How to use the power of data in e-Commerce? Applying the Big Data solutions makes it possible to analyse data in real time. This allows us to use the data not for reports only, but to translate them into action.
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Analytics and its Application in E-CommerceUyoyo Edosio
Abstract-This era unlike any, is faced with explosive
growth in the size of data generated/captured. Data
growth has undergone a renaissance, influenced
primarily by ever cheaper computing power and
the ubiquity of the internet. This has led to a
paradigm shift in the E-commerce sector; as data is
no longer seen as the byproduct of their business
activities, but as their biggest asset providing: key
insights to the needs of their customers, predicting
trends in customer’s behavior, democratizing of
advertisement to suits consumers varied taste, as
well as providing a performance metric to assess the
effectiveness in meeting customers’ needs.
This paper presents an overview of the unique
features that differentiate big data from traditional
datasets. In addition, the application of big data
analytics in the E-commerce and the various
technologies that make analytics of consumer data
possible is discussed.
Further this paper will present some case studies of
how leading Ecommerce vendors like Amazon.com,
Walmart Inc, and Adidas apply Big Data analytics in
their business strategies/activities to improve their
competitive advantage. Lastly we identify some
challenges these E-commerce vendors face while
implementing big data analytic
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Data Warehouse Process and Technology: Warehousing Strategy, Warehouse management and Support Processes.
Warehouse Planning and Implementation.
H/w and O.S. for Data Warehousing, C/Server Computing Model & Data Warehousing, Parallel Processors & Cluster Systems, Distributed DBMS implementations.
Warehousing Software, Warehouse Schema Design.
Data Extraction, Cleanup & Transformation Tools, Warehouse Metadata
,data warehouse process and technology: warehousing ,warehouse management and support processes. wareh ,c/server computing model & data warehousing ,parallel processors & cluster systems ,distributed dbms implementations. warehousing sof ,warehouse schema design. data extraction ,cleanup & transformation tools ,warehouse metadata
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Customer segmentation is a Project on Machine learning that is developed by using Clustering & clustering is the technique that comes under unsupervised learning of machine learning.
Segmentation allows prospects based on their wants and needs. It allows identifying the most valuable customer segment so the basis of it vender improve their return on marketing investment by only targeting those likely to be your best customer.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Surprising failure factors when implementing eCommerce and Omnichannel eBusinessDivante
We work on the large Omnichannel and eCommerce projects in Europe. Therefore, we can see from the inside how many companies approach this topic. Comparing it with the obtained results, we can determine positive and negative factors influencing success with great certainty. In this presentation we share stories of companies that are not mentioned in our case studies. These are the stories of bad choices, leading to failure.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Data Warehouse Process and Technology: Warehousing Strategy, Warehouse management and Support Processes.
Warehouse Planning and Implementation.
H/w and O.S. for Data Warehousing, C/Server Computing Model & Data Warehousing, Parallel Processors & Cluster Systems, Distributed DBMS implementations.
Warehousing Software, Warehouse Schema Design.
Data Extraction, Cleanup & Transformation Tools, Warehouse Metadata
,data warehouse process and technology: warehousing ,warehouse management and support processes. wareh ,c/server computing model & data warehousing ,parallel processors & cluster systems ,distributed dbms implementations. warehousing sof ,warehouse schema design. data extraction ,cleanup & transformation tools ,warehouse metadata
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Customer segmentation is a Project on Machine learning that is developed by using Clustering & clustering is the technique that comes under unsupervised learning of machine learning.
Segmentation allows prospects based on their wants and needs. It allows identifying the most valuable customer segment so the basis of it vender improve their return on marketing investment by only targeting those likely to be your best customer.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Surprising failure factors when implementing eCommerce and Omnichannel eBusinessDivante
We work on the large Omnichannel and eCommerce projects in Europe. Therefore, we can see from the inside how many companies approach this topic. Comparing it with the obtained results, we can determine positive and negative factors influencing success with great certainty. In this presentation we share stories of companies that are not mentioned in our case studies. These are the stories of bad choices, leading to failure.
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 & Hadoop Applications in E-CommerceSkillspeed
Explore the applications of BIG Data & Hadoop in eCommerce via Skillspeed.
BIG Data & Hadoop in eCommerce is a key differentiator, especially in terms of generating optimized customer & back-end experiences. They are used for tracking consumer behavior, optimizing logistics networks and forecasting demand - inventory cycles.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
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
Big data has been a buzzword in today’s business world for the last couple of years and especially in the context of e-commerce. The textbook definition of big data is ‘data sets that are so large or complex that traditional data processing applications are inadequate to deal with them‘. When a person reads this definition, he has the right to assume that processing big data requires state-of-the-art computing technologies coupled with best-in-class engineering approaches and that is pretty much true. Today’s banks, large retailers, insurance companies and telecom giants are all searching for new methods and innovative vendors to be able to derive meaningful results from the vast sources of data they have on hand. If we specifically focus on the use of big data in ecommerce, we may comment that big data on the e-commerce websites and of course on social media never sleeps.
Looking for interesting content on e-commerce and personalization? Check this out : http://www.perzonalization.com/e-commerce-personalization-resources/
Cheap data storage and high-performance analytics are going to change the face of retail sector. And big data is going to play pivotal role in this technological revolution. You can find other reports related to Big data at http://www.marketresearchreports.com/big-data
All you wanted to know about analytics in e commerce- amazon, ebay, flipkartAnju Gothwal
One stop solution to your search for Data Analytics practices being followed by various E-commerce giants like Amazon, Ebay, Flipkart, Snapdeal and also some of the suggested approaches.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The challenge for retailers is no longer how to lure shoppers online, but how to add value for their existing customer base. Examine critical business functions for online commerce success, as well as best-in-class factors — transpromo, crosschannel campaigns and deep-dive personalization. Learn the “four Cs” of staying alive — conversion, clarity of messaging, channel crossings and cooperative communications with third parties. Also, learn several key software and cycle management tools that incorporate analytics and printed literature.
Presentation is about online macro environment and digital marketing environment. Further, market place analysis, SWOT analysis, online market place map, PESTLE analysis, digital economy defined, digital immigrants vs digital natives, innovation vs disruptive innovation, non existing businesses, etc.
A Framework for Digital Business TransformationCognizant
By embracing Code Halo thinking and a programmatic approach to business process change, organizations can better engage with customers and deliver mass-customized products and services that drive differentiation and outperformance.
Big Data Done Right by Successful OrganizationsEuro IT Group
Euro IT Group can help you unlock the tones of information already flowing through your organization, analyze it, extract value and transform it into insight that drives growth and revenue. Furthermore, by going through one of our big data quick wins programs, you will be able to enjoy the benefits of big data extremely fast, test and validate big data technologies and make better strategic decisions for managing your overall company data; our quick win program enables you to enjoy quickly new insights and measurable results by putting at work your existing data streams and to test and validate Big Data Technologies that can complement your legacy BI / DWH infrastructure.
Slides to the growth hackathon (code-free) the Kellogg alumni club just held in Palo Alto. We covered the Lean Canvas, getting to product-market fit, Aha! moment, growth marketing, and the analytics you should be focused on.
How is channel marketing evolving now that the value of consumer data is more...Grace Russell
The explosion of consumer data brings a lot of opportunity to the table, however with increased understanding of it's value, consumers want a lot more for it: individualised conversations, predictive personalisation, hyper convenience - and they want this across their preferred channels, on days and at times that suit them. They want a ‘customer-first’ value exchange, and if that’s not received they will find it somewhere else.
The business value of consumer analytics and big data is not just about what you can discover or infer about the consumer, but how you can use this insight promptly and effectively across multiple touchpoints (including e-Commerce systems and CRM) to create a powerful and truly personalized consumer experience.
For most organizations, mobilizing this kind of intelligence raises organizational challenges as well as technical ones.
This presentation reveals how some leading companies are starting to address these challenges, and describes the vital role of enterprise architecture in supporting such initiatives.
Direct to consumer is a major growth platform for any brand and retailer today. With the rise in e-commerce, companies are offering innovative solutions to help shoppers enjoy a less costly, more convenient, and most of all more satisfying shopping experience.
Digital Marketing Course Week 2: Introduction to Digital MarketingAyca Turhan
Second week slides of eMarketing Course at Hacettepe University taught by Ayca Turhan
Topics covered within the presentation include:
Digital Marketing vs. Traditional Marketing
Digital Marketing Strategies
For more please visit: www.aycaturhan.com/man423
The eCommerce Platforms in the Global Setup Divante
A feature-by-feature comparison of top-notch eCommerce platforms like Shopware 6, Magento 2, Spryker, commercetools, and Salesforce Commerce Cloud.
Selecting the right platform for the company’s global eCommerce is probably the most important decision at the early stage. Depending on the current and future needs, the selected eCommerce application will define your company’s direction and the elements required for fast development.
We compare five eCommerce solutions by analyzing six major aspects that should be considered when selecting a platform for global eCommerce. These functionalities were selected due to their impact on rollouts and future global eCommerce management, they include: country and language; structure and design; order and product management; customer and group management; pricing, taxes, and currencies; payments and shipping.
If you're interested in making your online store expansion efficient, read the ebook Global Rollouts for eCommerce: https://bit.ly/global_rollouts_for_ecommerce
It's a comprehensive book for CTOs, CMOs, and CDOs facing the challenge of global eCommerce rollouts: a practical guide to planning and expanding online stores using existing and emerging technologies.
The eCommerce Trends 2020 report is a comprehensive guide through emerging technologies in the world of online sales. It is based on the extensive research, run by Divante and Kantar, among over 250 eCommerce experts representing 8 countries. The report is complemented with trends rankings, implementation examples, and opinions from 20 independent experts.
Key learnings:
- Going Mobile for higher CR
- The future lies in AI
- Security is at stake
- Asia sets the new standard
- Technology natives win
and more!
Download full version: https://divante.com/ecommerce-trends
Async & Bulk REST API new possibilities of communication between systemsDivante
Async & Bulk REST API - new possibilities of communication between systems - Marek Borzęcki, Team Leader at Divante
Presentation originally presented at Magento Lightning Talks meetup on October 3rd, 2019, in Divante HQ.
Learn more at Divante at https://divante.com
Check out more Magento Lightning Talks at https://divante.com/blog/tag/magento-lightning-talks/
Magento Functional Testing Framework a way to seriously write automated tests...Divante
Magento Functional Testing Framework - a way to seriously write automated tests in your project? - Łukasz Adamczyk, QA at Divante
Presentation originally presented at Magento Lightning Talks meetup on October 3rd, 2019, in Divante HQ.
Learn more at Divante at https://divante.com
Check out more Magento Lightning Talks at https://divante.com/blog/tag/magento-lightning-talks/
Die Top 10 Progressive Web Apps in der ModernbrancheDivante
Wir Ihnen 10 Beispiele von Online-Shops aus der Modebranche, die Progressive Web Apps nutzen. Erfahren Sie mehr über deren Geschichten und die geschäftlichen Vorteile, die durch den Mobile-First-Ansatz und Progressive Web Apps erzielt werden.
How to reduce customer churn?
There are many ways to do that. First of all, customers should trust you. Sound easy but in the end, it’s extremely hard for a brand to gain trust. The market is very competitive and customers requirements are constantly growing. If you interesting about customer churn and how to stop it, we created a report about it.
Our new report is here!
eCommerce Trends is our annual report, we've been doing for 6 years. This year we reached out to 10k eCommerce Managers and asked them to fill in our survey. Of those asked, 150 responded and we created this report based on the results.
What's inside?
Market Overview
B2C eCommerce Sales
Top eRetailers (Based on Revenue)
Top eCommerce Sites (Based on Traffic)
Capital Market
eCommerce Startups to Watch
Survey Results
eCommerce Investments 2018
eCommerce Investments 2019
Changes in Investments 2018-2019
Which Investments Had the Biggest ROI in 2018?
Most Impactful eCommerce Trends for the Next 5 years
Trends Matrix
Inspirations
Marketplaces: Better Prices, Free or Discounted Shipping and a Broader Selection
Voice Interfaces
Big Data & Business Intelligence: IoT as a Source
Chatbots: Growing Popularity
Microservices & Headless: Micro Frontends
Content Marketing & Digital Content
CRM: AI & Social CRM
Mobile CRO
Free Shipping/Returns
Mobile Approach: PWA
Personalization: Hyper-Personalization
Social Commerce: See. Tap. Shop.
Customer Lifetime Value and Referral Programs
Methodology
Research Methodology
Demography
TL;DR - Quick Summary of the Report
Content Marketing: Still Important
Personalization & Loyalty for the Win
Cryptocurrencies and the GDPR: Things of the Past
Content Marketing With the Biggest ROI
Voice Interfaces & Big Data Revolutionize eCommerce
Quick Wins vs. Highest ROI
What do experts say?
Download full version: https://go.divante.co/ecommerce-trends-2019/
How to successfully onboard end-clients to a B2B Platform - Magento Imagine ...Divante
Magento Imagine 2018 Presentation
Developing eCommerce since 2004, I‘ve seen how successful B2B clients digitize their businesses and how they onboard users
eCommerce trends from 2017 to 2018 by Divante.coDivante
We asked managers about their eCommerce investments last year and about investment plans for this year.
What are the most important trends, game changers and quick wins.
Download full version from: http://go.divante.co/ecommerce-trends-2018/
Trends we asked about:
Content Marketing
Big Data
Business Intelligence
Email Automation
Chatbots (Conversational Commerce)
Digital Content
Social Commerce
Cloud Hosting
Multimedia Content – better photos/…
Marketplaces
Omnichannel
Personalization
In-Store PickUp
Predictive Analytics
General Data Protection Regulation…
Automated Pricing Optimization
Automated Promotion Management
Free Shipping / Returns
In-Store Digital Touchpoints
Mobile App
Same Day Delivery
Drop Shipping
Order Management Systems
Other AI based tools
Own Brands
Personal Shopping
Price Intelligence
Progressive Web App (PWA)
Programmatic Ad Buying
Recommendation Systems
Customized Products
Data-driven Loyalty
Microservices Architecture
Online Security
Wearables
Algorithmic Driven Supply & Demand
Cross Border Commerce
Cryptocurrencies (Bitcoin / Ethereum)
mPayments
Open API Economy
Security
Subscription Business Models
Visual Search
Beacon Technology
Headless Approach
Serverless Architecture
IoT
Push Notification
Virtual Reality
Alexa and other voice interfaces
Drones
3D printing
vue-storefront - PWA eCommerce for Magento2 MM17NYC presentationDivante
Vue.js, mobile first, offline second eCommerce frontend, we're developing under MIT - http://vuestorefront.io. Become a contributor today - https://github.com/DivanteLtd/vue-storefront
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
1. Big Data in e-Commerce.
How to Use the Power
of Data in E-Commerce?
Tom Karwatka
2. Monitoring E-Commerce Today
• NC (new customer);
• RC (retained customer);
• ROI (return on investment);
• CLV (customer lifetime value);
• ROI CLV;
• RR (return rate);
• CR (conversion rate);
• CPO (cost per order);
• CPNC (cost per new customer);
• CPRC (cost per retained
customer);
Today, the majority of the e-commerce world monitors the following
indexes:
3. Sources of Data in E-Commerce
• E-commerce
Orders
Products
Baskets
Visits
Users
Marketing campaigns
Referring links
Keywords
Catalogues browsing
• Social data
FB
Twitter
Google
• Cookies /
reMarketing / MA
• Google Analytics
• … and many others
4. The Choice of Data Source in Traditional Retail Is Even Greater
Source: http://www.slideshare.net/MarketResearchReports/big-data-1
Already in 2012 the Walmart transaction database was estimated to have 2.5
petabyte of customer data.
5. Questions the Analytics Can Answer
• What are the best sellers in a category?
• Is the most watched product at the same time the best selling one?
• Which products sell best among the users who have already bought an item in
the product category?
• How often does a given user group (eg., new users) return to your shop?
• …
The problem is, however, that answering these questions does not lead directly
to a bigger profit.
Companies often get discouraged as the answers are difficult to apply in real
life.
6. The Actionable Data
• Collaborative filtering
• Using the information on users'
actions to automatically find
the correlations between:
Elements on a website
A keyword and the link chosen
• Recommendations
Products
Offers
• Classification
Users who continue shopping
Applying the Big Data solutions makes it possible to analyse data in real time. This
allows us to use the data not for reports only, but to translate them into action –
usually personalized and in real time.
• Regression
Indicating trends or the lack of
trends
Predicting stocks
Anticipating a product's future
popularity
Anticipating the future popularity
of promotions
Assessing the effect of marketing
activities on sales or the number
of users
• Categorization and
segmentation
Customers
Products
7. Example: Actionable Data
If, thanks to Big Data, we can find the correlation between the social
media and our system data, then taking into account that:
40% users purchased a product after liking or sharing it on social media
71% users of social media buy mainly based on recommendations
We can prepare shopping recommendations for specific customers,
based on their social media behavior.
9. Example: CREDEM Banca
• Predicting what products and
services will a customer like
• Increasing an average revenue on a
customer by 22%
• Marketing costs reducted by 9%
Source: http://www.slideshare.net/Dell/big-data-use-cases-36019892?related=1
10. Example: STARBUCKS
• Collecting the data about the
customers' orders
• Personalizing adverts
• Personalizing vouchers
• Selecting the customers losing their
interest in the offer
• Recovering lost customers
Source: http://www.slideshare.net/Dell/big-data-use-cases-36019892?related=1
11. Example: NORDSTROM
• Aggregating data from www pages,
social media, transactions, loyalty
program.
• Choosing a message based on the
customer's preferred
communication channel.
Source: http://www.slideshare.net/Dell/big-data-use-cases-36019892?related=1
12. Example: EasySize
Analyzing orders and returns – using the findings to decide which
sizes in different brands would fit a given person.
Source: http://easysize.me
14. Example: Promotional Activity of Brands
The Kizzu app is available on
iPhone and Android. Over
10.000 users enjoy the app. It
gives the information on
current promotions in the
users’ shopping malls.
• Using a consumer mobile app,
we collected the information on
the special offers in shopping
malls that customers find
attractive.
• The data let us answer the
questions:
Which brands have the highest
promotional activity?
Which special offers are the most
effective?
15. Example: Promotional Activity of Brands
The free-of charge magazine for the
customers of Deichmann is published
twice a year - in spring and fall. It
shows the latest fashion trends - very
popular online
• Among the most popular special offers,
we found also some less popular, niche
brands.
Internet / Mobile gives them opportunity to compete
against strong brands for the customers' attention.
They attract customers, offering big discounts.
• Among the most popular special offers
there are frequently content based
promotion activities (a promotional
newsletter or a magazine).
• Activities targeting the most loyal
customers are also popular.
• The number of promotional activities
does not depend on the status of a brand.
Our TOP 50 includes also some of the brands
positioned as premium ones. Their customers
apparently expect a frequent interaction with the
brand.
16. Future: Big Data & Design
• Continuing to use Big Data
together with the automation
of the layout creation
- Responsive-web design
- Font-end frameworks
• Creating user-customized
layouts
• Case study:
https://www.behance.net/gallery/22
089487/Tchibo-Content-Automation-
Platform
Source: https://www.behance.net/gallery/22089487/Tchibo-
Content-Automation-Platform
17. Future: Big Data & Machine Learning
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developer-cloud-enterprise.html
Three days in and we’re already acting like it’s been here forever. (…) Alexa can maintain two lists for
you: To-do and Shopping List. Adding things is as simple as ”Add butter to shopping list” and „addng
gutters to to-do list.” (…) Once you’ve added things to your list, you access them through the app.
One great thing is that everyone in your household who installs the app shares everything. So when I
was at the store, my wife texted me that she’d put some things on the Echo shopping list. Sure
enough, I opened my app and there it was. I could check off the things I got and they disappeared.
http://www.engadget.com/products/amazon/echo/reviews/14cw/
•IBM Watson - Developer Cloud Enterprise
Medical diagnostics support
Legal consultations
•Google
Google Now – the first apps for eBay
DeepMind
•Siri, Cortana, Amazon Echo
Amazon Echo already makes it possible to create
shopping lists, among others
18. Future: Big Data & Machine Learning
• The assistant will deduct the products we are
about to need from a number of data, and will
order them autonomously.
• As far as the mass products go, the competition
will become more and more difficult.
• The promotion of FMCG as we know it will stop
being recognizable by the customers.
• The companies controlling e-assistants will
become the biggest shopping portals.
• Basic competitive advantage will grow in
importance – the product's availability,
competitive price, and swift logistics.
• Internet will become just another layer of
technology – little interesting for an average
user.
Source: https://itunes.apple.com/us/app/fetch-personal-buying-assistant/id867636554
19. Future: Big Data & Machine Learning
• Right now, it is worth to develop new mechanisms for data
exchange and offer creation automation.
• It is also worth to expand your own client databases, so as to
keep in direct touch with your customers as long as possible.
• Owned Media!
Source: https://itunes.apple.com/us/app/fetch-personal-buying-assistant/id867636554
20. Thank You for the Attention
• Are you interested in Big
Data?
• Let's talk!
20
Tom Karwatka
http://divante.co
tkarwatka@divante.co