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Marketing Analytics in a Week
1. Marketing Analytics in a Week
Stephan Sorger, The “Analytics Ambassador”
www.StephanSorger.com
2. ABOUT OUR SPEAKER
The “Analytics Ambassador”
Author - “Marketing Analytics: Strategic Models and Metrics” (2013)
Professional Expertise - VP Strategic Marketing, On Demand Advisors
Academic Expertise - Instructor at UC Berkeley, San Francisco Extension
Applying marketing analytics to grow revenue
Teaching Marketing Analytics since 2008
Board Member - Served on UC Berkeley Ext. Marketing Metrics Board
Website: http://www.stephansorger.com
LinkedIn: http://www.linkedin.com/in/stephansorger
3. ABOUT THE NEW BOOK
Authoritative Guide to Marketing Analytics
Over 10 years of professional experience
Over 5 years of academic research
Comprehensive
Nearly 500 pages of text
Nearly 400 figures, tables, and graphs
Practical
Structured around marketing and products, not math
Packed with examples
Available on Amazon.com:
Search on “Marketing Analytics”:
www.amazon.com/Marketing-Analytics-Strategic-ModelsMetrics/dp/1481900307
www.StephanSorger.com
4. ON DEMAND ADVISORS: PROCESS
2. Market
Definition
3. Lead
Generation
4. Lead
Management
5. Sales
Enablement
1. Revenue Engineering
4
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6. ON DEMAND ADVISORS: UPCOMING EVENTS
Revenue Engineering Workshops held every month: See
OnDemandAdvisors.com for complete schedule
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7. MARKETING ANALYTICS IN A WEEK AGENDA
Why a Week?
Monday: Defining the problem and building a business case
Tuesday: Selecting the people for the project
Wednesday: Preparing the technology and data
Thursday: Executing the analysis and computing the answer
Friday: Gaining insight and presenting the results
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Introduction
Practices & Pitfalls
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8. TRENDS DRIVING ANALYTICS ADOPTION
Online Data
Availability
Accountability
Improve productivity
Reduce costs
“What gets measured
gets done”
Data-Driven
Presentations
Data to back up proposals
Predict success of plans
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Marketing
Analytics
Adoption
Cloud-based data storage
Online = speed
Online = convenience
Reduced
Resources
Massive Data
Initiatives to capture customer
information
What to do with all that data?
Do more with less
Scrutinized budgets
Marketers must show outcomes
www.StephanSorger.com
9. MARKETING ANALYTICS ADVANTAGES
Persuade
Executives
Drive Revenue
Marketing as cost center
Marketing as profit center
Correlation between
spending & results
Save Money
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Old way: execute campaign
& guess outcome
No longer tolerate this
approach
New way: predict outcome
Marketing
Analytics
Advantages
Encourage
Experimentation
Test multiple scenarios before
proceeding
Run Simulations
Predict which will work best
Focus on revenue impact
from marketing
Correlation between
spending & results
Side-Step
Politics
Some CEOs do not appreciate
marketing
Show impact of efforts with
metrics
www.StephanSorger.com
10. WHAT IS MARKETING ANALYTICS?
“It’s a Wall!”
It must be Big
Data!
“It’s a Fan!”
It must be Social
Media!
“It’s a Rope!”
It must be Predictive
Analytics!
“It’s a Snake!”
It must be Marketing
Automation!
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“It’s a Tree!”
It must be Google Analytics!
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11. THE MARKETING ANALYTICS FRAMEWORK
Market
Analysis
Competitive
Analysis
Strategy and
Operations
Marketing Mix
The 4 Ps
Sales and
Support
Chapters 1-3
Chapter 4
Chapters 5-6
Chapters 7-10
Chapter 11
Segmentation
Targeting
Positioning
Competitive Analysis
Forecasting
Big Data
Predictive Anyl.
Conjoint
Google Analytics
Social Media
Marketing Auto.
Strategic
11
Analytics
in Action
Chapter 12
Tactical
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12. WHY A WEEK?
Project Scope vs. Appetite for Quick Results:
Day(s): Not credible for all but the most trivial projects
Week(s): OK for small initiatives; Easy to digest
Month(s): OK for medium initiatives; Perception of “major project”
Year(s): OK for large initiatives; Significant risk management required
Monday
Wednesday
Thursday
Friday
Defining the
problem and
building the
business
case
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Tuesday
Selecting the
people for
the project
Preparing
the
technology
and data
Executing
the analysis
and
computing
the solution
Gaining
insight and
presenting
the results
www.StephanSorger.com
13. RUNNING EXAMPLE
Topic
Description
Example
Straightforward marketing analytics project
Performed at a Fortune 500 enterprise software firm
Problem
Assess customer satisfaction of major accounts
Constraint
Little budget availability for customer sat survey
Approach
Correlate customer sat with existing data
Time
ASAP
Regional
Office
13
Headquarters
Regional
Office
Customers
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15. MONDAY
Topic
Description
Define Problem
State Problem to be Solved
Completed to Estimate Project Scope
Build Business Case
Estimate Cost Savings or Other Benefit
Completed to Obtain Budget for Project
Monday
Define problem
15
Build business
case
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16. BEST PRACTICES: PROBLEM DEFINITION
Topic
Description
Problem Definition
Describe clearly the problem to be solved
X: “Gauge customer satisfaction”: Too vague
OK: “Determine predictive indicators for defection.”
Success Criteria
Define success criteria
X: “Done once data is collected”: No outcome
OK: “Show correlation at 95% confidence”
Business Case
Estimate savings expected vs. cost
X: “Will improve customer sat.”: Too vague
OK: Estimate hard and soft costs
16
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17. POLL: PROBLEM DEFINITION
Question
Score
How many of you have encountered the following:
Project proposals without clear problem definitions?
_____
Project proposals without success criteria?
_____
Project proposals without dollar-based business cases?
_____
VOTE
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18. RUNNING EXAMPLE: PROBLEM & BUSINESS CASE
Topic
Description
Problem Definition
Determine existing indicators of imminent defection
Business Case
See below
Category
Computation
Hard Savings:
Regional data collection
20 reg. mgrs. * 3hr/ea * $100/hr
Soft Savings:
Customer sat
1 lost customer
$100,000/hr
Hard Cost:
Marketing analyst
40 hr * $100/hr
($4,000)
Net Savings
Subtotal
$6,000
$2,000
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20. TUESDAY
Topic
Description
Core Team
Statistical modeler: M.S./Ph.D. math or econ;
SAS/SPSS
Data Analyst: B.S.; SAS/R/Pig/SQL; Large data sets
Analytics SW Developer: OO; Scrum/Agile; SQL
Extended Team
Project Leader
Business analyst(s)
Evaluator(s)/ Tester(s)
Core Team
Statistical Modeler
Extended Team
Analyst
Project Leader
Evaluator(s)
Developer
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Business Analyst(s)
Source: Roldan, Alberto: “Implementing Business Analytics.” Atomai blog. May 5, 2010.
Link: http://atomai.blogspot.com/2010/05/implementing-business-analytics.html
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21. SATISTICAL MODELER: SAMPLE
Senior Statistical Modeler: SunTrust; Atlanta, GA
Responsibilities:
Develops or analyzes quantitative models.
Researches best practices and new technologies.
Performs complex analysis and draws conclusions.
Responsible for the analysis and/or development of quantitative models both financial and non-financial in support of the
company’s risk management effort.
Consults with practitioners, the academic community, and other financial institutions in researching the development of risk
management models.
Qualifications:
Masters/PHD degree in a in a quantitative field such as Mathematics, Statistics, Econometrics, Actuarial Science or
Engineering.
Programming skills (SAS, Matlab, Visual basic).
Demonstrated mastery of quantitative modeling requirements for non-parametric type of models.
4+ experience in building Basel compliant models and involved in the entire life-cycle of building models.
Basic understanding of financial statements.
22. DATA ANALYST: SAMPLE
Data Scientist: Cisco; San Bruno, CA
Responsibilities:
The Data Scientist will apply disciplined analysis to explore and develop new techniques for identifying and mitigating
internet security threats (spam, malware, etc.).
Deliverables include research proposals, research documents describing a technique and quantitative measures of
expected efficacy improvement, prototypes, functional specifications and ad-hoc measurement tools
As a leading team within Cisco STG, the Analysis Team is responsible for developing new techniques to identify and
mitigate network security threats, as well as for assessing the efficacy of those techniques in defending against security
threats.
Qualifications:
5+ years of big-data experience including applied techniques in data mining, machine learning, or graph mining.
Experience with Hadoop, Hive, MapReduce, or column stores, as well as working with large, unfiltered data sets.
Able to persuade stakeholders and champion effective techniques through product development.
Understanding of network security, including email and/or web threats highly desirable.
Proficiency with Unix and databases, as well as working knowledge of PERL or Python.
Advanced degree in a relevant field is desirable.
23. ANALYTICS SOFTWARE DEVELOPER: SAMPLE
Software Engineer, Analytics Big Data Quality: Salesforce.com; SF, CA
Responsibilities:
Understand and perform analysis on the unique requirements in on-demand multi-tenancy model for Analytics tools assuring
that changes to existing functionality are truly required and correctly deployed.
Participate in the scrum team under our agile development process utilizing principles such as test-driven-development
Perform both functional manual/automated testing of application features using automation tools such as Selenium and JUnit
and extensive white-box testing through an application program interface (API).
Qualifications:
Experienced. Experienced using automation frameworks such as Selenium and JUnit, coming up with comprehensive test
plans and tests cases, as well as hands on experience with Java programming and testing.
Having BI tool testing experience is definitely a big plus.
Highly technical. Strong background in Object-Oriented programming concepts and constructs.
Solid knowledge of SQL and understanding of relational database schema design.
Testing expert. Industry experience in testing on various types of browsers (Google Chrome, Firefox, IE) and web
technologies, such as HTTP, XML, Javascript, HTML5, and CSS3.
In depth knowledge of SQA methodologies, tools and approaches (black box, white box and automated testing experience) in
testing multi-tier scalable applications.
24. ANALYTICS PROJECT LEADER: SAMPLE
Analytics Project Manager: NYC Dept. of IT and Telecomm; Brooklyn, NY
Responsibilities:
Manage Citywide Performance Reporting (CPR)/Analytics platform support releases and new application development projects
Lead the Analytics Production Support team on initiatives necessary to maintain and support the platform for City agencies
Manage vendor relationships performing ongoing Analytics support and development work, Security, PMQA, independent
contractors and similar engagements, including the creation of RFPs, review/selection of vendors, etc.
Ensure that applications are stable and maintainable;
Provide information to the public upon request and approval of executive management
Qualifications:
3+ years’ experience managing large projects (end-to-end)
Knowledge of SDLC and/or Agile;
2+ years’ experience in Vendor management, WBS creation, Project and resource planning
Proficiency in Microsoft Project and other project management software
Business analysis experience creating requirements, use cases, functional specifications preferred
Experience with Oracle Business Intelligence Enterprise Edition (OBIEE);
PMP certification; experience working with City of New York agencies
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25. BUSINESS ANALYST: SAMPLE
Business Analyst: Magenic; San Francisco, CA
Responsibilities:
Developing use case based requirements specifications to capture project business requirements
Managing functional and non-functional requirements artifacts through all development and QA iterations
Facilitation of requirements analysis sessions with project stakeholders
Collaboration with project stakeholders to establish requirements baseline.
Stakeholders include client business team, Magenic development team, third party development teams, QA team
Qualifications:
Hands on experience as a business analyst in a software production environment
Must have experience working with end users and/ or product owners
Ideally, some level of experience developing software
TFS ideally, and an understanding of how to use it to drive requirements <TFS: Microsoft Visual Studio Team Foundation
Server>
Expression Blend experience a plus <Microsoft Expression Blend: Software UI Tool>
A sense of humor and perspective
Experience with Agile, or Agile-based, development methodologies
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26. EVALUATOR/TESTER: SAMPLE
Analytics Software Tester: JMP (SAS); Cary, NC
Responsibilities:
As a JMP Analytics Software Tester, you will validate statistical features of JMP.
Interact directly with developers to test the numerical accuracy of statistical algorithms during the development life cycle.
Ensure quality and functionality of software code that is used to make critical decisions.
Understand the needs of JMP's customer base and give usability feedback in order to make data-based analytical problem
solving accessible to a wide audience.
Research technical literature, maintain test scripts and participate in the documentation review process
Qualifications:
Master's degree in statistics or a related quantitative field including extensive coursework in mathematics.
2 or more years of experience using JMP in a professional capacity.
Ability to think analytically and to effectively communicate problems and suggest fixes.
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27. TUESDAY
Topic
Description
Statistical modeler
Data analyst
Analytics SW developer
Project Leader
Business Analyst
Evaluator
+Executive Sponsor
Core Team
Statistical Modeler
Developer
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No dedicated modeler due to simple model
Data analysis done by product manager
No dedicated developer due to simple model
Leadership done by product manager
Worked with financial business analyst to get data
Testing done by product manager
VP Products
Extended Team
Analyst
Project Leader
Business Analyst(s)
Evaluator(s)
www.StephanSorger.com
30. DATA ANALYSIS: PREPARATION
Step
Selection
Pre-Processing
Transformation
Data
Select portion of data to target
Data cleansing; Removing duplicate records
Sorting; Pivoting; Aggregation; Merging
Data Mining
Interpretation
Selection
Description
Find patterns in data
Form judgments based on the patterns
Pre-Processing
Target
Data
Transformation
PreProcessed
Data
Data Mining
Transformed
Data
Interpretation
Patterns
Actionable
Information
www.StephanSorger.com
31. POLL: DATA PREPARATION
Question
Score
How many of you have encountered the following:
Problems with selecting the right data to analyze?
Problems with pre-processing the data? (de-duping, etc.)
Problems with transforming the data? (merging, etc.)
_____
_____
_____
VOTE
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32. RUNNING EXAMPLE: DATA ANALYSIS PREP
Step
Description
Selection
Pre-Processing
Transformation
Limit data to customers served by regional centers
Remove duplicate records
Merged two databases
Selection
Data
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Pre-Processing
Target
Data
Transformation
PreProcessed
Data
Data Mining
Transformed
Data
Interpretation
Patterns
Actionable
Information
www.StephanSorger.com
34. DATA ANALYSIS: EXECUTION
Step
Selection
Pre-Processing
Transformation
Data
Select portion of data to target
Data cleansing; Removing duplicate records
Sorting; Pivoting; Aggregation; Merging
Data Mining
Interpretation
Selection
Description
Find patterns in data
Form judgments based on the patterns
Pre-Processing
Target
Data
Transformation
PreProcessed
Data
Data Mining
Transformed
Data
Interpretation
Patterns
Actionable
Information
www.StephanSorger.com
35. POLL: DATA MINING
Question
Score
How do you analyze data for patterns:
“Eyeball it”: Look over columns of numbers and identify patterns
“Sort it”: Sort the data and examine trends
“Analyze it”: Conduct regression or other types of analysis
_____
_____
_____
VOTE
35
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36. RUNNING EXAMPLE: DATA ANALYSIS - EXECUTION
Step
Description
Data Mining
Pre-Processing
Transformation
Limit data to customers served by regional centers
Remove duplicate records
Merged two databases
Selection
Data
36
Pre-Processing
Target
Data
Transformation
PreProcessed
Data
Data Mining
Transformed
Data
Interpretation
Patterns
Actionable
Information
www.StephanSorger.com
38. COMMUNICATIONS WITH ANALYTICS: BEFORE
Engineering Department Status
Engineering resources are very low; definitely need more engineers
Some engineers working many hours per week
Engineers risk getting burned out from working so many hours
New projects coming up will require more resources than we have
Engineering resource types
Engineering resource type A: have 10 engineers; need at least 12
Engineering resource type B: have 3 engineers; need at least 4
Engineering resource type C: have 5 engineers; need at least 6
Engineering resource type D: have 15 engineers; need at least 20
Possible slips to schedule can occur unless we hire more engineers
Recommend hiring at least 2 additional engineers in next month
Many engineers complaining to their management about workload
www.StephanSorger.com
39. COMMUNICATIONS WITH ANALYTICS: AFTER
Department Revenue and Resources
Professional Services Organization Department Status
Will Stop Producing Incremental Revenue
Here
Current Resource Level
Projected Revenue
Revenue to Date
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
40. RUNNING EXAMPLE: DATA PRESENTATION
Step
Description
Conclusion
Secondary Outcome
Money Savings
Increased Accuracy
Primary Outcome
40
Problem solved; Correlated variable identified
Big deal in enterprise software world
Ghost-wrote article; “Authored” by EVP
Company positioned as expert in analytics
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41. KEY TAKE-AWAYS
Monday: State clear definitions, success criteria, and business cases
Tuesday: Identify the right people for the job
Wednesday: Adopt skill sets in preparing and merging data
Thursday: Be on the lookout for patterns in data; Be open to new ones
Friday: Develop presentations that scream Action and Insight
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43. SPONSOR
Act-On is a leading provider of integrated marketing automation software.
Using Act-On, more than 1700 companies tie inbound, outbound and nurturing programs together -across email, web, mobile, and social -- and achieve a superior Return on Marketing Investment.
www.act-on.com
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44. HOST
Demand Metric is a marketing advisory firm serving a membership community of over 30,000
marketing professionals and consultants in 75 countries with consulting methodologies, advisory
services, and a library of 500+ premium marketing tools and templates.
These tools allow Demand Metric members to plan more efficiently and effectively, and answer the
difficult questions about their work with authority and conviction. Demand Metric tools enable
members to complete marketing projects more quickly and with greater confidence, boosting the
respect of the marketing team and making it easier to justify resources the team needs to succeed.
www.demandmetric.com
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