Data Science and Engineering for Marketers

@MicahHerstand
INNOVATION IN MARKETING:
DATA SCIENCE & ENGINEERING

@MicahHerstand
Software Engineer, User Advocate,
Writer, Actor, Singer-Songwriter
@MICAHHERSTAND
ˈmikə

@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

@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

@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”

@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

@MicahHerstand
DATA SCIENCE
Measure, Metric, CSF, KPI
Customer segmentation
Big Data, Open Data, Linked Data
Growth Hacking

@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

@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?

@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

@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

@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

@MicahHerstand
DATA SCIENCE: CSFs vs KPIs
Graphic origin: bit.ly/sidata-kpi-vs-csf

@MicahHerstand
DATA SCIENCE: Prioritization
“Never confuse motion with action.” ~Benjamin Franklin
Graphic Origin: bit.ly/sidata-metrics-graphic

@MicahHerstand
DATA SCIENCE: Customer Segmentation

@MicahHerstand
DATA SCIENCE: Customer Segmentation, 2.0

@MicahHerstand
DATA SCIENCE: Customer Segmentation, 3.0

@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

@MicahHerstand
DATA SCIENCE: Big Data, Open Data, Linked Data

@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

@MicahHerstand
DATA SCIENCE: Big Data

@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

@MicahHerstand Designed by Forrester Research, accessed at bit.ly/sidata-bigdata

@MicahHerstand
Designed by Forrester Research, accessed at bit.ly/sidata-bigdata

@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)

@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

@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

@MicahHerstand
DATA SCIENCE: Growth Hacking
Graphic origin: bit.ly/sidata-gh-cartoon-2

@MicahHerstand
Graphic origin: bit.ly/sidata-gh-cartoon-3

@MicahHerstand
“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

@MicahHerstand
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

@MicahHerstand
DATA SCIENCE: Growth Hacking Example

@MicahHerstand
DATA ENGINEERING
Database
SQL (Relational)
NoSQL (Document, Graph)
Data warehouse

@MicahHerstand
DATA ENGINEERING: Database

@MicahHerstand
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?

@MicahHerstand
Structured data
Primary
key

@MicahHerstand
DATA ENGINEERING: Levels of structure
Graphic Origin: http://5stardata.info/

@MicahHerstand
DATA ENGINEERING: Database Keys

@MicahHerstand
DATA ENGINEERING: Database Security

@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

@MicahHerstand
Foreign
key
Movies

@MicahHerstand
Movies
Directors

@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

@MicahHerstand
DATA ENGINEERING: Data warehouse

@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

@MicahHerstand
DATABASE SETUP
Database manager application
Database Connections
SQL Helpers to Bookmark

@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

@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.

@MicahHerstand
DATABASE SETUP: DB Connection Info
Server: www.herstand.com
User: sistudents
Password: Hf68S9CpK67RUDV3
Database: simovies
Port: 3306 (default MySQL port)

@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

@MicahHerstand
PRACTICE SQL
English queries
Vocabulary
Stock SQL queries
Exercises

@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?

@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

@MicahHerstand
PRACTICE SQL: Anatomy of a Query
SELECT * FROM movies;
Result

@MicahHerstand
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

@MicahHerstand
SELECT * FROM movies
GROUP BY release_date
titleORDER BY ASC
DESC
Result
;

@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?

Data Science and Engineering for Marketers

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