Frakture Deck

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Intro Deck to Frakture: Math Eats Marketing

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Frakture Deck

  1. 1. Smart Data NewSQL Predictive Analysis Data Moneyball Flow Import Marketing Consultants Node.js Touch Mobile Cloud Marketing MathRegression Analyze Testing Message Production Actionable Insight Analytics 2.0 Data Scientists Customer Cohorts Multiple Channels ROI Quants CMOSegments BI Competitive Advantage Strategy Simplify Ease of Use Speed Insight & Action: Math Eats Marketing NewSQL Predictive Analysis Data Moneyball Flow Import Smart Data Marketing Consultants Node.js Touch Mobile Cloud Marketing MathRegression Analyze Testing Message Production Actionable Insight Analytics 2.0 Data Scientists Customer Cohorts Multiple Channels ROI Quants CMO Segments BI Competitive Advantage Strategy Simplify Ease of Use Speed
  2. 2. The Problem >#1Data Sources:Marketing is being transformed due to theflood of data. Harnessing that power is stilla problem. Aggregating data ACROSSchannels and technologies is hard.65% of Organizations say that integrating multiple data sources is a challenge DATATOO many channels, toomany data sourcesSource: http://blog.neolane.com/conversational-marketing/big-data/
  3. 3. The Problem >#2 Good Analytics are difficult to get > Lack of Human Resources 2018 Data Analyst Shortage > 140,000-190,000: Shortage by 2018 of people with deep analytical skills among US Companies > Benefits are tangible, but not always obvious > Lack of good, easy cross-channel modeling tools = 190,000#3 Turning Insight into Action: = 1500 Once you HAVE good analytics, implementing and getting to real engagement is hard > Turn data into something: Relevant, Timely, Contextual, Personal – A fancy dashboard is insufficient > Being an expert in each of these channels is impossibleTools that do all three of these things well and make it simple do not exist Source: http://blog.neolane.com/conversational-marketing/big-data/
  4. 4. Why it’s solvable NOW > Digital Advertising and Social Media have moved beyond hype Public acceptance of Analytics and Big Data Professional data analysis techniques and controlled experimentation have matured
  5. 5. Who’s Trying >Automation OptimizationAutomation companies are focused Optimization companies improveon making work easier for the impact of marketing, generallymarketers. This includes in a small number of channels.dashboards, process automation,action triggers, integration with Analysis Speeding up web page delivery, targeting ads, testingCRM, etc. Analysis companies make marketers infrastructures, etc. all fall under better by delivering new or better Optimization. statistics for them to make decisions on. Predictive analytics, cross-channel metrics, numbers, numbers, numbers.
  6. 6. Why were the ones to solve it > Chris Lundberg April Pedersen Co-Founder, CEO Co-Founder Geek with People Skills. I build Social Entrepreneur addicted to highly scalable technology that helping people figure out how to people actually need to do their use technology for meaningful jobs better and scalable engagement. Leader, organizer, companies to support them. marketer, rabble rouser.We are : Experienced Tech & Social Entrepreneurs who enjoy majordisruption in industries & helping groups grow and better engage with theiraudiences.Were all about the Action.Together we have : Founded and ran $9M SaaS CRM and Founded and ran nonprofit SaaS to Communications Platform with 2000 make technology accessible to clients and 70 staff. small and mid-sized charities.
  7. 7. The Technology Were Building >Our working prototype includes :1) Data, Data, Data: Weve built a number of wicked fast Extract/Transform/Load libraries to ingest millions of records from a number of different sources, identifythem to the person, and pull out transaction details. In seconds.2) Segment and Prediction engine: Using combinations of column storeDBs(Redshift), object stores (MongoDB), and modeling (custom), we can slice and dicethe data in thousands of ways, run prediction models, and expose them via an API. 3) Interface: The interface layer delivers results from the engine API via Node.js to desktops, tablets, and touchscreens, producing interactive timelines and charts, as well as producing and managing full campaigns across a number of different channels.
  8. 8. The Tech Stack > APIChannels Amazon Redshift Warehouse 3rd Party Prediction DATA Statistics Modeling
  9. 9. The Company Were Building >Focus over the next 6-12 months:Build out team of 5-7 and working with our "Gamma" cohort ofclients to test & build on our prototypeTarget Market:CMOs, consultants, agencies serving companies with: > $1M marketing budget > Marketing across min 4 channels > Smart Analytics/Data folks w /ability to recognize ROIBuild Brand:Geeks solving Marketing/Engagement problemswith a simple, elegant solutions
  10. 10. The Investors We Want >Goal:Raise just enough seed money ($750K) to build an eliteteam for 6-12 months of work with a small cohort ofclients to build the product and ensure relevanceWe want investors who: Are founder-centric Believe in the importance of a tech CEO Interested in helping us build a strong network Believes in iterative development of product & company Can provide industry advice when necessary Believes the product is only as good as the clients it has
  11. 11. Insight & Action: Math Eats Marketing www.frakture.com info@frakture.com angel.co/frakture twitter.com/fraktureinc

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