directing intelligence in_retail

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


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


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.


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.


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


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.


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


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


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.


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.


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.


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


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.


case that store status changes (becomes a

discount market or a delicatessen store).


case that area properties change (metro station,

commercial center, etc...).


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


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.

directing intelligence in_retail

Data & Analytics

business strategy

business objectives

customers churn

customers segmentation

supply chain

consequence retailers

integrated applications

reporting application