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Building Digital Marketing Measurement Systems
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Russ Merz, Ph.D.
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Presentation given to EMU's Center for Digital Engagement Summer Clinic.
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Building Digital Marketing Measurement Systems
1.
Building Digital Marketing Measurement
Systems Russ Merz, Ph.D. Professor of Integrated Marketing Communication Research Scientist and Analytics Consultant The Center for Digital Engagement © Russ Merz, Ph. D., 2016
2.
Agenda § Why do Digital Marketers Need Measurement Systems? § How Are Measurement Systems Built? §
What are the Important Parts of Each Stage in the Process? § What is the Role of Digital MarkeKng Technology? 2 The Center for Digital Engagement © Russ Merz, Ph. D., 2016
3.
“If you don’t measure it, you can’t manage it” • OK, everybody has heard this trite phrase,…is it true? • If it is, then it is only a necessary condiKon, not a sufficient one…because, “What you choose to measure has a huge influence on the nature of your business” •
Sounds good, but is it? • SomeKmes managers will measure the things they manage, which might be good, but only if they are measuring the right things? • If they aren’t, then they will become what they measure and probably make bad decisions…so then, “What you choose to measure, and the quality of the measures used, affects the financial yield of your management efforts and your business” • Now this sounds beTer because quality measures are Ked to an outcome…but, • Do you know how to do it? Why do Digital Marketers Need Measurement Systems? 3 © Russ Merz, Ph. D., 2016
4.
How Are Measurement Systems Built? The process for building digital markeKng measurement systems (DMMS) can be summarized in terms of four key steps: Design Develop DeployDiagnose The goal here is to thoroughly understand how the business is using digital markeKng to achieve desired performance outcomes: •
What is the digital markeKng strategy model? • What assumpKons are being made? • How are acKons and results currently measured? • Who are the audiences? • What are the SWOTs? Revise and modify the exisKng measurement system by: • IdenKfying business goals for each digital markeKng strategy, • Mapping goals to KPIs, • Se[ng target performance levels for each KPI metric, • Specifying how KPIs are related (“cause and effect”), • Determine how metrics will be sourced and collected, • PrioriKzing audience segments. Implement the measurement plan by: • CollecKng the relevant metrics from data sources, • Organizing and classifying metrics according to role in data model, • ConducKng iniKal analysis required by model, • Assess reliability and make changes as needed, • Perform analysis by segments as needed. Report findings and communicate insights by: • Making formal presentaKons of the results to key stakeholders, • CreaKng customized manager tools such as dashboards, scorecards, or calculators, • Showing how results can be used to improve decision making, • IdenKfy areas for change. Measurement Strategy Measurement TacKcs Measurement AcKons Measurement Outcomes 4 © Russ Merz, Ph. D., 2016
5.
Diagnose the Digital MarkeKng Strategy A good starKng point for diagnosing the digital markeKng strategy of a business is to compare the business’s strategy against a norma&ve model like the generic version below. Use the comparison to idenKfy what is present, and what is missing, from the digital markeKng strategy being evaluated. 5 Diagnose © Russ Merz, Ph. D., 2016
6.
Diagnose the Digital MarkeKng Strategy TIP: Some templates for various types of digital markeKng strategy models can be found in digital publicaKons. They can be useful as starKng points for diagnosis. For example, this framework from Search Engine Watch presents a systemaKc plan for building a social media strategy measurement plan. (source: hTp://searchenginewatch.com/sew/ how-to/2202307/social-media-roi-how- to-define-a-strategic-plan). 6 Diagnose © Russ Merz, Ph. D., 2016
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Design the Measurement System Aher the digital markeKng strategy model is mapped out, then the next step is to designate the measurement tac&cs to be used. Here’s an example of an approach primarily intended for public relaKons pracKKoners developed by the Associa&on for the Measurement and Evalua&on of Communica&on (AMEC: www.amecorg.com). It consists of the five communicaKon effects shown below. 7 Design © Russ Merz, Ph. D., 2016
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Examples of Metrics that can be used for each model stage and type of media. Social Media Metrics Website Metrics 8 Design the Measurement System Design © Russ Merz, Ph. D., 2016
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TIP: Use a worksheet to organize your planning. Here’s a useful one from AMEC. 9 Design the Measurement System Design © Russ Merz, Ph. D., 2016
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• AnalyKcs are metrics and models that provide acKonable insight. • Metrics are measurements represenKng performance levels of important variables to an organizaKon (KPIs). They are used to monitor situaKons and diagnose problems. The dashboard in your car is an analogy. •
Models are tools that combine metrics to aid managers in making decisions. They simplify and represent the real world along a dimension of interest (e.g. customer experience). An analogy might be a GPS system in your car. • Both Metrics and Models are needed for Insight. AnalyKcs Metrics (Measures, KPIs) Models (Decision Tools) AcKonable Insight AnalyKcs-Based Measurement Systems Combine Metrics with Models to Generate AcKonable Insights for BeOer Decision Making Metrics + Models = Insight 10 Design the Measurement System Develop © Russ Merz, Ph. D., 2016 Adapted from: Sorger, Stephan (2013) MarkeKng AnalyKcs: Strategic Models and Metrics
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Metrics: • Can be “Hard” (observable/countable) ü Financials, units sold, clicks, page views, bounce rates, etc. •
Can be “Soh” (unobservable) ü Measures on a survey ü Scaled – Nominal/Categorical (e.g., gender) – Ordinal (GT or LT) – Interval – RaKo ü Open-ended • Ohen Displayed in Dashboards ü Grouped and Color coded ü AutomaKcally updated • Provide performance insight ü Monitored and tracked Models: • Have Different “Styles” ü Verbal ü Pictorial ü MathemaKcal • Have Different “Forms” ü DescripKve/ComparaKve ü PredicKve ü NormaKve • Can be “Built” with ü InducKve methods (data determines theory), or, ü DeducKve methods (theory determines data) • Require at least, ü One dependent and one independent variable [ Y = ƒ(X) ] Necessary, But Not Sufficient Required for Decision-Making Value 11 Design the Measurement System Develop © Russ Merz, Ph. D., 2016 Adapted from: Sorger, Stephan (2013) MarkeKng AnalyKcs: Strategic Models and Metrics
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12 Design the Measurement System Develop TIP: The value of the insight created from the measurement system is largely a funcKon of the types of models used. Measurement systems that integrate metrics into wholisKc views of strategy performance are more useful for decision-making. © Russ Merz, Ph. D., 2016
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The integraKon of metrics has gone through at least three phases of evoluKon: ü Phase 1: Dials on a Dashboard—good, but not true integraKon, it only integrates metrics by presenKng them side by side. ü Phase 2: Trend line comparisons— beOer, because it incorporates a temporal perspecKve of changes in metric values. ü
Phase 3: Quan:fied Rela:onships— best, because it computes a mathemaKcal value for the “cause and effect” relaKonship between metrics. Satisfaction, On-Line Purchase Intentions and Revenues over Time -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 4/21/2003 4/28/2003 5/5/2003 5/12/2003 5/19/2003 5/26/2003 6/2/2003 6/9/2003 6/16/2003 6/23/2003 6/30/2003 7/7/2003 7/14/2003 7/21/2003 7/28/2003 8/4/2003 8/11/2003 8/18/2003 8/25/2003 9/1/2003 9/8/2003 9/15/2003 9/22/2003 9/29/2003 10/6/2003 10/13/2003 10/20/2003 10/27/2003 Weekly Revenue Dates PercentageChange Lagged %ChngPOL Lagged %ChngSAT %ChngREV Design the Measurement System Develop © Russ Merz, Ph. D., 2016
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14 Design the Measurement System Develop The central tasks in designing a measurement system are: (1) determining the metrics to be used, (2) idenKfying the role of the metrics, and (3) specifying the “cause and effect” pathways between the metrics (if any). This “hypotheKcal model” forms the basis for the development of the measurement system. © Russ Merz, Ph. D., 2016
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Example: A soh drink brand wanted to know if the traffic generated by the a branded “naKve ad” based website as measured by Google AnalyKcs would affect the volume of social media conversaKons related to the “NaKve Ad”, across a specified Kme period. 15 DescripKve Analysis of the Data: The correlaKon matrix showed the direcKon of the relaKonships between the predictor and target variable sets. Design the Measurement System Develop PredicKve Modeling Results: The results supported the hypothesized relaKonships between the branded “naKve ad” campaign and social media conversa&ons related to the campaign. • The results demonstrated a significant impact of Google AnalyKcs metrics onto all three sets of brand related conversaKons. © Russ Merz, Ph. D., 2016
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16 Design the Measurement System Develop Example: XXX soda wanted to know if social media conversaKon topics related to the psychographic characterisKcs of potenKal XXX users could predict (1) XXX brand related conversaKons, and (2) XXX website traffic, using 120 4-hour Kme periods. PredicKve Modeling Results: The results showed that the psychographic topics were predicKve of the specific XXX brand related conversaKons, and moderately predicKve of website traffic. In addiKon, XXX brand conversaKons were are also jointly predicKve of website traffic. DescripKve StaKsKcs: The summary staKsKcs of the data used in the analysis show relaKve sizes of the counts for each metric. © Russ Merz, Ph. D., 2016 Visits SKckiness Engagement
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17 Design the Measurement System Deploy Deploying the measurement system is the structuring and organiza&on of the metrics and modeling results so that they facilitate digital markeKng decision making. ü Transforming data to informa&on is sufficient for monitoring performance levels. ü But, if the measurement system will be used for prioriKzing changes and allocaKng resources, then knowledge about how changes in one or more metrics affects another metric is necessary. ü
Thus the managerial uses of the measurement system determine the type of deployment. Raw Data InformaKon about Performance Knowledge about Cause and Effect DescripKve/ComparaKve PredicKve/PrescripKve © Russ Merz, Ph. D., 2016
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Dashboards are useful tools for presenKng descripKve or comparaKve analysis results. ü Dashboards are basically reporKng and monitoring tools that allow the display of various website metrics or Key Performance Indicators (KPIs). ü They do not show the user how changes in one metric effects changes in another metric. ü
It is up to the user to make connecKons between metrics based on observaKon rather than quanKtaKve methods. ü There are many vendors providing SaaS based dashboards that allow you to decide what to display, when the metrics are displayed, and how the metrics are organized by using special widgets. Design the Measurement System Deploy © Russ Merz, Ph. D., 2016
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Key Success Indicator (KSI) "What if" Calculator Change PW Online
Subscriptions 72423 979 Activity Cost Savings 235,375.64$ Change PW 3.25$ 5,248.41$ Newsletter Enrollments 2.75$ Online Registrations Online Purchase Plan CSI 2,446.92$ Online Subscriptions 2.50$ 175000 2537 0.88 2,836.65$ Balance Transfers 4.25$ Weekly Enrollments Resulting from 245,907.63$ Weekly Total Savings less call center and mail service 1909 667 Newsletter Account Transfers Enrollments Instructions: Enter the desired values in the blue boxes. When knowledge is needed to inform the decision maker about how changes in metric performance is needed then “what if” tools can be built to embody the analysis results. ü For instance this online banking client wanted to know what total customer service cost savings would be realized under different online registraKon scenarios and with different cost savings per online acKon. Design the Measurement System Deploy © Russ Merz, Ph. D., 2016
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Based on the modeling results a “what if” calculaKon tool was built for an ecommerce client. ü It demonstrates the changes in conversion levels that can occur with changes in website customer saKsfacKon. ü The examples show the effects of a 5 point gain in saKsfacKon (65 to 70) on the rest of the variables in the causal network. Design the Measurement System Deploy © Russ Merz, Ph. D., 2016
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21 The Role of MarkeKng Technology in DMMS Back-End Data CollecKon/ Processing Technology Refining or AnalyKc Technology Front-End Technology User Interface Can the capabiliKes to build out a digital markeKng measurement system (DMMS) be purchased off-the-shelf from a vendor? ü The recent explosion in the number of marke&ng technology providers allow companies to create a so-called marke&ng stack to provide the basic components of a digital markeKng measurement system. ü Three basic sub-plavorms would be needed and can be purchased from various vendors. Cloud-based data storage and processing tools; in PaaS, IaaS, form Data analysis and modeling tools; R, SAS, SPSS, etc. in SaaP form ReporKng and presentaKon tools; Excel, iDashboard, in SaaP form © Russ Merz, Ph. D., 2016
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MarkeKng Technology Landscape Unfortunately, this will not be an easy task given the mindboggling number of vendors. According to ScoT Brinker at chiefmartech.com , as of March 2016 there are currently 3874 markeKng technology (MarTech) soluKons1—expanding from only 150 in 2011! Source: hIp://chiefmartec.com/2016/03/marke&ng-technology-landscape-supergraphic-2016/ 1 SoluKons do not equal companies, some companies are in mulKple categories. 22 © Russ Merz, Ph. D., 2016
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MarkeKng Technology Landscape They can be roughly organized into six MarTech capability categories, each with a number of sub-categories—represenKng a overwhelming number of players and choices. Source: hIp://chiefmartec.com/2016/03/marke&ng-technology-landscape-supergraphic-2016/ 23 © Russ Merz, Ph. D., 2016
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24 Summary and QuesKons This presentaKon has aTempted to provide you with a brief overview of a generic process for building out a digital markeKng measurement system (DMMS). • The process as described is composed of four basic stages: ü Diagnoses—the determinaKon of the underlying digital markeKng strategy model to be measured; ü
Design—the idenKficaKon of the metrics necessary for understanding how well the strategy is performing; ü Develop—the applicaKon of analysis tools for calculaKng the values of the metrics, and impacts of those metrics, related to key outcomes expected from the digital markeKng strategy measured; and, ü Deploy—the idenKficaKon of appropriate presentaKon methods for insuring appropriate levels of communicaKon and usage of the DMMS by decision makers. Any QuesKons? © Russ Merz, Ph. D., 2016