Penser Consulting answers the key questions: - What is big data, and why does it matter? - How can big data drive business decisions? - How can you build data analytics capabilities in your organisation?

1. Big Data What’s the big deal?

2. 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?

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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

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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

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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

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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?

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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.

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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.

9. Penser Consulting Ltd.
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www.penserconsulting.co.uk
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© Penser Consulting 2014