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Big Data Marketing Analytics


A brief overview of what Big Data analytics is all about and how it helps change business decisions in the right directions.

A brief overview of what Big Data analytics is all about and how it helps change business decisions in the right directions.

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  • Unstructured Data: Most of the content on web is unstructured
  • The speed with which Big data is growing, companies which are proactive and have the capability to provide big data analytics will have a competitive edge over the competitors as compared to the companies which do not have such provisions.


  • 1. EmergingMarketingAnalyticsBIG DATA Akash| Bryan|Nishi| Prashant| Subhadeep| Vaibhav
  • 2. Types Of Data Data Semi Structured Unstructured Structured Enterprise Resource Call centre logs withTraditional Data in a Facebook, linkedin Planning, back up toll –free responses, Structured buttraditional Database logs, web chats, Inconsistent storage for large unstructured “blobs” web logs that track structure YouTube volumes of data website activity
  • 3. What is Big Data? Advanced analytics operate on Big Data. Leverage data to make better business decisions. Data is increasing at a Rate at which data Velocity rate of 15-20% is consumed or Extremely large amounts -Batch generated of data (Terabytes) -Real time -Near Time Big data Variety Volume -Structured -TerabytesRange and type of -Unstructured - Transactions data sources -Semi structured - Tables etc.
  • 4. Big Data: Why? Uses of Big Data:• Marketing decisions and analytics• Innovating new products and services• Risk management• Applicable to all domains – BFSI, Telecom, Media, entertainment etc. Potential of Big Data Increase value Increase value Decrease Increase US of US of Europe’s PSA manufacturing retails net healthcare by by EUR250 cost by 50% margin by 60% $300 billion billion Source: McKinsey Report
  • 5. RETAIL- Walmart
  • 6. Need Retail industry is customer driven Fierce competition - Very less switching cost -Stock out - Non Availability of any item Companies should be well informed -Continuous monitoring of customers data (real time monitoringFor retailers to be1. Competitive2. Customer retentionTrack and analyse social media and all other forms of customer data available
  • 7. Market Causality Intervening Component Antecedent Extraneous
  • 8. Data OrganizationType of Errors Manual Data Automated Data Organization OrganizationIncoherentIncorrectIrrelevantIncompleteInconsistent
  • 9. Metadata Y = Sales X = 10 P’s of marketing Y = f(X)Type of Metadata:• Customer Life time Value• Consumer buying behavior• Transaction pattern• Churn score
  • 10. Social Media Analytics- the new wave 50% are unsure of 50% businesses 53% are unaware how to measure are unsure of of their ROI from impact of direct value of Twitter business metrics LinkedIn from blogs
  • 11. Social Media Analytics- the new wave Configure your Revise your• Quantitative- specific KPIs • Social n/w New likes, Analytics strategy • PAID tools- Radian 6, format • Change content and total likes, metrics that • Choose Page views, •SYSOMOS, Lithium, Raven Frequency change referrals translate into business •• Create a filter or segment Test test test to get better • FREE tools- Social market • Study target page context• Qualitative data- Users, for social traffic results Mention, Whostalking, • Your response rate and language, locations, •• Add term benefits Long event tracking Thinkup relevance comments •• Measure events responses Identify worst performing• Activity data- post views, and interactions metrics interactions, interaction Define measurable •• Ad campaign tracking Ad campaign tracking Use super social times, response rates. and Actionable KPIs What to do? tools Understanding each social metrics
  • 12. How Walmart connects! More than 2 Daily consumerOver 22 million million insights and likes comments data mapping
  • 13. RESULTS Inventory• Cost and Mission- • Best Price to Customers success alignment • Right portfolio of goods • Better inventory and • Understand Customer Logistics management better by using Predictive analytics Improved Cost Effective Customer Service
  • 14. THANKS