www.directing.gr – [email protected]
Directing Intelligence
in Retail
Creates Business Intelligence
Strategy and Solutions
aligned to Business Objectives
of a European leader Supermarket Chain
by Gregory Philippatos
10/9/2014
www.directing.gr – [email protected]
CONTENTS
1. The Challenge............................................................................................................... 3
1.1. Market Evolution .................................................................................................................. 3
2. Directing Intelligence in Business ................................................................................ 4
3. The Solution. Align Knowledge Engineering to Business Strategy ................................. 5
3.1. Customer Centric Positioning................................................................................................ 5
4. The Solution. DATACTIF© Business Intelligence Opensystem....................................... 6
5. DATACTIF©. Integrated Applications ........................................................................... 7
5.1. Machine Learning Application ............................................................................................... 7
5.2. Customers Segmentation ..................................................................................................... 7
5.3. Reporting Application ........................................................................................................... 9
5.4. Association Rules ................................................................................................................10
5.5. Hyper Clusters ....................................................................................................................10
5.6. Customers Segmentation History ........................................................................................12
5.7. Stores Network performance evaluation. .............................................................................13
5.8. Suppliers Performance Evaluation .......................................................................................14
5.9. Customers Churn, LTV and LTC. .........................................................................................15
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1. THE CHALLENGE
A European Leader Supermarket chain decided to
design and implement a Business Intelligence
Strategy and applications in order to increase
competitiveness and profitability.
1.1. Market Evolution
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.
Competitive landscape. Deflation and insecurities
lead to cautious consumers. As a consequence
retailers need to find strategies that allow them to
differentiate from their competitors within their
segment.
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).
The result is that consumers in Europe will adjust
their lifestyle (e.g. diet, leisure) leading to changing
demands in personal care categories, technology-
advanced products and easy-to-use consumer
solutions (e.g. assisted living products for older
people).
Internet of Things. Technology adoption requires
new service models, offered via the internet and
moving beyond selling individual products.
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2. DIRECTING INTELLIGENCE IN BUSINESS
Business Intelligence is a vital component in
strategic planning for companies that are aware of
worldwide competition, ever-shorter production
cycles and increasing customer requirements. Due
to actual speed of communication through internet
of things, it is important to identify meaningful
patterns quickly within the collected data.
DIRECTING's mission is the design of knowledge
architectural plan as part of business engineering
and the creation of Business Intelligence models
and applications in order to provide decision makers
a business valuable knowledge, diffused to all
management levels, increasing this way teamwork,
efficiency and profitability.
This is been accomplished by the initial concept
of DATACTIF®, a Business Intelligence Platform
able to generate concept-applications tailor made
for each enterprise, enriching in same time each
case, with a 20 year overall experience of learning
processes, accumulating knowledge and finding
solutions to problems in industrial, financial and
retail sectors.
DATACTIF® uses machine learning methodology
and algorithms such as neural network, Kohonen
SOM, fuzzy systems, genetic algorithms, Support
Vector Machines, etc… and contains visualization
methods that allows both a global and an analytical
view to information.
Contrary to the high level of complexity of used
algorithms, DATACTIF® user interface requires no
knowledge in statistics and in computer science.
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3. THE SOLUTION. ALIGN KNOWLEDGE ENGINEERING TO BUSINESS STRATEGY
3.1. 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
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4. THE SOLUTION. DATACTIF© BUSINESS INTELLIGENCE OPENSYSTEM
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 and created
specific 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’s Reporting Tools in order
to present a multi level, combined view allowing to
the end user to create its own reporting
DATACTIF® as end result, allows real time, direct,
substantive assessment of enterprise corporate
knowledge through visualization offered by and at
all levels.
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5. DATACTIF©. INTEGRATED APPLICATIONS
5.1. 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.
5.2. 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.
For example customers who prefers white yogurt
0% fat are different from those who prefers a yogurt
with fruits and 2%. And a customer who prefers
Danone yogurt differs from whom prefer Nestle.
Or another example; in some cases "FRUCTOZE"
is associated with some diseases, including
metabolic syndrome and insulin resistance and in
some others to diet and fitness. Our objective
concerning segmentation was to obtain scientific
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state of the art segmentation and in same time useful
for business decision making
In order to solve this problem we opted for a multi
layers approach, training Self-Organizing Maps first
with an intermediary categorization (sub categories
with brands) and finally with detailed products.
We used as data, customers annual transactions and
unsupervised learning (neural networks and Self
Organized Map).
We selected the 25 clusters solution (5 X 5) as it is
important for a retail company to have the less
possible groups of customers in order to design
large scale, cost effective business and marketing
campaigns.
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
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5.3. 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.
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5.4. 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 categories of products, promotional
campaigns evaluation, or external data such as
qualitative researches, etc…
This way based on an opinion research we could
create Life Style Segmentation based on clusters
classification by social type indexes. For example
we can see that Health & Wellbeing social type
scores the maximum in the cluster 1 (Organic
customers).
Figure 4. Example of Life Style Segmentation
5.5. 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 easily
relate Behavioral, Benefit and Life Style
Segmentation results unified in a way that allows to
the enterprise to design cost efficient large scale
business strategies (deals with suppliers, price
reduction or in store promotions) and marketing
campaigns for each Hyper Cluster.
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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.
Ready-made meals, meat lovers, fish, mussels and
drinks. Potential of becoming high spending clients
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. Loyal
clients, average spending.
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. No
ready-made meals. Good and loyal clients
5. ALL SHOPPING IN SHOP
Families with big children, value for money. Best,
dedicated and high spending clients. Preference to
meat, variety of cheeses, ready-made meals.
6. EXPERIMENTALS
Young couples, trendy, price sensitive. Beef fillet,
mussels, ostrich meat, try new tastes. Average
spending, high potential.
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5.6. 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. Customer Segmentation History
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5.7. 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…),
data concerning the social, demographic, economic
and structural environment of each area, data
about the competition and data concerning
customers provided by Segmentation Application,
we created DATACTIF® Network Evaluator
that realized with success the following tasks:
For new stores : Evaluation of new site location
options and prediction of future profitability.
For existing stores :
Profitability's Prediction for next year.
Estimation of the effect on the profitability in
case of a new competitor appearance.
Estimation of the effect on the profitability in
case that store status changes (becomes a
discount market or a delicatessen store).
Estimation of the effect on the profitability in
case that area properties change (metro station,
commercial center, etc...).
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5.8. Suppliers Performance Evaluation
Ssupplier's evaluation in a Customer Centric
Strategy, has to provide knowledge beyond market
shares and profitability performances, taking into
consideration suppliers brands and their marketing
strategy, brands impact to customers and through
this impact the result in the relation between the
retailer and its customers.
An overall Supplier 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, Customers Segments
importance to the enterprise profitability, Brands
impact to Customer Segmentation, etc...
Classification by the impact of brands to the
Enterprise overall Profitability
Evaluation by each index separately
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5.9. Customers Churn, LTV and LTC.
DATACTIF® LTC-LTV Application is trained
with historical data and predicts churn, Life Time
Cycle and Life Time Value.
DATACTIF® LTC-LTV also connects the LTV
curve with other important economical factors, such
as market share, sales, net profit, etc….
In addition, this tool assists the user in decision
making by suggesting optimum actions to be taken
in difficult or unknown market conditions.
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