This document describes a business intelligence solution called DATACTIF RETAIL that was implemented for a European supermarket chain. The solution includes several integrated applications like customer segmentation, behavior prediction, and intelligent stock management. It analyzes customer purchase data using machine learning to generate insights. These applications help the supermarket optimize operations, increase customer retention and profitability.
DATACTIF®, is a Business Intelligence Platform
that generates concept-applications tailor made
for each enterprise needs, enriching in same time
each specific case, with a 15 year experience of
learning processes and accumulating knowledge.
DATACTIF® uses machine learning methodology
and algorithms such as neural network, fuzzy
systems, genetic algorithms, Support Vector
Machines, etc… and contains visualization methods
that allows a global view on the domain that is
under analysis, and an analytical view to all details
offered by the existing data.
Digitizing Insurance - Transforming Legacy Systems to Adopt Modern and Emergi...RapidValue
This paper explains how insurers can use the digitization (digitalization) opportunity to deliver greater value to their customers. It is also, revealed how the companies can gain competitive advantage. Insurers are able to engage more intensely with the existing customers and also, attract newer customers with the help of innovative products. Digitizing improves profitability and facilitates growth.
DATACTIF®, is a Business Intelligence Platform
that generates concept-applications tailor made
for each enterprise needs, enriching in same time
each specific case, with a 15 year experience of
learning processes and accumulating knowledge.
DATACTIF® uses machine learning methodology
and algorithms such as neural network, fuzzy
systems, genetic algorithms, Support Vector
Machines, etc… and contains visualization methods
that allows a global view on the domain that is
under analysis, and an analytical view to all details
offered by the existing data.
Digitizing Insurance - Transforming Legacy Systems to Adopt Modern and Emergi...RapidValue
This paper explains how insurers can use the digitization (digitalization) opportunity to deliver greater value to their customers. It is also, revealed how the companies can gain competitive advantage. Insurers are able to engage more intensely with the existing customers and also, attract newer customers with the help of innovative products. Digitizing improves profitability and facilitates growth.
Driving Value Through Data Analytics: The Path from Raw Data to Informational...Cognizant
As organizations gather and process colossal amounts of data, analytics is essential for operational and strategic excellence. We offer a guide to the phases of the data analytics journey, from descriptive to diagnostic to predictive to prescriptive, covering intentions, tools and people considerations.
Streaming Big Data Analysis for Real-Time Sentiment based Targeted Advertising IJECEIAES
Big Data constituting from the information shared in the various social network sites have great relevance for research to be applied in diverse fields like marketing, politics, health or disaster management. Social network sites like Facebook and Twitter are now extensively used for conducting business, marketing products and services and collecting opinions and feedbacks regarding the same. Since data gathered from these sites regarding a product/brand are up-to-date and are mostly supplied voluntarily, it tends to be more realistic, massive and reflects the general public opinion. Its analysis on real time can lead to accurate insights and responding to the results sooner is undoubtedly advantageous than responding later. In this paper, a cloud based system for real time targeted advertising based on tweet sentiment analysis is designed and implemented using the big data processing engine Apache Spark, utilizing its streaming library. Application is meant to promote cross selling and provide better customer support.
ISO standards for identification of medical products (IDMP) are set to require pharmaceuticals companies to comply with medicinal identification standards. We explain how to get started and navigate a smooth IDMP transition.
Cognitive Integration: How Canonical Models and Controlled Vocabulary Enable ...Cognizant
For pharmaceuticals companies dealing with multiple partners' systems, employing a canonical model for data communications facilitates point-to-point integration, and applying a controlled vocabulary (CV) in such models alleviates semantical ambiguity and facilitates cognitive and systems integration. We demonstrate how this works with a pharma business scenario involving Contract Research Organizations (CROs).
Consumer Behavior project. Examine and define best ways for Consumer Research Company (Equitec) to target and reach new customers, along with suggesting new ways for the company to market itself.
Analytics is a two-sided coin. While on one side, it uses
descriptive and predictive models to gain valuable knowledge from data, i.e. data analysis, on the other side, it provides insight to recommend action or guide decision making, i.e. communication
Equitec's production-based solutions are a result of the multidimensional data obtained from Consumer Dynamics, the company's proprietary information platform. By incorporating the consumer decision process (CDP) model with the Consumer Dynamics platform, similar variables can be recognized and analyzed to provide solutions for firms.
North American Utility Sparks Up its Complaint Handling SystemCognizant
Electric utility's new complaint handling system reduces resolution times, increases staff productivity, boosts customer satisfaction and improves regulatory compliance.
Knowledge management (KM) has become an effective way of managing organization‟s intellectual capital or, in other words, organization‟s full experience, skills and knowledge that is relevant for more effective performance in future. The paper proposes a knowledge management to achieve a competitive control of the machining systems. Then an application of Knowledge Management in engineering has been attempted to explain. The model can be used by the manager for the choosing of competitive orders.
Turning Customer Knowledge into Business GrowthCognizant
By embracing big data and predictive analytics to create multidimensional customer profiles, companies can make more informed business decisions that better anticipate customer needs, wants and desires.
A novel approach to dynamic profiling of E-customers considering click stream...IJECEIAES
In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.
Clear Direction on Using Big Data to Solve Retail ProblemsBill Bishop
It’s time to identify where retail analytics can deliver specific, practical benefits to the bottom line and strengthen competitive position. This new report cuts through the vastness of big data's potential and shows how the opportunities stack up based on the results of our recent survey.
Big data, analytics and the retail industry: LuxotticaIBM Analytics
Luxottica Retail North America is the world's largest designer, manufacturer, distributor and seller of luxury and sports eyewear. To make better use of the data on its 100 million customers, and increase marketing effectiveness, Luxottica turned to IBM. With IBM's Customer Intelligence Appliance (CIA), Luxottica gained a 360-degree view of its customers and can now fine tune its marketing efforts to ensure customers are targeted with products they actually want to buy.
Driving Value Through Data Analytics: The Path from Raw Data to Informational...Cognizant
As organizations gather and process colossal amounts of data, analytics is essential for operational and strategic excellence. We offer a guide to the phases of the data analytics journey, from descriptive to diagnostic to predictive to prescriptive, covering intentions, tools and people considerations.
Streaming Big Data Analysis for Real-Time Sentiment based Targeted Advertising IJECEIAES
Big Data constituting from the information shared in the various social network sites have great relevance for research to be applied in diverse fields like marketing, politics, health or disaster management. Social network sites like Facebook and Twitter are now extensively used for conducting business, marketing products and services and collecting opinions and feedbacks regarding the same. Since data gathered from these sites regarding a product/brand are up-to-date and are mostly supplied voluntarily, it tends to be more realistic, massive and reflects the general public opinion. Its analysis on real time can lead to accurate insights and responding to the results sooner is undoubtedly advantageous than responding later. In this paper, a cloud based system for real time targeted advertising based on tweet sentiment analysis is designed and implemented using the big data processing engine Apache Spark, utilizing its streaming library. Application is meant to promote cross selling and provide better customer support.
ISO standards for identification of medical products (IDMP) are set to require pharmaceuticals companies to comply with medicinal identification standards. We explain how to get started and navigate a smooth IDMP transition.
Cognitive Integration: How Canonical Models and Controlled Vocabulary Enable ...Cognizant
For pharmaceuticals companies dealing with multiple partners' systems, employing a canonical model for data communications facilitates point-to-point integration, and applying a controlled vocabulary (CV) in such models alleviates semantical ambiguity and facilitates cognitive and systems integration. We demonstrate how this works with a pharma business scenario involving Contract Research Organizations (CROs).
Consumer Behavior project. Examine and define best ways for Consumer Research Company (Equitec) to target and reach new customers, along with suggesting new ways for the company to market itself.
Analytics is a two-sided coin. While on one side, it uses
descriptive and predictive models to gain valuable knowledge from data, i.e. data analysis, on the other side, it provides insight to recommend action or guide decision making, i.e. communication
Equitec's production-based solutions are a result of the multidimensional data obtained from Consumer Dynamics, the company's proprietary information platform. By incorporating the consumer decision process (CDP) model with the Consumer Dynamics platform, similar variables can be recognized and analyzed to provide solutions for firms.
North American Utility Sparks Up its Complaint Handling SystemCognizant
Electric utility's new complaint handling system reduces resolution times, increases staff productivity, boosts customer satisfaction and improves regulatory compliance.
Knowledge management (KM) has become an effective way of managing organization‟s intellectual capital or, in other words, organization‟s full experience, skills and knowledge that is relevant for more effective performance in future. The paper proposes a knowledge management to achieve a competitive control of the machining systems. Then an application of Knowledge Management in engineering has been attempted to explain. The model can be used by the manager for the choosing of competitive orders.
Turning Customer Knowledge into Business GrowthCognizant
By embracing big data and predictive analytics to create multidimensional customer profiles, companies can make more informed business decisions that better anticipate customer needs, wants and desires.
A novel approach to dynamic profiling of E-customers considering click stream...IJECEIAES
In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.
Clear Direction on Using Big Data to Solve Retail ProblemsBill Bishop
It’s time to identify where retail analytics can deliver specific, practical benefits to the bottom line and strengthen competitive position. This new report cuts through the vastness of big data's potential and shows how the opportunities stack up based on the results of our recent survey.
Big data, analytics and the retail industry: LuxotticaIBM Analytics
Luxottica Retail North America is the world's largest designer, manufacturer, distributor and seller of luxury and sports eyewear. To make better use of the data on its 100 million customers, and increase marketing effectiveness, Luxottica turned to IBM. With IBM's Customer Intelligence Appliance (CIA), Luxottica gained a 360-degree view of its customers and can now fine tune its marketing efforts to ensure customers are targeted with products they actually want to buy.
Omnichannel Customer Experience. Companies such as Amazon, Facebook, Google, Apple already know that the future of user experience is automated interface creation depending on customer needs.
Big Data: Real-life Examples of Business Value GenerationCapgemini
This presentation looks at real-world cases of how organizations are using, or planning to use, big data technology to drive value. It looks at the different ways in which the technology is being used in a business context. Examples are drawn from Retail, Telco, Financial Services and Consumer goods.
It also develops a range of business scenarios from simple cost reduction through to new business models specifically looking at how the business case has been built and what value has been realized.
First presented by Richard Brown, Capgemini Program Lead for Business Information Management, at the IP Expo – Big Data Summit 2014.
http://www.capgemini.com/big-data-analytics
Big Data in Retail - Examples in ActionDavid Pittman
This use case looks at how savvy retailers can use "big data" - combining data from web browsing patterns, social media, industry forecasts, existing customer records, etc. - to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions, and monitor real-time analytics and results. For more information, visit http://www.IBMbigdatahub.com
Follow us on Twitter.com/IBMbigdata
The concept of Big Data emphasizes the use of the complete data set to analyze process and predict various phenomena in the business world. This document describes the business uses of Big Data and outlines a Strategy for implementing Big Data analytics for Social Media
[AI Webinar Series P1] - How Advanced Text Analytics Can Increase the Operati...JK Tech
Digitization is considered as the next step-change that will have a bigger impact on businesses than even the internet. To win in the digital journey, companies must act now, or be left behind wondering what happened!
In this webinar series, JKT Smart Analytics demonstrates how they empower their customers to create maximum business value out of this eminent Digital data explosion through digital business empowerment by leveraging the digitization to increase their top-line revenue – customer experience, optimize the bottom-line costs – operational efficiency, enhancing the safety factor and reinventing the business process in line with the changing world.
This webinar is focused on how our AI-based text analytics solutions – First, JKT Social Media Radar; a SaaS-based AI NLP Platform, helping organizations to gain insights on market and customer perceptions on their brands, products & services. Secondly, Sales Promotion Recommendation Engine helps customers to enhance their top-line growth and streamline the bottom-line costs.
KEY TAKEAWAYS:
1) How should a business plan their journey through the Digital data revolution?
2) How can a company make use of digital data to create effective data strategies for the increased outcome(s)?
3) How IT practitioners can catalyst the digital data mining journey and attract business adoption?
4) JKT Social Media Radar solution – What, Why, Supporting Business applications, and more.
5) How can companies reduce operational costs by automating human effort-intensive tasks using cognitive Analytics?
When it comes to product development, companies have long relied on traditional tools and approaches. By incorporating predictive analytics into the process, organizations can sharpen their forecasts; better predict product performance, failures, and downtime; and generate more value for the business and its customers. Yet doing so requires companies to thoroughly assess their strategic goals, their appetite for investment, and their willingness to experiment.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
6. 17448 33940-1-ed 20 apr 13mar 28dec2018 ed iqbal qcIAESIJEECS
Business intelligence comprises of tools and applications that are leverages software and services to translate data into intelligent actions for strategic, tactical and operational decisions. The intelligent business solution facilitates and develops the service provided to the market researchers, saves time and effort needed to identify the customers predict demand and manage production more efficiently, ability to explore possibilities to increase revenue. The purpose of this paper is using business intelligence solutions for forecasting in Marketing Researches. The intelligence solutions are helping the market researchers to achieve efficiency, effectiveness, and differentiation.
NMIMS Semester 1 Assignment Solution Dec 2021 palaniappann
Sir / Madam,
Prof.Dr.N.Palaniappan.,MBA.,MCom.,MPhil.,PhD. has 15 years of teaching experience in MBA Business schools. For last fifteen years Prof.Dr.N.Palaniappan.,MBA.,MCom.,MPhil.,PhD has taught various subjects from Marketing, Finance, Human Resource Management, Information Systems, International Business and General Specializations. He has written many research papers and case studies.
Prof.Dr.N.Palaniappan.,MBA.,MCom.,MPhil.,PhD organizes online MBA subject coaching / MBA Assignment help and MBA Project help. Many clients national and international has appreciated Prof.Dr.N.Palaniappan.,MBA., MCom.,MPhil.,PhD for his timely help in the assignments and projects and MBA subject coaching.
You can call him on his mobile no. 09025810064 (whatsapp available) or mail him at palaniappanmail@gmail.com. He does help/guide for the below question. If urgent or any query’s, Please feel free to call him on his mobile no. 9025810064 (whatsapp available) or do mail on palaniappanmail@gmail.com. He does help/guide for the below question
Contact:
Prof.Dr.N.Palaniappan.,MBA.,MCom.,MPhil.,PhD
Mail ID: palaniappanmail@gmail.com
Ph: - 9025810064 (whatsapp available)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
3. www.directing.gr – info@directing.gr - philippatosgregory@gmail.com
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.
4. www.directing.gr – info@directing.gr
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
7. www.directing.gr – info@directing.gr - philippatosgregory@gmail.com
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.
8. www.directing.gr – info@directing.gr
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 :
9. www.directing.gr – info@directing.gr - philippatosgregory@gmail.com
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
10. www.directing.gr – info@directing.gr - philippatosgregory@gmail.com
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.
11. www.directing.gr – info@directing.gr
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.
12. www.directing.gr – info@directing.gr - philippatosgregory@gmail.com
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.