This document discusses big data analytics and its impact on e-commerce. It begins with background on how data analysis motivates human actions and helps businesses understand customer expectations. It then defines big data as the collection of traditional and digital data used to discover insights. The document outlines how e-commerce businesses can apply big data analytics to identify customer segments, make recommendations, optimize operations, and contact customers at the right time. It also discusses the impact of big data on increasing sales and margins. Finally, it covers methods used in data analysis, benefits of big data for e-commerce, challenges faced in the author's experience with e-commerce projects, and future challenges around privacy and costs.

1. Big Data Analytics and its
Impact in E-Commerce
Presented by:
Ankita Tiwari
(Research Scholar)
Department of Electronics and Electrical Engineering
IIT Guwahati

2. Content
Background
Big Data: A Buzzword
Application of Big Data Analytics in E-Commerce
Impact of Big Data on E-Commerce
Steps of Data Analytics in E-Commerce
Methods used in Data Science
Algorithms used for Big Data Analysis
Benefits of using Big Data in E-Commerce
My Experience
Future Challenges and Drawbacks

3. Background
 Almost every action in human life, its online or offline, is
motivated by the some kind of data analysis.

4. Contd..
 Understanding the situation (i.e. based on data analysis) in
a more precise and perfect way helps you to achieve best
of your goals.
 When it comes to a business, weather it is online or offline,
you are required to be at one step ahead what customers
are thinking or expecting.

5. Contd..
 The day by day increasing technologies in data collection and analysis
domain are making the life of merchants more and more easier.
 However it is not as simple as you are thinking, due the advanced data
collection methods, technology has expanded to the point where we have
“huge amount of” data. Organizing, studying and understanding this
information has become even more complicated because we’re inundated
with endless numbers, facts, percentages and perceptions.

6. Big Data: A Buzzword
 To deal with the problem of huge data present
around us we have an approach called Big Data
Analytics
 Big data has been a buzzword floating around the
digital space for a few years now.

7. So, what exactly is big data
 Basically, Big data is the combined collection of traditional and digital
data from inside and outside of an environment/company.
 The main purpose of Its collection is to be a source of analysis and
continued discovery.
 Particularly the introduction of big data has allowed businesses to have
access to significantly larger amounts of data, all combined and packaged
for analysis.
 This data offers insight for e-commerce businesses. E-commerce business
owners can take the information from big data and use it to study trends
that will help them to gain more customers and streamline operations for
success.

8. Application of Big Data In E-Commerce
 To identify the specific customer who are likely to buy specific products and
anticipated demand using their social media activity, browsing patterns,
their purchase history, blogs and forums activity, your customer data,
demographic data, weather data, geopolitical situation, etc.
 Recommendations, advertisements or real time offers based on advanced
customer segmentation and shopping patterns, thus influencing purchase
decisions and up-sell
 Smart shopping experience, smart merchandising and marketing Predictive
analytic that enable you to optimize pricing, inventory levels, check your
competition pricing, predict the hot items of the season, improve your
customer service, increase customer satisfaction and your margins

9. Contd..
 Contact your customers and prospects when they are ready
to buy, on their preferred channel (social media, SMS,
email) in the most appropriate location (driving, at work,
at the mall)
 Concrete use case:
you arrive at the local mall → you check in via Facebook →
within a matter of minutes, you get 30% coupon for
purchases today at X retailer via email or SMS

10. Impact of Big Data on E-Commerce
 Approximately 80% higher sales for companies which use Predictive
Analytic than those who have never done it.
 About 70% of customers feel completely frustrated and annoyed when
you’re sending them incoherent offers and experiences through
different channels.
 An estimated 60% increase in business margins and a 1% improvement
in labour productivity for retailers who started using Big Data
(McKinsey)
 About half of online shoppers are more likely to shop on a site that
offers personalized recommendation (invesp Consulting)

11. Steps of Data Analysis in E-Commerce

12. Methods used for Data Analysis
 Classification
 Classification is the creation of classes that represent users and use cases. Class
Probability Estimation tries to predict how to classify each single individual data
asset. Based on the specific question to be answered, classes are created.
 Regression
 Regression can be confused with classification methods because the process of
using known values to predict an outcome is the same. But Regression is the
method of trying to predict a numerical value for a particular variable for that
individual data asset.
 Similarity Matching
 Similarity Matching, looks for correlation of attributes.

13. Benefits of Using Big Data E-Commerce
 It helps ecommerce companies to develop product portfolio
 A better pricing experience may be offered
 online/in store experience can be improved via virtual reality
 Advertising and marketing budget may be reduced
 Assurance in the secure payments/transaction can be increased and
made easier.
 Overall customer satisfaction may be improved
 Companies also have a increased shopper analysis.

14. MY Experiences

16. E-Magazine

18. E-Shopping Window

20. Major issues faced during E-Commerce experience
 Finding the right products/content to sell/display for a person visits the
e-platform.
 Provide optimised pricing.
 To attract the perfect customer using personalized display of
products/content.
 To have a predictive measurement of traffic on ecommerce site.
 To promote their offers to customers using appropriate and
personalized manner when they are away from ecommerce site.
 Nurturing the ideal prospects.
 Converting shoppers in to paying customers.
 Retaining Customers.

21. How these issues are related to Big Data
Ø To display the right content/product to a person needs to analyse
the information related to their habits, current situations, their responses
in such situations etc. That require a lot of data to be collected and
analyse.
Ø For predictive measurement of traffic can be estimated by analysing
the behaviour of customers, like at what time generally they tends to
the shopping or visits the ecommerce site, and hence peak hours can
be decided.
Ø We need to know that what is the best possible way is to communicate
the offers/Deals and hence should be aware that, at what time which of
the communication method is suitable. Therefore it is require to have the
data related to the usage of e-mail, WhatsApp, phone etc with
accurate timings.

22. Contd..
Ø To prepare a more effective prospects related to their products, we need to
have data related to the a likings of a customer, their needs, budgets etc
and their expectations for a particular product, hence having big data can
play a crucial role.
Ø By the shopping experiences of the customers we can know have an
about their level of satisfaction, to have this kind of data is very important
in retaining the customers who visits the ecommerce site.

23. Role of Big Data in solving these issues
Ø Provides the data related to the customers by accessing their social media
accounts, browsing patterns, shopping experiences etc.
Ø Helps in identifying the relevant data and develop a trend for a particular
customer, also the prices for a product can be compared and made optimised.
Ø Helps Retailers to figure out where their audience is and how to attract them
efficiently without killing their marketing budget using the analysis of their
activities and financial status via developing a predictive model based on the
data analytics.
Ø Analysis of the feedback and past shopping experiences helps ecommerce
sites to understand their further expectations and preparing offers based on
that can attract the new and retain the existing customer in more effective
manner.

24. Future Challenges and Drawbacks
 1 of 3 Internet users say they have stopped using a company's website or have
stopped doing business with a company altogether because of privacy concerns
(2013 Truste study)
 Some real life scenarios that involved privacy concerns: –
Nordstrom: customer privacy concerns after using sensors from an analytics
vendor to get shopping information from customers' smartphones each time they
connected to a store's Wi-Fi service – due to widespread criticism from privacy
advocates, Nordstrom is no longer using the service
Urban Outfitters hip clothing retailer is facing a lawsuit for allegedly violating
consumer protection laws; they told shoppers who pay by credit card that they had
to provide their ZIP codes. The purpose was to obtain the shopper’s address.

25. Contd..
Facebook is often at the centre of a data privacy controversy, whether
it's defending its own enigmatic privacy policies or responding to
reports that it gave private user data to the National Security Agency
(NSA).
 Other Drawbacks may include
Cybersecurity Risks.
Difficulty integrating legacy systems.
Manpower's and IT Resources that may increases cost as well.
Data security and quality maintaining costs.

26. THANK YOU!