Directing Intelligence in retail

www.directingintelligence.com– [email protected]

D i r e c t i n g

Intelligence in Retail

http://www.directingintelligence.com/


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

Data & Analytics

Transcript of Directing Intelligence in retail