Big Data Analytics in Marketing

1. Barry Ooi
Managing Director,
Potentiate Sdn Bhd
BIG DATA ANALYTICS
IN MARKETING
1
29th Januray 2015

2.  What is big data
 How big data is used in marketing
 Cases on successful use of big data & analytics
•Snacks • Retailing • Food
 Components involve in big data
 How do you get started
 demo on some of the work which Potentiate
has been doing on data integration &
visualisation
FLOW OF PRESENTATION
2

3. PRELUDE TO BIG DATA & ANALYTICS
3
Collects Evidence
Gathering all the pieces together;
murder weapon, body fluids, hair & fiber, blood
sample, foot print, finger print, powder, chemicals,
location, vehicle
Sports drama screened in 2011 about a baseball
team, Oakland Athletic's, ending their 2001 season
with a loss. Lost 3 star players and under funded
It took a statistical approach towards scouting and
analysing players.
Its GM Billy, played by Brad Pitt, meets Peter Brand,
a quiet Yale Economics graduate, hires him to help
picked undervalued players for what they can do

4. SO, WHAT DOES BIG DATA LOOK LIKE?
4
1. data warehouse & processing?
power?
2. social media data and over the internet?
3. data from multiple sources?

5. 5
Big data, it’s characterised by :
VOLUME
VARIETY
VELOCITY
VERACITY
4V’s
Note: Gartner Analyst, Doug Laney, came up with famous 3Vs back in 2001

6. 6
The 4V’s
VOLUME
VARIETY
VELOCITY
VERACITY
• huge data sizes
• terabytes, petabytes & zetabytes
• various data sources
• social, mobile, M2M, structured,
unstructured
• high speed of data flow, change and
processing
• various levels of data uncertainty and
reliability and model approximation
sensor
data
imagesdatabase
location
email
click
stream
social
html

7. DATA SIZE & HIERARCHY EXPLAINED – rough guide
7
Unit Size Indications
• Byte A single letter
• Kilobyte One page of typed text is 2KB
• Megabyte A typical pop song is about 4MB
• Gigabyte A 2 hour film can be compressed into 1 - 2GB
• Terabyte Big enough to hold all the x_-ray files in a modern hospital
• Petabyte
Big enough to hold 13 years' worth of high-definition TV content.
Google processes 1 PB every hour
• Exabyte Equivalent to 10 billion copies of the Economist
• Zettabyte
If we’re able to record every human word that has ever been spoken
they would fill up about 42 zettabytes worth of memory
• Yottabyte Too big to imagine

8. A ZETTABYTE SIZE IN NUMBERS!
8

9. 9
HOW DID DATA GOT SO BIG?
Computation Power
• Moore’s Law; the number of transistors in a
integrated circuit doubles approximately every 2
years
Proliferation of Internet Enabled Devices
• M2M, vehicle with navigational controls, Web
cams
Falling Costs of Data Storage
• cloud computing and storage

10. • Many marketers may feel like data has always been big
• But there’s a contrast in terms of data source and speed to access
BIG DATA & MARKETING
10
THEN NOW
 Purchase records
 Point of sale transaction
 Responses to direct mail
campaigns
 Coupon redemption
 Customer careline
 Online purchase data
 Click through rates
 Browsing behaviour
 Social media interactions
 Mobile devices usage
 Geo-location

11. BIG DATA in MARKETING – SUCCESSFUL CASE
11 ref: https://www.youtube.com/watch?v=lhCz3WDdo94
Hippo Snack Foods (India)
• use social media data to improve its supply chain management operations
• it has to compete with very powerful big box and supermarket retailers in the
pipelining of its products esp. new items confronted with a very diffuse
distribution channel and thousands of small retailers
• introduced Twitter feed that allows its consumers to provide direct feedback on product availability
• Hippo asked its followers on Twitter to send tweet whenever they couldn't find hippo in stores and
promised to replenish stocks within hours
• Tweets were pouring in from over 45 cities. People were tweeting from various store types
• Hippo set up a core cell which instantly passed the information to the sales and distribution teams
• this enable Hippo to identify empty racks faster and restock locations within hours and gave regular
updates on stock replenishment to the people
• Hippo even sent out personalized anti hunger hampers to the most active tweeters, complete with a
handwritten note
• as people saw Hippo packs reappearing in stores on demand, the Hippo brand became a cult!
• it’s number of follower on the internet began to grow; blogs and articles began to pop up all over
• demand is now matching with supply, Hippo managed to grow its sales by 60%.

12. • In 1995, Tesco introduced the Tesco Clubcard in response to the
numerous challenges to its existing business model & competition
TESCO & DATA ANALYTICS (1)
12
• The value of this clubcard is just not about offering deals but in the shoppers’ data buying
behaviour
• Through this programme, Tesco was able to receive detailed data on two-thirds of all
shopping baskets resulting in an avalanche of data
• Tesco outsourced the analysis to Dunnhumby, a company they would later buy a majority
stake in
• The first analytical step was to segment the customers into appropriate groups. That
resulted in:
more targeted mailings of vouchers and coupons, rate of redemption for coupons shot
up from 3% to 70%
it also launch new product lines according to customer demands
upmarket customers were targeted through product lines such as ‘Tesco Finest’
health-conscious customers could now buy ‘Tesco Healthy Living’
‘Tesco Value’ aimed to entice the price-sensitive among Tesco’s customers

13. 13
• The mailings became more complex also. As they obtain more insights into its shopper buying pattern and
behaviour, it had over 145,000 mailing variations
• Having broken down customers into segments, Tesco increased its reach by launching the Clubcard Plus,
an an integrated debit card
• This was later replaced by a credit card but nonetheless influence customers into spending more
• Using all this data Tesco started trying to convert the non-buyers
 example, finding that recent parents were spending their money elsewhere, they launched a Baby club
and ended up capturing 24% of the baby market.
• Seeing that its analytics approach worked, Tesco started applying it to other fields also
 an example is its optimized stock keeping system which forecasts sales by product for each store
based on historical sales and weather pattern data
 through predictive analytics Tesco managed to save £100m in stock that would have otherwise expired
 management of the fridge and store temperatures which enabled significant savings in energy costs.
• Using the insights it gained from the collected data Tesco evolved from a retailer that thought it knew what
the customers wanted into one that actually did know and could monitor the preferences as they changed
over time
TESCO & DATA ANALYTICS (2)

14. • Food Genius is a company that collects and integrates massive amounts of
restaurant industry data, from menu descriptions and item prices and creates
actionable insights and its delivered to clients through a variety of platforms; web,
mobile, portal
BIG DATA in BUSINESS
14
• They're tracking and trying to understand what motivates customers interest and ever
changing tastes around the dishes that we eat. How hard can that be!
• What Food Genius did was, rather than take the approach that traditional food marketers have
taken, which is to look at current consumption rather after the fact
• They try to determine this based on the actual decision that was taking place ie at the
restaurant itself
• It worked with restaurants across the U.S and build a dashboard reporting application that will
compile and serve up incredible amount of data captured in order to make sense of it
• It involves tracking customer dining choices at over 360,000 different restaurant locations
offering over a hundred and ten thousand unique menus. Together, they were able to report
on an amazing 16.3 million different individual menu items

15. BIG DATA in BUSINESS
15
• The data that Food Genius is being able to generate through this incredible commercial
exercise is providing food marketers with insights that they've never had before in real-time.
• So for example, food processors can start to plan product based on the hot new trending
ingredient items. Restaurant chains can pick out those winning new meals that are going to
pop up on the menus over the next few months and even food retailers will get some insight
on how to promote and package and position within their stores and what hot new products to
introduce
• The marketing and sales strategies of big food companies, the big restaurant chains and the
big food retailers are really super turbocharged by this kind of information and you can see the
impact it's going to have on their strategy's going into the future. This is a far cry from the kind
of data that the same types of marketers were able to use just a few years ago
• It's a real example of the kind of opportunities and vertical areas that companies like Food
Genius are seizing on by helping those companies close the complexity gaps in each of their
own market place
Ref: https://www.youtube.com/watch?v=VL4JcU3C2yg

16. WHAT’S THE VALUE OF BIG DATA
16
• This represents about 60% of American adults
• The retailing giant collects information on: what shoppers buy, where they live, what they like
• Information on shoppers allows Walmart and more than 50 third-party sites to profile customers accroding to
their demograpics and to target people for specific products and deals
• In order to compile its figure, the report's authors used a Walmart statement in which the company's CEO
said that 60% of Americans shop at the store each month. The authors then used U.S. adult population
estimates to arrive at the figure
• Walmart E-commerce Spokesman Dan Toporek disputed some of the report's findings, noting that most of
Walmart's stores aren't Wi-Fi-enabled and that the company doesn't track shoppers in stores. He said the
company takes pains to protect customer privacy and mostly uses customer data and passes it along to
third-party sites in aggregate rather than individually.
"We use that to deliver a better customer experience," Toporek said. "Third parties don't see that individual
information.”

17. WHAT’S INOVOLVED IN BIG DATA ANALYTICS
17 Source: Forbes; Big Data Hiring Trends, December 2014
?

18. MORE THAN HARDWARE & SOFTWARE …….
18
network and computer system administrators
systems engineers and architects
software developers
software engineers
web developers
web designers
data analysts/statisticians/ modellers
market researchers
business analysts
marketing managers
The 5
HATS
16 Top Big Data Analytics Platforms
www.informationweek.com/big-data/big-data-analytics/16-top-big-data-analytics-platforms/d/d-id/1113609

20. POTENTIATE’S APPROACH TO DATA INTEGRATION

25. THANK YOU.
WWW.POTENTIATEGLOBAL.COM
Barry Ooi
Managing Director
012 210 2018
barry.ooi@potentiateglobal.com