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Data Science and Engineering for Marketers

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Learn data-driven marketing theory and practice.

"Marketing has become a technology-powered discipline, and therefore, 
marketing organizations must 
infuse technical capabilities 
into their DNA." ~Scott Brinker

Published in: Marketing
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Data Science and Engineering for Marketers

  1. 1. @MicahHerstand INNOVATION IN MARKETING: DATA SCIENCE & ENGINEERING
  2. 2. @MicahHerstand Software Engineer, User Advocate, Writer, Actor, Singer-Songwriter @MICAHHERSTAND ˈmikə
  3. 3. @MicahHerstand “Marketing has become a technology-powered discipline, and therefore, 
 marketing organizations must 
 infuse technical capabilities 
 into their DNA.” ~Scott Brinker, MarTech Conference Program Chair
  4. 4. @MicahHerstand LESSON OBJECTIVES: Theory Discover how data science enables marketing innovation Measure, Metric, CSF, KPI Customer segmentation Big Data, Open Data, Linked Data Growth Hacking Ensure your org’s data engineering empowers marketers Database SQL (Relational), NoSQL (Document, Graph) Data warehouse
  5. 5. @MicahHerstand LESSON OBJECTIVES: Setup Install a database manager Sequel Pro for Mac MySQL Workbench for Windows & Linux Connect to your org’s database Standard, SSH, SSL Bookmark SQL helpers SQLZoo (run SQL online!), Tutorials Point, Khan Academy Google queries: “site:docs.oracle.com UNKNOWN TERM”
  6. 6. @MicahHerstand LESSON OBJECTIVES: Practice SQL Create a mental model for what it’s like to query SQL using English first Acquire the vocabulary to understand a SQL query Encounter example SQL queries and see their results Practice your knowledge through exercises
  7. 7. @MicahHerstand DATA SCIENCE Measure, Metric, CSF, KPI Customer segmentation Big Data, Open Data, Linked Data Growth Hacking
  8. 8. @MicahHerstand DATA SCIENCE
  9. 9. @MicahHerstand DATA SCIENCE: Measure, Metric, CSF, KPI It’s the metrics, stupid! “The price of light is less than the cost of darkness.” ~Arthur C. Nielsen, namesake of Nielsen TV ratings “What gets measured, gets managed.” “There is nothing so useless as doing efficiently that which should not be done at all.” "Management is doing things right; leadership is doing the right things." ~Peter Drucker, the founder of modern management
  10. 10. @MicahHerstand DATA SCIENCE: Measure Definition: Anything that can be measured Caveat: Must be a single variable measure E.g. № of users E.g. № of active users Challenges: Definition of terms E.g. Does account creation make someone a customer? Measurement process E.g. How frequently should data be collected?
  11. 11. @MicahHerstand DATA SCIENCE: Metric Definition: Value derived from 2+ measures Metric selection: Efficiency vs effectiveness E.g. cost of customer acquisition vs customer lifetime value Analysis: Information vs insights E.g. customer value vs value of customers acquired through LinkedIn Optimization: Source vs campaign E.g. customers w/ expired CC vs customer bounce rate when CC expired Caution: Vanity, engagement, and benchmark metrics E.g. Facebook Likes, Time on Page, DVD sales
  12. 12. @MicahHerstand DATA SCIENCE: CSF (Critical Success Factor) Definition: What is required to achieve business objectives. E.g. acquire new customers Prerequisites: Business objectives E.g. to obtain 10% market share (BO), must acquire new customers (CSF) More on CSFs: bit.ly/sidata-csf
  13. 13. @MicahHerstand DATA SCIENCE: KPI (Key Performance Indicator) Definition: A measurable value that demonstrates how effectively a company is achieving key business objectives E.g. cost per lead, customer lifetime value, traffic-to-lead ratio, retweets of last ten tweets, landing page conversion rates Prerequisites: Critical Success Factors E.g. to acquire new customers (CSF), track those acquired per week (KPI) Requisites: SMART (Specific, Measurable, Achievable, Relevant, Time) E.g. weekly rate of customer acquisition Caution: Perverse incentives and unintended consequences E.g. referral programs to increase customer acquisition More on KPIs: bit.ly/sidata-kpi
  14. 14. @MicahHerstand DATA SCIENCE: CSFs vs KPIs Graphic origin: bit.ly/sidata-kpi-vs-csf
  15. 15. @MicahHerstand DATA SCIENCE: Prioritization “Never confuse motion with action.” ~Benjamin Franklin Graphic Origin: bit.ly/sidata-metrics-graphic
  16. 16. @MicahHerstand DATA SCIENCE Measure, Metric, CSF, KPI Customer segmentation Big Data, Open Data, Linked Data Growth Hacking
  17. 17. @MicahHerstand DATA SCIENCE: Customer Segmentation
  18. 18. @MicahHerstand DATA SCIENCE: Customer Segmentation, 2.0
  19. 19. @MicahHerstand DATA SCIENCE: Customer Segmentation, 3.0
  20. 20. @MicahHerstand DATA SCIENCE: Customer Segmentation Definition: the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing E.g. SI grads, New Yorkers, users who have yet to purchase Utility: One size does not fit all. Allows for novel KPIs. Prerequisites: Business Objectives, Metrics E.g. Want to gain 10% salon market (Biz Objective), while 25% of total customers are men (metric), target men as it’s an under-saturated market Types: A priori, Needs-based, and Value-based Caution: Don’t break the law by targeting protected classes E.g. AirBnb cannot offer Iranian-Americans discounts for Nowruz More on KPIs: bit.ly/sidata-kpi
  21. 21. @MicahHerstand DATA SCIENCE Measure, Metric, CSF, KPI Customer segmentation Big Data, Open Data, Linked Data Growth Hacking
  22. 22. @MicahHerstand DATA SCIENCE: Big Data, Open Data, Linked Data
  23. 23. @MicahHerstand DATA SCIENCE: Big Data, Open Data, Linked Data "Big Data will spell the death of customer segmentation and force the marketer to understand each customer as an individual.” ~Ginni Rometty, CEO, IBM "Google only gives you answers for questions people have asked before.” “A mark of a good site is realizing you're not the only site in the world.” ~Tim Berners-Lee, inventor of the World Wide Web
  24. 24. @MicahHerstand DATA SCIENCE: Big Data
  25. 25. @MicahHerstand DATA SCIENCE: Big Data Definition: Data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Technical Challenges: Volume (amount of data) Velocity (speed of data in and out) Variety (range of data types and sources) Human Challenges: No magic bullets, easy to overstate current capabilities Novel Opportunities: Real-time pricing, Sentiment analysis, Optimized offers
  26. 26. @MicahHerstand Designed by Forrester Research, accessed at bit.ly/sidata-bigdata
  27. 27. @MicahHerstand Designed by Forrester Research, accessed at bit.ly/sidata-bigdata
  28. 28. @MicahHerstand Designed by Forrester Research, accessed at bit.ly/sidata-bigdata
  29. 29. @MicahHerstand DATA SCIENCE: Open Data Definition: Data should be freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control. “Free as in speech, not beer.” E.g. data.gov, census.gov Alternate Definition: Public or private data stores available for integration into one’s own data system. E.g. developer.nytimes.com, Thomson Reuters Challenges: Low cost, high quality, and large quantity—pick two Data normalization (e.g. gender and sex, China bowls vs China country)
  30. 30. @MicahHerstand DATA SCIENCE: Linked Data Definition: A method of publishing structured data so that it can be interlinked and become more useful through semantic queries. E.g. Facebook’s Open Graph, Google Rich Snippets, Twitter Cards Novelty: Data sources share schema so no middleware necessary Challenges: Comparatively few data sources Data analysis tools less mature Fewer trained developers "Marketing department might want to dominate the Linked Data web.” ~Ralph Swick, COO of the W3C, organization responsible for World Wide Web standards
  31. 31. @MicahHerstand DATA SCIENCE: Linked Data "When companies post data as Linked Data they can be held accountable. Regex has [fuzzy] responsibility.” ~Ralph Swick, COO of the W3C, organization responsible for World Wide Web’s technology standards Accessed March 8th, 2017
  32. 32. @MicahHerstand DATA SCIENCE Measure, Metric, CSF, KPI Customer segmentation Big Data, Open Data, Linked Data Growth Hacking
  33. 33. @MicahHerstand DATA SCIENCE: Growth Hacking Graphic origin: bit.ly/sidata-gh-cartoon-2
  34. 34. @MicahHerstand DATA SCIENCE: Growth Hacking Graphic origin: bit.ly/sidata-gh-cartoon-3
  35. 35. @MicahHerstand DATA SCIENCE: Growth Hacking “Growth hackers are a hybrid of marketer and coder.” “[Growth hacking] requires a blurring of lines between marketing, product, and engineering, so that they work together to make the product market itself.” ~Andrew Chen, Head of Rider Growth at Uber “The true unicorns are those who can go end-to-end designing, building, measuring, analyzing, and iterating with a combination of user intuition and deep analytics.” ~Matt Humphrey, Sold his startup HomeRun for $100M+ after 18 months
  36. 36. @MicahHerstand DATA SCIENCE: Growth Hacking Definition: A process of rapid experimentation across marketing channels and product development to identify the most effective, efficient ways to grow a business. E.g. Airbnb cross-listing on Craigslist Novelty: Interdisciplinary skills and knowledge Prerequisites: Interdisciplinary teams, acceptance of failure, outside-the-box thinking Requisites: Measurable, metric-based
  37. 37. @MicahHerstand DATA SCIENCE: Growth Hacking Example
  38. 38. @MicahHerstand DATA SCIENCE: Growth Hacking Example
  39. 39. @MicahHerstand DATA ENGINEERING Database SQL (Relational) NoSQL (Document, Graph) Data warehouse
  40. 40. @MicahHerstand DATA ENGINEERING: Database
  41. 41. @MicahHerstand DATA ENGINEERING: Database Definition: A collection of structured data, organized for rapid search by an automated computer program. Novelty: List or calculate data from various sources E.g. How much revenue has been made by sales from customers whose first visit was referred by a Facebook ad? E.g. How many customers (who have made at least $100 in purchases total) have used our referral program?
  42. 42. @MicahHerstand DATA ENGINEERING: Database Structured data Primary key
  43. 43. @MicahHerstand DATA ENGINEERING: Levels of structure Graphic Origin: http://5stardata.info/
  44. 44. @MicahHerstand DATA ENGINEERING: Database Keys
  45. 45. @MicahHerstand DATA ENGINEERING: Database Security
  46. 46. @MicahHerstand DATA ENGINEERING Database SQL (Relational) NoSQL (Document, Graph) Data warehouse
  47. 47. @MicahHerstand DATA ENGINEERING: Relational Database Definition: A type of database that organizes data into tables (think spreadsheet) and creates clearly defined relationships between those tables. E.g. SQL (MySQL, PostgreSQL, SQLite, Oracle Database, MS SQL) SQL is a programming language that lets people setup relational database as well as add, update, delete, and lookup data within them. Novelty: Up-front schema, data integrity checks, transactions. E.g. ensure a movie cannot be added without an associated director Challenges: Large datasets and an evolving schema are difficult to manage. E.g. you want to track customers’ age, then decide not to, then decide to track gender as a binary, then decide to make gender a free-text option… bit.ly/sidata-sql-vs-nosql
  48. 48. @MicahHerstand DATA ENGINEERING: Relational Database Foreign key Movies
  49. 49. @MicahHerstand DATA ENGINEERING: Relational Database Movies Directors
  50. 50. @MicahHerstand DATA ENGINEERING Database SQL (Relational) NoSQL (Document, Graph) Data warehouse
  51. 51. @MicahHerstand DATA ENGINEERING: NoSQL Databases Definition: A database that is not a relational database. (NoSQL is colloquial jargon, not a standard) E.g. MongoDB, Redis, Couchbase, neo4j Novelty: No schema required to store data. Easily scalable. Super fast lookups. E.g. easy to track customers’ age, then decide not to, then decide to track gender as a binary, then decide to make gender a free-text option… Challenges: Data integrity, stable transactions. E.g. cannot ensure a director is always included when adding a movie bit.ly/sidata-sql-vs-nosql
  52. 52. @MicahHerstand DATA ENGINEERING Database SQL (Relational) NoSQL (Document, Graph) Data warehouse
  53. 53. @MicahHerstand DATA ENGINEERING: Data warehouse
  54. 54. @MicahHerstand DATA ENGINEERING: Data warehouse Definition: a computer system optimized for analytical and informational processing that is filled with data copied from both inside and outside the enterprise E.g. a database with both a sales table and a google analytics table and a census table. Novelty: analyze business data without affecting day-to-day operations E.g. you want to see employee clock-in times without preventing them from simultaneously clocking out. Challenges: large overhead and maintenance costs without being necessary
  55. 55. @MicahHerstand DATABASE SETUP Database manager application Database Connections SQL Helpers to Bookmark
  56. 56. @MicahHerstand DATABASE SETUP: DB Manager Application Definition: A graphical user interface that simplifies database interactions for developers Examples: Sequel Pro for Mac: bit.ly/sidata-mac MySQL Workbench for Windows & Linux: bit.ly/sidata-not-mac PHPMyAdmin for web access
  57. 57. @MicahHerstand DATABASE SETUP Database manager application Database Connections SQL Helpers to Bookmark
  58. 58. @MicahHerstand DATABASE SETUP: Database connections Unsecured Connections are often called “standard” and require no setup besides the application you just downloaded Secured Connections can use SSH or SSL and require additional encryption technology to be installed on your computer. Your company should have documentation on how to use these.
  59. 59. @MicahHerstand DATABASE SETUP: DB Connection Info Server: www.herstand.com User: sistudents Password: Hf68S9CpK67RUDV3 Database: simovies Port: 3306 (default MySQL port)
  60. 60. @MicahHerstand DATABASE SETUP Database manager application Database Connections SQL Helpers to Bookmark
  61. 61. @MicahHerstand DATABASE SETUP: SQL Helpers to Bookmark Learn: TutorialsPoint.com, KhanAcademy.com Play: SQLZoo.net (run SQL online!) Cheatsheet: bit.ly/sidata-sql-cheat-sheet Cheatsheet with examples: bit.ly/sidata-cheat-with-examples RTFM: bit.ly/sidata-mysql-rtfm
  62. 62. @MicahHerstand PRACTICE SQL English queries Vocabulary Stock SQL queries Exercises
  63. 63. @MicahHerstand PRACTICE SQL: English queries Questions SQL can answer: Who, What, Which, Where, When, How Many E.g. Who directed the film Get Out? E.g. Who acted in the film Get Out? E.g. What films were released before Jan 1, 2000? E.g. Where did the director of Get Out go to college? E.g. Which colleges had the most graduates direct films since Jan 1, 2000. E.g. When was Get Out released? E.g. How many actors were in both Get Out and The West Wing?
  64. 64. @MicahHerstand PRACTICE SQL English queries Vocabulary Stock SQL queries Exercises
  65. 65. @MicahHerstand PRACTICE SQL: Vocabulary Syntax , . ; ( ) “ ” * Verbs SELECT INSERT UPDATE DELETE Query Parts AS FROM WHERE HAVING ORDER BY GROUP BY Filters LIKE NOT > < = != >= <= AND OR IN % Sort ASC DESC Aggregate Functions MIN, MAX, SUM, AVG, COUNT Advanced Functions INNER JOIN OUTER JOIN REGEXP
  66. 66. @MicahHerstand PRACTICE SQL English queries Vocabulary Stock SQL queries Exercises
  67. 67. @MicahHerstand PRACTICE SQL: Anatomy of a Query SELECT * FROM movies; Result
  68. 68. @MicahHerstand PRACTICE SQL: Anatomy of a Query SELECT FROM movies WHERE ; title AND release_date title COUNT(title) AS num_of_titles title AND MIN(release_date) title = “%Star Wars%” release_date > ‘2000-1-1’ release_date > ‘2000-1-1’ AND title = “%Star Wars%” title = “Get Out”* Result
  69. 69. @MicahHerstand PRACTICE SQL: Anatomy of a Query SELECT * FROM movies GROUP BY release_date titleORDER BY ASC DESC Result ;
  70. 70. @MicahHerstand PRACTICE SQL English queries Vocabulary Stock SQL queries Exercises
  71. 71. @MicahHerstand PRACTICE SQL: Exercises List all movies and their average rating, the average column should be called 'average' List only the top-rated movie List only the bottom-rated movie Which user gives the highest rating on average?

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