The document discusses the utilization of big data in e-commerce, highlighting key metrics such as customer acquisition, retention, and return on investment. It emphasizes the importance of real-time data analysis for actionable insights, as well as providing examples of companies that have successfully implemented big data strategies to enhance customer engagement and personalization. The future of e-commerce is portrayed as increasingly reliant on machine learning and automation to improve the shopping experience and optimize marketing efforts.

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.

8. Example: T-Mobile
Source: http://www.slideshare.net/Dell/big-data-use-cases-36019892?related=1
• Billings, social media data
• Selecting clients for migration to
premium models
• Detecting clients with high Lifetime
Customer Value

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

13. Example: EasySize
Results: decrease in returns by 35-40%
easysize.me
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