A summarized view of a Research and Development (R&D) project done with Tata Consultancy Services (TCS)

1. INTERNSHIP ON
“ TAILORING NEXT BEST
OFFERS (NBOs) USING REAL
TIME BIGDATA ANALYTICS
(RTDBA) “
with

2. A RESEARCH & DEVELOPMENT
(RND) PROJECT UNDER THE ABLE
GUIDANCE OF :
MR. SATISH KUMAR (TCS IT Analyst)
&
MR. RAHUL SHARMA (Prof. IT Dept.
AKGEC)

3. ABOUT THE ORGANISATION
• Founded – 1968
• Founder - J. R. D. Tata, F. C. Kohli.
• Indian Multinational information technology
(IT) service, consulting and business solutions.
• Headquartered in Mumbai, Maharashtra.
• It is a subsidiary of the Tata Group and
operates in 46 countries.
• World's 10th largest IT services provider.

4. • Listed in Fortune 500 company.
• TCS is one of the largest private sector employers in
India.
• In 2006, it designed an ERP system for the Indian
Railway Catering and Tourism Corporation.
• Revenue: US$16.54 billion (2016)
• Number of employees: 3,62,079 (June 2016)
• Services: IT, business consulting and outsourcing.
• Total assets: US$13.76 billion (2016)

5. TCS NOIDA, Sec-62

6. TATA GROUP

9. About the Project
• Customers once relied on a familiar
salesperson to get the most customized deal.
• Today’s distracted consumers, bombarded
with information and options, often struggle
to find the products or services that will best
meet their needs.
• Poorly informed floor staff at many retailing
sites can’t replicate the personal touch that
shoppers once depended on.

10. • With the above view keeping in mind the
phenomenon of Customer Relationship
management (CRM) has largely come into
play.
• We analyzed a conceptual framework for
customer relationship management (CRM)
that helps broaden the understanding of CRM
and its role in enhancing customer value.
• Using granular data, from detailed
demographics and psychographics to
consumers’ click streams on the web.

11. • Right merchandise at the right moment, at the
right price and in the right channel known as the
Next Best Offers (NBOs).
• Considering Microsoft’s success with e-mail
offers for its search engine ‘Bing’. Those e-mails
are tailored to the recipient at the moment
they’re opened.
• In 200 milliseconds—analytics software
assembles an offer based on real-time
information about him or her that includes
location, age, gender, and online activity both
historical and immediately preceding, along with
the most recent responses of other customers.

12. • Consider Facebook, which maps consumer
habits on a real-time basis, 24 X 7.
• Every time a Facebook member posts a
comment, photo or event, or responds to a
comment, photo or event, Facebook logs that
data.
• In that manner, Facebook analysts determine
in a millisecond what users want to see, and
what they are interested in doing.

13. • How about eBay, and its Hunch-driven
recommendation service?
• The online retailer leverages Hunch’s “taste
graph” software-based consumer searches
and responses to advertising, to push
recommendations toward items based on
their individual shopping preferences.

14. • NBO has its origins in Amazon’s early use of
so-called recommendation software to spur
shoppers toward a “next best offer” based on
site page visits and, of course, actual
purchases.

15. • “Next best offer” is increasingly used to refer
to a proposal customized on the basis of the
consumer’s attributes and behaviors
(demographics, shopping history , etc).
• NBOs are most often designed to inspire a
purchase, drive loyalty, or both.

16. • The effectiveness of NBO’s can be seen by the
software company SAS’s statement - that
deployment of next best offer technology and
processes “is essential for gaining sustainable
competitive advantage and achieving
response rates as much as 10 times greater
than standard outbound promotions.”

17. • Many organizations flounder in their NBO
efforts not because they lack analytics
capability but because they lack clear
objectives.
• So the first question is, what do you want to
achieve? Increased revenues? Increased
customer loyalty? A greater share of wallet?
New customers?

18. • UK-based retailer Tesco has focused its NBO
strategy on increasing sales to regular
customers and enhancing loyalty with
targeted coupon offers delivered through its
Club card program.
• Tesco uses Club card to track which stores
customers visit, what they buy, and how they
pay.

19. • For example, Club card shoppers who buy
diapers for the first time at a Tesco store are
mailed coupons not only for baby wipes and
toys but also for beer.
• (Data analysis revealed that new fathers tend
to buy more beer, because they are spending
less time at the pub.)

20. • More recently, Tesco has experimented with
“flash sales” that as much as triple the
redemption value of certain Club card
coupons.
• A countdown mechanism shows how quickly
time or products are running out, building
tension and driving responses.
• Some of these offers have sold out in 90
minutes.

21. • As a result of its carefully crafted, creatively
executed offers, Tesco and its in-house consultant
achieve redemption rates ranging from 8% to
14% -- far higher than the 1% or 2% seen
elsewhere in the grocery industry.
• Microsoft had a very different set of objectives
for its Bing NBO:
• Getting new customers to try the service,
• Download it to their smart phones,
• Install the Bing search bar in their browsers &
• Make it their default search engine.

22. • Walmart.com purchases on the basis of their
social media interests.
• The apparel retailer H&M has partnered with
the online game “MyTown” to gather and use
information on customer location.
• If potential customers are playing the game on
a mobile device near an H&M store and check
in, H&M rewards them with virtual clothing
and points.

23. • Early results show that of 700,000 customers
who checked in online, 300,000 went into the
store and scanned an item.
• Many retailers focus on how to use
customer’s location information in real time;
where the customers have been can also
reveal a lot about them. In the United States
alone, mobile devices send about 600 billion
geospatially tagged data feeds.

24. • Technologies such as Hadoop and MapReduce
will be needed to integrate to existing
architecture.
• Appliances that integrate servers, networking
and storage into a single enclosure to run
analytical engines for near-real time
extraction of insights and information are
becoming popular.

25. • Real-time big data analytics (RTBDA) is a
ticket to improved sales, higher profits and
lower marketing costs.
• To others, it signals the dawn of a new era in
which machines begin to think and respond
more like humans.
• We aim at making computer systems more
close to the buyers in giving them those
desired choices, better called as “tailored
choices”.

26. • For an example, Twitter uses Storm to
identify trends in near real time.
• Let’s say that someone tweets that he’s going
snowboarding.
• Storm would help you figure out which ad
would be most appropriate for that person,
at just the right time.

29. REFERENCES
• Harvard Business Review- “ Know What Your Customers
Want before They Do” - by Thomas H. Davenport, Leandro
Dalle Mule, and John Lucker.
• “ Next Best Offer: Customer-Based Predictive Data’s New
Frontier ” by-Daphna Gal.
• www.cooladata.com/blog/next-best-offer-customer-based-
predictive-data-new-frontier.
• “The elements of data analytic style" by- Jeff Leek.
• “Hadoop-The Definitive Guide” by- Tom White.
• “Mining of massive data sets” by- Jure Leskovec, Anand
Rajaraman, & Jeff Ullman.
• Next-Best-Action- ‘The One to One Future’ by K.R. Sanjiv,
WiproTechnologies.

30. Thank you !
A presentation by:
Shubham Agarwal
(IT- 4th year)
(1302713097)