Directing Intelligence in retail

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D i r e c t i n g
Intelligence in Retail

1. Introduction. ................................................................................................................. 3
1.1. Market Context................................................................................................................3
2. The Solution. Directing Intelligence in Business............................................................ 4
2.1. Intelligent Enterprise Ecosystem .......................................................................................4
2.2. DATACTIF© RETAIL. Business Intelligence Platform ..........................................................5
2.2.1 Machine Learning Application.......................................................................................5
2.2.2 . Customers Segmentation...........................................................................................5
2.2.3 . Hyper Cluster............................................................................................................7
2.2.4 . Customers Segmentation History................................................................................8
2.2.5 . Customers Behavior Prediction (Churn, LTV and LTC, etc...). .......................................8
2.2.6 . Stores Network performance evaluation. New Store best emplacement indication
and profitability prediction ...........................................................................................9
2.2.7 Assortment Evaluation.................................................................................................9
2.2.8 . Intelligent Stock and Waste Reduction Management System......................................10
3. Summary..................................................................................................................... 11

1. INTRODUCTION.
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. Intelligent Enterprise Ecosystem
Business Intelligence as entity that process
information, transforms information into
knowledge, must be in the centre of Business and
Technology unified conceptual and operational
processes creating an Intelligent Enterprise
Ecosystem. In such an Intelligent Ecosystem, BI
applications, technical architecture, business
processes, systems, corporate and external data
work together under the business strategyIn order to
achieve a Intelligent Enterprise Ecosystem, 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).
Business Intelligence architectural Plan

2.2.DATACTIF© RETAIL. Business Intelligence Platform
DATACTIF RETAIL® platform uses machine
learning methodology and algorithms (neural
network, fuzzy systems, genetic algorithms, Support
Vector Machines, etc…) and performs : 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.
2.2.1 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.
2.2.2 . 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

2.2.3 . Hyper Cluster
Based on Behavioral, Benefit and Life Style (with
data provided from Social Media) Segmentation
results we obtained 6 Groups of Clusters, called
Hyper Clusters.
.
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.

2.2.4 . 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 figure, comparison between 2009
and 2010, we observe that 41,1% of basic and new
clients (Cluster 5) remain the same consumption
point of view. A significant part of the rest, moves
horizontally from cluster 5 to cluster 25 (high
spenders and loyal clients) and another part moves
vertically from cluster 5 to cluster 1 (fruits and
vegetables, organic products).
2.2.5 . 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.

2.2.6 . 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...).
2.2.7 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
2.2.8 . 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.

3. SUMMARY
DATACTIF gives answers to concrete business questions
by its 4 levels of analysis :
Full information concerning each client (i.e. for
one to one marketing),
25 segments – clusters based on purchase
behavior (i.e. for promotional campaigns),
6 Hyper Clusters based on purchase behavior as
well as life style profile (i.e. for mass
communication but targeted campaigns) and
4 Group based on the clients relation with the
Enterprise (i.e. for combination of activities)
QUESTIONS like :
Clients evolution (and most specifically new ones as they
allows to measure acquisition strategy effectiveness)
based on his purchase behavior.
Analysis of profitability of each client and each segment,
reaction to marketing and promotional activities, reasons
of churn, etc..
Customers Behavior Prediction (Churn, LTV and LTC,
etc...).
STORES. Performance evaluation, future profitability
prediction
ASSORTEMENT. Evaluation of assortment, results
prediction for modifications
Stock and Waste Management. 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.

Directing Intelligence in retail

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