Marketing Analytics
Technology Overview
Disclaimer:
• All logos, photos, etc. used in this presentation are the property of their respective
copyright owners and are used here for educational purposes only
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 1
Marketing Analytics Technology Overview
Category
Sample Companies*
Affiliate Marketing
Linkshare
Attribution Analytics
Adometry, Apsalar, VisualIQ
Big Data Analytics
Hadoop, Oracle RTD, Teradata
Customer Acquisition Analytics Angoss, Nettpositive, Vertex Group
Data Visualization
Leftronic, QlikView, Tableau Software
Direct/email Marketing Analytics Icontact, Litmus
Marketing Automation
Eloqua (Oracle), Marketo, Pardot
Marketing Intelligence/BI
IBM, PivotLink, Sybase (SAP)
Marketing Tools and Templates Demand Metric
Social Media Analysis
Radian6, SproutSocial, Visible Technologies
Statistical Software
R, SAS, SPSS
Web Analytics
CoreMetrics, Google, Omniture, WebTrends
*Notes:
• Sample companies; Not intended to be an exhaustive list
• Companies might belong to several different categories
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 2
Attribution Analytics
Attribution Model: Rule, Google, and Example
Attribution model: Rule that determines how credit for conversions and sales
should be assigned to touchpoints in conversion path
Google Attribution Modeling: Released Q1 2013
Source: https://support.google.com/analytics/answer/1662518?hl=en
Lisa finds site
from PPC ad
Returns to site
due to email
Returns to site
to buy product
Example: Customer Lisa finds your site by clicking on one of your PPC ads
She returns one week later by clicking over from your Facebook page.
One day later, she returns to the site due to one of your email direct marketing
campaigns.
Later that day, she returns to the site and purchases one of your products.
Which conversion point gets credit for the sale?
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 3
Attribution Analytics
Rule
Description and Application
Last Interaction
Description: 100% credit to final touchpoint
immediately preceding sales or conversion
Application: If ads are designed to attract people
at the moment of purchase; transactional
Last Non-Direct Click
Description: 100% credit to the last channel that
customer clicked through before buying
Application: To filter out direct visits and focus
on last marketing activity before buying
First Interaction
Description: 100% credit to the first channel with
which the customer interacted
Application: If you run campaigns to create initial
awareness
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 4
Attribution Analytics
Rule
Description and Application
Linear Interaction
Description: Equal credit to each channel interaction
Application: If campaigns are intended to maintain
contact through sales cycle
Custom Credit
Description: Create custom model
Application: Blend value of direct touchpoints
and marketing campaigns
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 5
Big Data Analysis
Category
Description
Definition
Process of examining large amounts of data
in a variety of types to uncover hidden patterns
and unknown correlations.
Purpose
Provide competitive advantages over rivals
through superior marketing and increased revenue
Data Sources
Web server logs, Internet clickstream data;
Social media activity reports, etc.
Goal
Make more informed business decisions and identify trends
not spotted with traditional BI tools
Source: http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 6
Big Data Analysis
Category
Description
Tools
NoSQL databases, Hadoop, MapReduce
Predictive analytics and data mining might not work
due to unstructured data
Hadoop
Apache Hadoop is open source software project
enabling distributed processing of large data sets
across many clusters of servers
Pitfalls
Lack of internal analytics skills
High cost of hiring experienced professionals
Challenges of integrating Hadoop systems & data warehouses
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 7
Customer Acquisition Analytics
Category
Description
Goal
Identify potential customers likely to buy now
Learn
Analyze sales and transaction data with data mining
and business intelligence software to understand
determinants of past successes
Predict
Develop a predictive analytics model
Apply
Compare sales leads and prospects against predictive model
to assess the likelihood of prospect purchase; also predict
timeframe for purchase
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 8
Data Visualization
Category
Description
Definition
Visual representation of data
for easier understanding and additional insight
Concept
Combine science of data and statistics with graphic arts
DPA
Data Presentation Architecture
Goal: Identify, locate, manipulate, format, and present data
Interaction
Lets users interact with data by seeing different representation
Example: See one company in context of industry
Example: See groups of companies in context of industry
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 9
Direct/Email Marketing Analytics
Category
Description
Direct Mktg.
Communicating directly with prospects: Email, Phone, Mail, …
Email most popular method
Email Tools
Benchmarkemail, ConstantContact, Vertical Response
Integration
Direct Marketing Analytics firms often integrate with Google
Attaching Google Analytics parameters to messages to track
Data
Open Rate; Breakdown of Glanced; Skimmed; Read
Device Usage: Apple Email; Outlook; iPhone; etc.
Referral: Tendency to forward email to others
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 10
Marketing Automation, with Analytics
Category
Description
Definition
Marketing automation tools
Manage multiple marketing campaigns, often including email
Analytics
CPL: Cost per lead; Cost basis for analyzing marketing ROI
Open Rates: Determine interest based on subject line
CTR: Click through rates
Conversions: Number of signups, downloads, purchases, etc.
Revenue: Track revenue by campaign
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 11
Marketing Intelligence/ Marketing Data Analysis
Category
Description
Definition
Information related to company’s markets
Gathered and analyzed for more informed decision making
Identify market opportunities
Increase market penetration (users) and development (product)
Focus
Uses data visualization and business intelligence (BI)
to support marketing decision-making
Apply knowledge from big data into market insights
Translate data into stories and information graphics
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 12
Marketing Analytics Tools and Templates
Category
Description
Definition
Practical resources for quick analyses
Tools
Pre-completed Excel spreadsheets for many common areas
Examples
Analysis of company vs. competitor ad & PR spending
Break-even analysis
Campaign impression calculator
Trade promotion analysis
Marketing dashboards
Email marketing ROI
Brand scorecard
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 13
Social Media Analytics
Category
Description
Definition
Measuring, analyzing, and interpreting interactions
Powerful tool to discover customer sentiment
Purpose
Develop brand; Drive revenue; Measure impact of programs
Radian6
Dashboard: Real-time monitoring of social media
Console: Highly scalable social web client
Integrates with Salesforce.com and Webtrends.com
Twitter, YouTube, MySpace, LinkedIn Answers, and more
Comprehensive; Powerful; Expensive: $600+ per user
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 14
Statistical Software
Category
Description
Definition
Specialized computer programs for statistical analysis
Often seen as advanced counterpart to general purpose tools
Companies
SAS: Market leader, especially in Fortune 500
SPSS: Strong in education market (IBM)
R: Open source
Others: StatPac, StatSoft STATISTICA, etc.
Uses
Advanced statistical techniques
Nonlinear functions; Multiple regression; Conjoint
Advantages
Powerful; Accurate; Specific tools
Disadvantages Command line interface; steep learning curve
Very expensive (except for R)
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 15
Web Analytics
Category
Description
Definition
Measurement, collection, analysis, reporting of Internet data
Purposes of understanding and improving web usage
Off-Site
Web measurement and analysis of general Internet areas
Measurement of potential audience (opportunity) ;
Share of voice (visibility); Buzz (comments)
On-Site
Web measurement and analysis of specific company website
Example: Conversion performance of landing pages
Compare data to KPI (key performance indicators)
Data used to improve website conversion performance
© Stephan Sorger 2013: www.StephanSorger.com; Marketing Analytics: Tech Overview 16