Zakipoint Introduction

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Zakipoint Introduction

  1. 1. Big Data to Revenue™ 1 Strictly Confidential • Copyright 2011 zakipoint
  2. 2. Context“In 2010 the amount of data collected exceeded 1 trilliongigabytes 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 2 Strictly Confidential • Copyright 2011 zakipoint
  3. 3. Our Visionzakipoint integrates the strategy, operations, technology and mathematical modelingfor 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™ 3 Strictly Confidential • Copyright 2011 zakipoint
  4. 4. Our Services • Identify goals, objectives, benefits, strategy and challenges for data analyticsData 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 learningData 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 fundamentallyTechnology 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 4 Strictly Confidential • Copyright 2011 zakipoint
  5. 5. Our Edge zakiEdge™ Connect Business There is Rigorous Fast Cycle of ROI focused Challenge to No Bad data Mathematics Analysis ScienceChallenge Data Analysis ActionFocus on business Work with full range of data Apply wide array of cutting Bias towards actionableobjectives 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 5 Strictly Confidential • Copyright 2011 zakipoint
  6. 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 6 Strictly Confidential • Copyright 2011 zakipoint
  7. 7. Our Science and Technologyzakipoint prides in being business challenge focused with highest quality datascience 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 7 Strictly Confidential • Copyright 2011 zakipoint
  8. 8. Our Big Data TeamRamesh Kumar is Managing Partner of zakipoint, and brings deep experience in strategyand decision making through data analytics. Ramesh has worked at Monitor Group’shelping fortune 50 clients develop data analytic driven marketing strategy Ramesh holdsan undergraduate and Masters degrees from Oxford University, UK inEngineering, Economics and Management and Masters from University of Pennsylvania inOperations Research. He has also completed Unit 1 of OPM program at Harvard BusinessSchool.Costas Boussios, PhD, leads the Data Science practice at zakipoint. Dr. Costas Boussios isa data scientist with expertise in Predictive Statistical Modeling and Machine Learning.He has over 12 years experience leading projects and building models with large datasets in a variety of industries, including financial risk scores and target marketing. He hasworked 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 ofcustomer loyalty, retention and up-sell. Shahin has worked with majorentertainment, broadcasting & mobile technology companies such as: DIRECTV, FoxMobile, HBO, Starz, Showtime, Helio/Virgin Mobile, AT&T U-verse, MTV Networks andothers. Shahin has a undergraduate degree from UMass and PhD from MIT. 8 Strictly Confidential • Copyright 2011 zakipoint
  9. 9. Our Executive Team (cont…)J.Singh, PhD leads the data technology practice at zakipoint. J is an adjunct professor atWorcester Polytechnic Institute teaching classes on data base technologies. J. has been a CTOat various technology companies, architecting scalable cloud based platforms, and launchingthem. Prior to that he was an executive at Fidelity working on new technology disruptions andlaunching 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) 9 Strictly Confidential • Copyright 2011 zakipoint
  10. 10. Our ExpertiseFinancial 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 modellingInsurance 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 10 Strictly Confidential • Copyright 2011 zakipoint
  11. 11. ProblemCompanies are not able to identify and focus on revenue maximizationopportunities 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 11 Strictly Confidential • Copyright 2011 zakipoint
  12. 12. OpportunityTremendous opportunity in combining transaction, customer service and externaldata 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 12 Strictly Confidential • Copyright 2011 zakipoint
  13. 13. Customer acquisition through big dataBig data that combines internal and external data sources can pinpoint customerswho 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 investSource: David Skok, Matrix Partners 13 Strictly Confidential • Copyright 2011 zakipoint
  14. 14. Customer Retention through big dataThe core of customer retention is knowing the customer; Big data analytics makestruly knowing the customer possible for the 1st time Commitment to A 5% increase in Satisfied customers customer experience Knowing the customerretention increases tell 9 people, while yields up to 25% more & meeting theirbusiness 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 behavior1,3: Gartner Group and “Leading on the Edge of Chaos”, Emmett C. Murphy and Mark A. Murphy2: http://www.allbusiness.com/sales/customer-service/1096122-1.html 14 Strictly Confidential • Copyright 2011 zakipoint
  15. 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 loyalty1,2: http://marketing.about.com/od/relationshipmarketing/a/crmstrategy.htm3: 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/) 15 Strictly Confidential • Copyright 2011 zakipoint
  16. 16. New Product and Service introduction through big dataCustomers will talk about products/services via many channels, big data analyticsturns 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 16 Strictly Confidential • Copyright 2011 zakipoint
  17. 17. Get started today Thank you ramesh.kumar@zakipoint.com +1 857 383 1574 www.zakipoint.com 17 Strictly Confidential • Copyright 2011 zakipoint

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