Using big data to turn shoppers into big spenders Applying the range, volume and velocity of retail data Advanced modelling, forecasting and segmentation of data Cultivating and retaining loyal customers using internal and external data Optimising analytics in merchandising and demand forecasting Increasing store level profitability and competitiveness Using analytics to detect retail fraud

1. Hani Saif, CIO eXtra
@hanisaif
hanisaif

3. 90% of the data in the
world today was created
in the last two years .
US healthcare can save $300B yearly if they
leverage big data
2016 :6 Million job will be
created to serve new data
(2 M IT and the rest non IT)
The total volume of data that the industry
captures and stores, gets doubled in every
1.2 years.

4. Source: GSMA_Global_Mobile_Economy_Report_2015

5. For a typical Fortune 1000
company, just a 10%
increase in data accessibility
will result in more than $65
million additional net
income.
Within five years there will be over 50 billion
smart connected devices in the world, all
developed to collect, analyze and share data.
Retailers who leverage the
full power of big data could
increase their operating
margins by as much as 60%.

9. What is the Big data
voluminous amount of
structured, unstructured and
semi-structured data

11. Three Characteristics of Big Data 3Vs
Volume
Velocity
Variety
Data at scale
Teradbytes to
Petabytes
“facebook 25TB
of data daily”
The speed at which new
data is generated
“500 million tweets daily”
Data is usually in various
formats, structure and
unstructured
Audio file ,Video file
“Text messages , location
coordinates ..etc”

12. Traditional data vs Big Data
Traditional Data
• Control on data creation
• Manageable volume
• Structured ,RDBMS is the right tool
• Query Language can be used
Big Data
• Uncontrolled creation of data
• Very large volumes
• Unstructured, need different tool to manage
• Query language cannot be used

13. Where does these data go ?
Big Data
Repository
Data
Processing
Business Analytics Software
Actionable
Data
Demand Forecasting
Inventory Forecasting
Trend Forecasting
Dynamic Pricing
Planning Product
Promotion
Timely Offers

14. How does big data works
Identifies the Prospect
via Smartphone Etc.
Feeds the Profile of Prospect
into Real-Time Expert
System
Big Data
Repository
Analysis
Processing

15. 25% off on
bundle book
purchase
New book
Arrived on
author “X”
Gift Coupon
On birthday
35% off on
Pre-Holiday
Season

16. How to Process the
data for Retailer?

17. “ The worst form of
inequality is to try to make
unequal things equal ”
Aristotle

19. Customer segmentation is the practice of classifying your
customer base into distinct groups. This separation of
customers into unique groups is often based on
multidimensional customer information such as; observed
customer purchase and product usage behavior, demographic
and lifestage characteristics, or even self-reported product /
service preferences, needs and attitudes
Definition and Application
Source: IKNOWTION

20. Predictive modeling is the practice of forecasting future
customer behaviors and propensities and assigning a score or
ranking to each customer that depicts their anticipated actions.
As is the case in segmentation, multi-dimensional customer
information is leveraged to identify and isolate customer
characteristics that are highly correlated with targeted
outcomes.
Definition and Application
Source: IKNOWTION

21. This Lead to…… Retain Customers ..make them Loyal
Better marketing effectiveness
Having the right assortment
Better Inventory Optimization
Maximize the Sales
Maximize the Profit
Fraud detection

22. Big Data is
an IT Decision
A Business Decision

23. Sponsorship
Source: IBM

24. Don’t forget to
measure the