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 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? Lisa finds site from PPC ad Returns to site due to email Returns to site to buy product © 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