zakipoint helps clients maximize revenue through big data analytics. It integrates strategy, operations, technology and data science to redesign businesses. zakipoint identifies goals and challenges, analyzes ROI from data opportunities, and prioritizes implementing new data models. It runs advanced analytics on structured and unstructured data using machine learning. zakipoint also implements infrastructure for storing, managing and analyzing big data to fundamentally change costs or store vast quantities of data. This allows targeting customers, improving retention, and increasing cross-sell and upsell through comprehensive use of data.

1. Big Data to Revenue™
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2. Context
“In 2010 the amount of data collected exceeded 1 trillion
gigabytes and it is doubling every 2 years”
- IDC
“Data is now big data, with increasing
volume, velocity and variety”
- Michael Stonebraker, Professor at MIT
The phrase “Drowning in data, but starving for
knowledge” has over 1 million search results on google
search
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3. Our Vision
zakipoint integrates the strategy, operations, technology and mathematical modeling
for big data to redesign client’s businesses for step change revenue growth
Data
Science to
Action™
Big data to
revenue™
Data Technology
Science on for Big
Big Data™ Data™
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4. Our Services
• Identify goals, objectives, benefits, strategy and challenges for data analytics
Data Science • Analyze ROI from various data analytics opportunities
to Action™ •
•
Prioritize plans for leveraging & implementing new data models
Train of executives in the data analytics decision making domain
• Run advance data analytics using latest developments in machine learning
Data Science • Merge structured and unstructured data for predictive modeling
• Merge and match to create unified data sets
for big • Advise and select the most appropriate modeling techniques for business problem
data™ • Review of existing data models and propose improvements
• Train in-house team on usage of big data analytics
• Architect technology stack to store, manage and analyze big data to fundamentally
Technology change the cost structure or store vast quantities of data
• Implement and set up infrastructure for on-going needs
for big • Set up DB to store or transpose existing data for on-going data using open source
data™ technologies like Hadoop, MongoDB, Hive etc.
• Train tech team to manage and maintain new technology stack
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5. Our Edge
zakiEdge™
Connect Business There is Rigorous Fast Cycle of ROI focused
Challenge to No Bad data Mathematics Analysis
Science
Challenge Data Analysis Action
Focus on business Work with full range of data Apply wide array of cutting Bias towards actionable
objectives and challenges • Transaction data edge data science modelling
• Strategy consultants from • Web clickstream data techniques • Segmentation
top tier strategy companies • Call centre data • Quantitative Analysis • Prioritization & Ranking
• Consultants with extensive • Customer service data • Linear and Logistic • Conversion improvement
industry and executive • Web scrapped data regression • Visualization tools
experience who • Unstructured data from • Text mining • Dashboards to ensure
understand operational blogs, portals, competitor • Natural Language
on-going usage of
challenges sites processing
• Social media data from
models developed
• Team is trained at world • Sentiment Analysis
class universities and LinkedIn, FB, Meetup, Even • Training of client team to
corporations to think big tbrite etc. continue model
and laterally • Competitor data improvements and on-
going management
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6. Our Process
Type of
Insights
Business Data data Apply Data
and Implementation
Evaluation Survey modeling Science
Decisions
and ROI
• Survey of the • Data audit • Quick analysis of • Prepare data for • Present insights • Develop
organization (type, format, acc sample data and analysis and decisions implementation
on current use essibility, use) types of models • Propose data tied to insights plan
of data • Type of data • ROI analysis and models to apply • Quantify • Identify
• Objectives and modeling used types of • Run algorithms improvements technology
business • External data improvement • Iterate to find the changes
challenges that can answer • Prioritization and truth or signal (dashboard or
• Workshop to strategic key areas of from data architecture)
understand questions focus • Set up
priorities and • Data architecture • Access data from technology for
decisions in place and external data on-going use
• Prioritization challenges sources to • Train client team
of key areas of augment internal to manage on-
opportunity data going model
development
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7. Our Science and Technology
zakipoint prides in being business challenge focused with highest quality data
science capability to work on the big problems and complex data sets
• Expertise in full array of data analytics methodologies e.g., econometric
modeling, machine learning, text mining, etc.
• Manage both structured and unstructured data
• Mash data to create unique & valuable data sets
• Experience in extracting, collecting and storing large & unstructured data
sets
• Focus on turning models into advanced visualizations and dashboards to
assist action oriented decision making
• Connected with data science innovations coming out of
MIT, Wharton, Harvard and WPI
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8. Our
Big Data Team
Ramesh Kumar is Managing Partner of zakipoint, and brings deep experience in strategy
and decision making through data analytics. Ramesh has worked at Monitor Group’s
helping fortune 50 clients develop data analytic driven marketing strategy Ramesh holds
an undergraduate and Masters degrees from Oxford University, UK in
Engineering, Economics and Management and Masters from University of Pennsylvania in
Operations Research. He has also completed Unit 1 of OPM program at Harvard Business
School.
Costas Boussios, PhD, leads the Data Science practice at zakipoint. Dr. Costas Boussios is
a data scientist with expertise in Predictive Statistical Modeling and Machine Learning.
He has over 12 years experience leading projects and building models with large data
sets in a variety of industries, including financial risk scores and target marketing. He has
worked for a variety of start-ups and large companies. He holds a PhD from MIT.
Shahin Ali, PhD, has over 12 years of strategy and operational experience in the areas of
customer loyalty, retention and up-sell. Shahin has worked with major
entertainment, broadcasting & mobile technology companies such as: DIRECTV, Fox
Mobile, HBO, Starz, Showtime, Helio/Virgin Mobile, AT&T U-verse, MTV Networks and
others. Shahin has a undergraduate degree from UMass and PhD from MIT.
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9. Our
Executive Team (cont…)
J.Singh, PhD leads the data technology practice at zakipoint. J is an adjunct professor at
Worcester Polytechnic Institute teaching classes on data base technologies. J. has been a CTO
at various technology companies, architecting scalable cloud based platforms, and launching
them. Prior to that he was an executive at Fidelity working on new technology disruptions and
launching these for the group. J. has presented at a number of conferences and seminars (TiE,
Boston Software Symposium and others) on Big Data technology. He also co-chairs “Big Data”
Special Interest Group at TiE (www.tie.org)
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10. Our Expertise
Financial Services Retail & E-commerce Entertainment & Media
• New product targeting • Segmentation • Improve ad inventory
• Segmentation • Pricing models management
• Customer acquisition models • Conversion model • Increase retention via
• Customer Retention through • Web traffic and mobile usage personalized recommendations &
survival analysis analysis targeted up-sell
• Conversion models • Increase retention through novel
• Cross-promotion models comprehensive operational
• Real time analytics to assist approach
sales staff (store or call centre) • Churn modelling
Insurance Healthcare Telecom
• New product targeting • Revenue leakage analysis • New product targeting
• Revenue leakage • Segmentation
• Customer acquisition models • Customer acquisition models
• Customer retention initiatives • Customer Retention through
survival analysis & novel
comprehensive operational
approach
• New product and service
introduction model
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11. Problem
Companies are not able to identify and focus on revenue maximization
opportunities that data analytics offers because:
Data not stored in one place for easy
1 access, legacy technologies not flexible and
cost effective for large scale analytics and
use
Limited access to math-whizz talent with
2 expertise in state-of-the-art data
science, machine learning and knowledge
discovery
Limited executive experience of leveraging
3 data analytics for large scale company wide
implementations
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12. Opportunity
Tremendous opportunity in combining transaction, customer service and external
data for revenue maximization across marketing activities
Acquisition Retention Cross-sell & Up-sell New
• Lots of data • Real value in • Detailed models on product/servic
about storing and related products and e launch
customers analyzing target products to • Detailed usage
interactions, con customer specific customers maps to develop
versions, social service data and new products
media integration of all and service
comments data offerings
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13. Customer acquisition through big data
Big data that combines internal and external data sources can pinpoint customers
who are likely to convert using the most cost effective channel
Customer Acquisition Cost
• There is a huge difference in acquisition
100 X costs across self-service vs. face-to-face
channel
• Likelihood of conversion also varies at
individual customer level
• Big data analytics of customer interactions
X
10 X through different channels (social media
chatter, transaction data and position in
Self-Service Online or Face to Face
Telephone
sales funnel) to provide insights about
who to target via which channel & and
how much to invest
Source: David Skok, Matrix Partners
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14. Customer Retention through big data
The core of customer retention is knowing the customer; Big data analytics makes
truly knowing the customer possible for the 1st time
Commitment to
A 5% increase in Satisfied customers customer experience
Knowing the customer
retention increases tell 9 people, while yields up to 25% more
& meeting their
business profits by dissatisfied customers retention & revenue
expectations is crucial
25% - 125%1 tell 22 people2 than sales or
marketing initiatives3
• Big data analytics combined with human expertise is the key to quickly identify
customer needs & wants as well as the areas the company is falling short
• Leverage all data sources simultaneously (customer
service, transactions, social media, blogs, etc)
• Identify insights not captured via manual processes
• Facilitates comprehensive organizational approach to customer retention
• Allows development of proactive retention tactics based on customer behavior
1,3: Gartner Group and “Leading on the Edge of Chaos”, Emmett C. Murphy and Mark A. Murphy
2: http://www.allbusiness.com/sales/customer-service/1096122-1.html
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15. Cross-Sell and Up-Sell through big data
Big data analytics makes possible highly targeted & extremely relevant cross-sell
& up-sell promotions
It costs six times more 88% of customers
Repeat customers to sell something to a value being advised
spend 33% more than prospect than to sell on products and
new customers1 that same thing to a services that better
customer2 meet their needs3
• Big data allows the company to learn customer behaviors and preferences
• Through pattern detection across all customers, systems can learn the
appropriate products & services to recommend
• By optimizing sales opportunities with customer retention attributes, a true
win-win can be achieved
• Customer wins: increased value and better experience
• Company wins: increased revenue and customer loyalty
1,2: http://marketing.about.com/od/relationshipmarketing/a/crmstrategy.htm
3: Research by The Forum Corporation of North America (http://www.changefactory.com.au/articles/customer-service/cross-sell-to-
provide-service-in-the-hospitality-industry/)
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16. New Product and Service introduction
through big data
Customers will talk about products/services via many channels, big data analytics
turns this into actionable insights
• Customer complaints and ideas are a valuable resource for improving
company operations & products
• Big data analytics allows mining of all available data sources to understand
how customers are using products/services
• Golden nuggets of information are “hidden” in conversations with
customer service or on social media forums
• Facilitates rapid collection of customer feedback regarding new product
features or service enhancement
• Monitoring of communication channels will provide insights regarding
features & enhancements
• Possible to test ideas without committing to development via starting
discussions and monitoring responses
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17. Get started today
Thank you
ramesh.kumar@zakipoint.com
+1 857 383 1574
www.zakipoint.com
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