Big Data What’s the big deal?

2

Big data is the exponential growth and availability of data – too large, complex and dynamic for conventional data tools,

but if harnessed and analysed effectively has the capacity to drive better, smarter and more timely decisions.

2,500,000 trillion bytes of data created each day

90%

72%

61%

39%

37% 56%

37%

28%

Sources

of Data

38%

26%

18%

16%

2%

What is big data?

Data from multiple sources

are analysed….

… to derive insights that

drive better decisions

resulting in increased sales,

greater operational

efficiencies, cost reductions

and reduced risk.

Source: IBM and Said Business School Study – ‘Analytics: the real world use of

big data’

Where do companies get their data? Why do companies use big data?

3

What is the impact of big data analytics?

Big data analytics have had significant impact on sales and productivity across industries….

….and companies with the best analytics capabilities consistently outperform others

~2X more likely to be top-quartile financial performers

No/ Poor

capabilities

Low

capabilities

Medium

capabilities

High

capabilities

Top

performers

0.0

0.5

1.0

1.5

2.0X

0.6

0.9

1.4

1.6 1.8

Average

~3X more likely to be “highly effective” at executing decisions

No/ Poor

capabilities

Low

capabilities

Medium

capabilities

High

capabilities

Top

performers

0.0

1.0

2.0

3.0X

0.4

0.6

1.6

2.4 3.0

Average

Big data is projected to grow into a $53.4 billion market by 2017,

up from $10.2 billion in 2013

Source: Bain Big Data Diagnostic Survey

Measuring the business impacts of effective data – University of Texas, Austin

4

How can business needs be answered with big data?

Targeted sales & marketing

Customer experience

Risk & financial management

Optimising operations

Enabling new business models

Business needs Data sources

Identify ‘premium’ customers

Test the efficacy of existing strategies

Personalised marketing

Identify new sales channels

Demographic data

Location and beacon data

Spending patterns – products, locations,

frequency and sequence of buying

What value added services would increase

customer retention

Which after-sales services leads to repeat

business

Customer segmentation data

Lifestyle data

Location and beacon data

Determine location of inventory shrinkage

and plan flow of goods

Identify synergies with other business units

Sales patterns – product location, volume,

POS data

Price movements

Inventory analysis – RFID

Weather and events

Determine new markets for expansion

Identify partnership opportunities

Analyse product trends

Product perception data – reviews,

tone/mood of customers in social media

Market sentiment data

Geographic and demographic data

Minimise fraud

Identify ‘high value’ customers with low risk

of non-payment

Spending patterns

Financial health – credit history,

frequency/timing of debt repayment, savings

Behavioural patterns – e.g., health

consciousness

5

Big data in action

Crunches click-stream and

historical user data to

recommend products leading

to 35% increase in sales

Processes and mines

petabytes of user data to

power ‘People you may know’

Analyses 75 million events

per day to better target

advertisements

Analyses web logs, transaction

data and social media to

detect fraudulent activity

Big data analysis led to 15%

increase in online sales for

$1B in incremental revenue’

Uses an analytics-based

telematics solution to price

insurance based on driver

behaviour

Creates personalised campaigns

based on real-time analytics of

geospatial, behaviour-based

customer data

Improves the search experience,

targets personalised emails and

improves click-through rates using

advanced analytics

Gains real-time actionable

insight to quickly identify

questionable patterns and stop

fraud before it happens

Uses big data analytics to put real-

time intelligence and control back

into the network, driving a 90%

increase in capacity’

Source: IBM big data and analytics; Wikibon; Penser Consulting analysis

6

01

Data

collection

02

Cleaning &

processing

Data

03

Data

warehousing

04

Exploratory

Data analysis

05

Data

Modelling

06

Report &

Visualise

Data

07

Draw

insights

from Data Penser

Consulting

focuses on

these areas

08

Make and

implement

decisions

How can Penser Consulting assist you in building data

analytics capabilities?

7

Data Visualisation

Gives users the capability to draw

insights from large complex data sets.

Enables the overlay of different data

sets including geospatial information to

understand drivers.

Risk & fraud detection

Identify product bundles

Customer segmentation

Predicting buying behaviour

Testing efficacy of programmes

Example 1: Network analysis indicates

that opinion formers (e.g., celebrities)

drive the likely demand for products

they use.

Example 2: Overlay spending patterns

with demographic and geospatial

information to segment customers and

detect patterns that are not visible

through traditional methods.

What do we do?

Analytical Tools

Applications

Examples

Predictive Modelling & Analytics

Encompasses a variety of statistical

techniques from modelling, machine

learning and data mining to

analyse current and historical facts to

make predictions about the future.

Customer retention

Fraud detection

Cross-selling of products

Logistics and supply chain

management

Example 1: A churn model revealed

that users with failed login attempts –

possibly after forgetting their password

– were at high risk for defection.

Example 2: A study showed better

credit behaviour among consumers

whose buying behaviour indicates they

are more cautious, e.g., purchasing

products designed for physical safety.

Cluster Analysis & Pattern Mining

Cluster analysis is grouping sets of

objects to detect similarities and

differences.

Pattern mining is finding relevant

patterns in data examples.

Personalised marketing

Cross-selling of products and

services

Predicting spending patterns

Customer attrition

Example 1: New graduates were more

likely to buy specific items, such as

furniture, mobile phones and cars.

Example 2: A major North American

telecommunications company has

shown that customers with a

cancellation in their calling network are

600% more likely to cancel.

8

About Us

Data Analytics & Insights Leads

Dinesh Markose co-leads the Data Analytics & Insights practice. He has deep expertise in big data analytics and

helping companies shape their business decisions. Previously he was Global Head of model validation for equity

products at Goldman Sachs. His expertise includes equity and credit products with a focus on pricing and risk. He

holds a PhD in Mathematics from Cambridge University, UK, where he held the prestigious Ramanujan scholarship.

Rebecca Jacob is a director at Penser Consulting and co-leads the Data Analytics & Insights practice. She has

expertise in using survey research and statistical analysis to drive business decisions on operations improvement,

process efficiency, and HR practices. Previously she has worked as a senior quantitative research analyst within the

Corporate Executive Board’s HR practice – Corporate Leadership Council, and was a Senior Vice President at Bank

of America where she led learning and leadership development for Global Risk, Compliance, Legal, Audit and

Corporate Affairs. Rebecca holds an MSc in Industrial/Organisational Psychology from Purdue University, USA.

Penser Consulting is a specialist consulting firm focused on the payments industry. Based in

London, our expertise spans online payments, mobile payments, remittances and prepaid cards

and we work with financial institutions and private equity portfolio companies across Europe and

Asia. We are a full service practice and provide end-to-end support on building and growing your

business, including strategy, product development, sales and marketing, data analytics and

insights, and international market expansion support to payments companies.

Penser Consulting Ltd.

23 Harcourt Street

London W1H 4HJ

United Kingdom

T: +44 207 096 0061

E: info@penserconsulting.co.uk

www.penserconsulting.co.uk

Twitter: @PenserConsult © Penser Consulting 2014