empowering the digital marketer with big data visualization · empowering the digital marketer with...

12
Insights from the DMA Annual Conference Preview Webinar Series Big Digital Data, Visualization and Answering the Question: Why? Featuring: Suneel Grover, Senior Solutions Architect, Customer Intelligence and Advanced Analytics, SAS Empowering the Digital Marketer With Big Data Visualization ›  Conclusions Paper

Upload: lethuan

Post on 18-Jul-2018

219 views

Category:

Documents


0 download

TRANSCRIPT

Insights from the DMA Annual Conference Preview Webinar Series Big Digital Data,

Visualization and Answering the Question: Why?

Featuring:

Suneel Grover, Senior Solutions Architect, Customer Intelligence and Advanced Analytics, SAS

Empowering the Digital Marketer With Big Data Visualization

›  Conclusions Paper

ContentsIntroduction ............................................................................ 1

A History of Fragmented Data and Analytics Tools ....... 1

Evolving Tools Enable Prescriptive Analysis .................... 2

Predictive Visual Analytics – an Example .......................... 2

Historical View ........................................................................................... 2

Predictive View .......................................................................................... 2

Improving the Forecast ........................................................................... 4

Illustrating Correlations and Regression .......................... 6

Conclusion .............................................................................. 9

Learn More ............................................................................. 9

About the Presenter

Suneel Grover is a Senior Solutions Architect, Customer Intelligence and Advanced Analytics, at SAS. Grover provides consulting services that blend marketing strategy (integrated/omnichannel, digital) with marketing analytics, visualization and customer intelligence technologies from SAS to solve complex business challenges in the media, entertainment, hospitality, communications, content and technology industries. In addition to his role at SAS, Grover is currently an adjunct professor at George Washington University, teaching in the MS in Business Analytics graduate program within the School of Business and Decision Science. Grover also serves on the program advisory committee for the Direct Marketing Association (DMA).

1

IntroductionIf you’re a digital marketer, you most likely share at least two common goals with all other marketers: working toward a holistic view of customers and predicting how customers will respond to your marketing efforts. In other words, you are ready to move beyond traditional Web analytics and seek true digital intelligence about your customers so you can answer such questions as:

• Whydocustomersinteractwithyourbrand?• Howdocustomersengagewithyourdigitalproperties(singleormultidomain)acrossmultipleexperiencesandtouchpoints?

• Whattypeofcontentdocustomersandprospectsspendthemosttimeengagingwithand,moreimportantly,why?

• Whichchannelsworktogethertoattractandcreatehigher-valuetrafficsegmentsfromanintegratedmarketingperspective?

• Whattypesofinteractionshavepredictivevalueinleading toconversions?

• Whatwillhappentodigitaltrafficifmoreadvertisingdollarsarespentononemediachannelversusanother–andviceversa?

Finding the answers to these questions has been difficult. Many digital-savvymarketershavelongbeenfrustratedintheirattemptstoachieveacomprehensive,forward-lookingunderstandingofcustomers. Part of the challenge has been the siloed nature and varying structures of digital data sources. Another part has been the limitations of Web analytics tools that aggregate and report on what happened in the past, but lack the sophistication of predictive marketing analytics.

The bottom line is that obtaining a holistic view of customers is a classic big data problem because it requires you to capture, prepare, manage, integrate and analyze huge amounts of digital data.Thegoodnews?It’sgettingeasiertodothosethings,thanksto developments in advanced analytics, data visualization and big data processing power.

In this paper – which is based on a webinar hosted by the Direct Marketing Association (DMA) and sponsored by SAS – we will take a look at how these technological advancements can enable you to become more predictive and prescriptive in your digital and integrated marketing efforts.

A History of Fragmented Data and Analytics ToolsMarketers have long had the challenge of stitching together different types of data and data sources to achieve their vision of integrated marketing. This includes:

• Internalfirst-partyCRMandtransactionaldata.• DigitalanalyticsdatafromWebanalyticsandadvertisingofferings

like Google Analytics, DoubleClick, Adobe Omniture or RightMedia.

• Third-partydatafromdatamanagementplatformslikeNielsen,BlueKai or X+1.

Unfortunately, it has been difficult to take advantage of such data sources because they have been stuck in silos. And marketers have struggled to bring together available online, offline and third-partydatainawaythatislogical,efficientandeasytouse.

Untilrecently,manymarketersdependedprimarilyonthird-partytools designed to use aggregated data to create reports that described what happened in the past. Obtaining an omnichannel, integrated view was extremely difficult. As a result, it was practicallyimpossibletogetadata-centric,comprehensiveviewof the customer that could feed integrated marketing analytics or, morespecifically,prescriptiveapproaches.

Whiledata-drivenmarketersandanalystshaveusedpowerfuladvanced analytics for many years to perform sophisticated analyses – such as regression, decision trees or clustering – they have been limited to using offline data, primarily due to restrictions on access rights to online data from third parties. On the flip side, Web and digital analytics tools primarily aggregated and reported on historical information and didn’t enable predictive analysis.

For the most part, Web and digital analytics tools are designed with the visualization of data as the primary driver for users, because data visualization enables a faster, deeper understanding of the insights and trends hidden within data in a more consumable manner. Their ease of use and visual appeal have helped marketers get a better understanding of the important trends and insights within data. And yet data visualization largely has been very descriptive in nature – that is, primarily about reporting, business intelligence and descriptive statistics.

2

For example, data visualization tools have provided basic charting techniques that allow marketers to see the distribution of males versus females, age bands, or the number of customers in specificstatesorgeographies.Thislevelofanalysiswillalwaysbeimportant; however, marketers today are asking bigger questions and need more sophisticated capabilities to take advantage of ever-increasingamountsofavailableconsumerdata.

Evolving Tools Enable Prescriptive AnalysisThere have been simultaneous advancements in advanced analytics, data visualization capabilities and the availability of big data. What’s more, predictive analysis is becoming more accessible to a wider community. In the past few years, there’s been a notable upswing in new and incremental abilities to process very large amounts of information. Data repositories – fromHadoopenvironmentstotraditionalrelationaldatabaseslike SAP, Teradata and Oracle – are getting bigger, stronger and faster. And now that it’s possible to handle very large amounts of information, we can approach digital data differently, no longer limited to using siloed digital data for basic retroactive analysis, ad hoc reporting and alerts.

Advancements in how we deal with big data allow us to take advantage of more sophisticated analytics. We’re starting toprogressfromdescriptiveanalysis(Whathappened?)todiagnosticanalysis(Whydidithappen?),predictiveanalysis(Whatwillhappen?)andprescriptiveanalysis(Howcanwemakeithappen?).Predictiveanalyticsanddataminingthriveon detailed data. When we can bring together very granular digital data streams that highlight consumer behavior and feed that into predictive models, we can improve our approaches to segmentation, ad targeting and customer experience management. This will enable marketers to take advantage of predictive digital analytic scoring and business process rules together to meet the challenge of prescriptive marketing within their automation platforms – including outbound, inbound and personalization systems.

Thebiggestvalueofdata-agnostic,advancedvisualizationplatforms is that they allow you to see things you could never before see using traditional digital tools. They are also extremely easy to learn, use and communicate results with. As the famous mathematician John W. Tukey said in his 1977 book Exploratory Data Analysis, “The greatest value of a picture is when it forces us to notice what we never expected to see.” Today we have an attractive opportunity to watch predictive analytic and visualization technology mesh together with positive implications for integrated marketers.

Predictive Visual Analytics – an ExampleTo illustrate how advanced visual analytics can help organizations improve their approach toward digital intelligence, let’s go through an example that brings together data from online and offline sources:

• Online data. Includes a Web analytics feed from SAS.com combined with digital advertising data from Google DoubleClick.

• Offline data.Includesthird-partymarketinglifestyleinformationabout digital visitors, associated with their location.

Nowlet’slookathowtrafficarrivesatSAS.com,bothfromahistorical and predictive perspective.

Historical ViewLet’s say that a manager asks, “What did our Web traffic look likeoverthelastfewmonths?”Wecangettheanswerinjusta few clicks (see Figure 1).

Predictive ViewNowsupposethemanagerasks,“What’sgoingtohappentoWebtrafficinthenexttwoweeks?”Inoneclick,wecanshowaforecastof expected site traffic of any duration – no coding required.

What’smore,thetechnologyuseschampion-challengerforecasting. That means that multiple forecasting algorithms are appliedtothedatainnear-realtime,andthealgorithmthatismoststatisticallyaccurateinfittingthedataisselectedforthevisualization. In other words, you get the most accurate result, no matter what your quantitative skill level is (see Figure 2).

3

Figure1:Historicaltrafficvisitationpattern.

Figure 2: Web traffic forecast.

4

Improving the ForecastWe can also improve how this model predicts future website traffic by providing more information from which it can learn. In Figures 1 and2,thevisualizationonlyrepresentedvisitorsbydate.Nowwe’lladd more data from originating visitor traffic sources – paid search, organic search and direct visitors who came to SAS.com without the stimulus of an advertisement.

By adding these three segments to the forecast model’s consideration,wecansee(inFigure3)thattheconfidenceinterval(i.e.,best-andworst-casescenarios)ofthepredictiongetsmuch tighter, showcasing accuracy improvement in the model’s prediction compared with the earlier iterations.

Asadigitalmarketer–andmorespecifically,adigitaladvertiserormedia planner – you have a limited amount of control over organic search traffic. You have more control over paid search, which is anad-centricchannel.Whatifweincreasedourpaidsearchadbudgetbyvaryingamounts?Whateffectwouldthathaveonoverallsitetraffic?That’sactuallyveryeasytoanswer.

In Figure 4, we have simulated a 35 percent increase in paid search advertising. Let’s see how this change will affect the traffic pattern forecastfortheentirewebsite.Withtoday’sever-changingadbudgets and short time windows, having the ability to simulate increases or reductions in ad spending in different marketing channels can be very valuable.

Figure 3: Add data to improve predictive accuracy.

5

Figure 4: Digital advertising simulation.

NowwehavetwonumbersrepresentingwebsitetrafficinFigure5. The baseline was the original prediction – 1,085. If we increase paid search by 35 percent, we can expect 1,323 visitors to the site. That means that a 35 percent increase in ad spending on paid search is predicted to produce a 22 percent increase in overall traffic over the next two weeks.

Figure 5: Simulation visualization.

Based on how your organization manages budgets and decisions, youcouldexploredifferentwhat-ifscenarios.Forexample,youcould determine if the impact of increasing paid search advertising by 25 percent or 45 percent would be worth the investment. This would be valuable information, indeed, for a manager or director.

Business question: What if we increase our paid search ad budget by 35 percent in the forecasted time period? How will that a�ect overall site tra�c?

Ability to inflate/deflate the impact of underlying factors by configurable time periods and percentage weights or constant values.

6

Illustrating Correlations and RegressionAs a second marketing application, let’s look at correlations. Correlationsarebeneficialwhenyoudon’tknowmuchaboutthedata and you want to improve your understanding of unexpected relationships or trends.

So let’s explore what factors may influence the quality of website conversions. For example, some conversions may generate high revenue, and others will generate low revenue. Once we understand the drivers of conversion, we may want to determine whatdriveshigher-qualityconversions.

Visitors come to SAS.com looking for information related to analytics, business intelligence or perhaps customer intelligence. When a visitor looks at a set of pages related to one of these product areas, it is indicative of that visitor’s contextual interest. One possible assessment we can make with correlation analysis is todetermineifcontextualinterestsinspecificproductareashaveany relationship to conversion quality. And that’s just skimming the surface of what we can learn.

We can easily drag and drop in data from contextual product interest categories, traffic source origination, site engagement depth,segmentation,adimpressionsandeventhird-partyinferreddata. Then in a few seconds, we will have a graphical depiction of where relationships are hot – shown below in white/pink/red.

Figure 6: Digital correlation analysis.

7

Nowwecanseeacorrelationbetweenvisitorengagementand conversion quality on our website. To dig deeper into thisrelationship,wecandouble-clickthecorrespondingbox highlighting the relationship, which launches a visual regression analysis, shown in Figure 7. The Y axis shows the quality of a site conversion, and the X axis demonstrates thedepthofavisitor’sengagement.Notethattrafficiscolor-coded,withdifferentcolorsrepresentingtrafficfrequency segments.

Nowlet’slookatthisfromasegmentationperspective–specifically,visitortrafficsource.Trafficsourcescanvary–social media, blogs, search, display, etc. – and we may want to identify trends in uniquely behaving traffic sources. In Figure 8, we can quickly see that direct visitation represents the highest volume of traffic to the website. But we can also see that the intensity of the regression line decreases

when we focus only on direct visitors (as compared to the overall population). That’s bad news from a marketing perspective, because it means that our largest segment of visitors needs to exhibit higher levels of engagement (pages viewed, time spent on-site)toachievethesamelevelofconversionqualitycomparedwith the overall population. This is a problem worthy of our attention.

To get our heads around this, we need to see a comparison. Figure 8 showcases how we can compare the analysis of the direct visitor segment on the left with a second visualization of the secondmost-voluminoussegment–organicsearch–ontheright.The organic search segment is behaving much more favorably, as indicated by the greater intensity of the regression line. These visitors are displaying a much lower amount of engagement – that is, they need to look at fewer pages and spend less time on the websitebeforetheystarttoconvertatahigher-qualitylevel.

Figure 7: Digital regression analysis.

8

Figure 8: Comparing segments.

This discovery is precisely what makes analytic data visualization so interesting, because it shows us exactly where we should focus our nextstepsandtime.Wecanconcentrateonfixingwhat’swrongwith the direct visitor segment or try to understand more about our organic segment so we can try to make our other segments look more like them. Discovery is key to prioritizing how we spend our time researching behavior and delivering prescriptive marketing recommendations.

9

ConclusionIn the past, siloed data gave marketers an incomplete view of their customers. The analysis tools that were available limited digital marketers to descriptive reporting of what happened in the past. With the latest advances in big data, advanced analytics and visualization, we can now perform holistic customer or prospect analysisusingvisualizationtoolsthataredata-agnostic,canintegrate data from multiple sources and provide robust analysis options. These new capabilities enable us to be more prescriptive in our digital marketing efforts so we can determine the best action to take to meet our business objectives.

Learn More ReadthewhitepaperSix Tips for Turning Big Data into Huge Insights.

To learn about SAS® Customer Intelligence solutions, visit sas.com/en_us/software/customer-intelligence.html.

Explore hot topics in marketing at Marketing Insights.

For fresh perspectives from other marketers, read our Customer Analytics blog.

Follow us on Twitter: @SAS_CI

To contact your local SAS office, please visit: sas.com/offices

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2014, SAS Institute Inc. All rights reserved. 106953_S116868.0214