation based on clusters classification by
social type indexes.
2.3.4.3 Hyper Clusters
Based on features extracted values of each cluster
and on clusters similitude’s analysis, we obtained
6 Groups of Clusters, called Hyper Clusters. We
need Hyper Clusters because we can relate
Behavioral, Benefit and Life Style Segmentation
results unified in a way that allows to the enterprise
to design large scale business strategies
Based on combination of purchase behavior, life
style attitudes and economic impact to
Supermarket profitability we could describe 6
Hyper Clusters as follow :

1. TRADITIONALS
Conservative third age couples, pensioners, medium
class, with ....cholesterol (sugar substitute and
margarine), price sensitive, average spending and
loyal clients
2. BON VIVEURS
Families of high income with small children,
conservative and gourmand in eating habits. They
do not pay much attention to healthy eating rules.
3. GOURMET COSMOPOLITAN
Families with small children. Modern and educated,
cosmopolitan, high income, they take care of their
diet and they choose beef fillet, ethnic food.
4. HEALTHY LIVING
Young couples with baby/child. People of middle-
upper class and upper educational level. They prefer
organic products, veal, fruits and vegetables.
5. ALL SHOPPING IN SHOP
Families with big children, value for money,
medium social class, clients that makes all their
shopping in Commercial Centers. Fans of
promotional offers.
6. EXPERIMENTALS
Young couples, trendy, price sensitive. Influenced
by social media comments, they share experiences.
Beef fillet, mussels, ostrich meat, try new tastes.
2.3.5 . Customers Segmentation History
Customers Segmentation observed through time,
offers a macroscopic point of view on customers
evolution in a social and economic context,
measuring in same time the efficiency of the
Enterprise's strategy. Customer Segmentation
History allows comparison for the same clients
between two time periods.
In the following example (Figure 6: comparison
between 2009 and 2010), we observe that 41,1% of
Cluster 5 clients (gate for new customers) remain in

the same cluster and have the same consumption
habits between 2009 and 2010. A significant part of
the rest, moves horizontally from cluster 5 to cluster
25 (all products from the same SM, that means they
became high spenders and loyal clients) and another
part moves vertically from cluster 5 to cluster 1
(fruits and vegetables, organic products). Another
benefit of Segmentation History is the
“visualization” of Loyalty and Churn.
Of course there are specific applications analyzing
and predicting Churn, Life Time Value and Cycle of
each customer or clusters of customers.
But with Segmentation History we have the “big
picture” about customers actual situation, evolution
and future trends.
Figure 6: comparison between 2009 and 2010
2.3.6 . Customers Behavior Prediction (Churn, LTV and LTC, etc...).
DATACTIF RETAIL ® LTC-LTV Application is
trained with historical data and predicts churn, Life
Time Cycle and Life Time Value as well as
Response to Promotional Activities.
DATACTIF RETAIL ® LTC-LTV also connects
the LTV curve with other important economical
factors, such as market share, sales, net profit,
growth evolution, etc….
In addition, this tool assists the user in decision
making by suggesting optimum actions to be taken
in difficult or unknown market conditions.

2.3.7 . Stores Network performance evaluation.
New Store best emplacement indication and profitability prediction
In retail business, it is crucial the ongoing
performance evaluation of existing stores and the
choice of the emplacement for a new one. Based on
historical data of existing stores (profitability,
surface, employees, facilities, etc…), social,
demographic, economic and structural environment
of each area data, data about competition and
customers, Network Evaluator realized with success
the following tasks:
For new stores : Evaluation of new site location
options, proposal for best emplacement and
prediction of future profitability for each option.
For existing stores :
i. Profitability's Prediction for next years.
ii. Estimation of the effect on the profitability in
case of a new competitor appearance.
iii. Estimation of the effect on the profitability in
case that area properties change (metro station,
commercial center, etc...).
2.3.8 Assortment Evaluation
Assortment evaluation in a Customer Centric
Strategy, has to provide knowledge beyond market
shares and profitability performances, taking into
consideration brands and their marketing strategy,
their impact to customers and through this impact
the result in the relation between the retailer and its
customers.

An overall Assortment Evaluation Index was
created based on brands (by categories of
products),as summary of partial indexes such as:
Category- Brand Gross Profit and Sales Evolution,
Brands Market Penetration, Number of different
Products per Brand on the shelf as well as display
(face, range, volume), Customers Segments
importance to the enterprise profitability, Brands
impact to Customer Segmentation, etc
2.3.9 . Intelligent Stock and Waste Reduction Management System
In the part Supplier _ Supermarket _ Consumer of
the Supply chain, most important reason of food
waste is the inefficient stock management into the
Supermarket area.
The other important reason is Customers demand.
We have already created a model supported by a
solution, DATACTIF RETAIL, that permits a deep
understanding of consumption trends and we have
also a consumption prediction model
Intelligent stock and waste management combining
information from clusters, consumption prediction
and indexes such as waste factor and products
expiration date, it performs stock optimization,
products waste reduction, clients satisfaction.

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