1The University of DundeeSchool of ComputingMSc Business Intelligence ProjectTitle: End To End Predictive Analytics Implem...
TABLE OF CONTENTSExecutive Summary ……………………………………………………… 4Acknowledgements …………………………………………………….... 5Certificate ………………………...
7. Evaluation ……………………………………………………………… 47Evaluate Results …………………………………………………………….. 47Review Process ………………………………………………………...
Executive SummaryThe recognition of data as an organisational strategic asset continues to increase acrossindustries, thou...
Table of FiguresFigure 1: Last Click Digital Marketing Measurement Model …………………………. 11Figure 2: The Data Mining Process -...
1. OverviewIntroductionGone are the days when organisations could happily rely on mass reach advertisingchannels such as t...
In line with commonly accepted estimates around the development of business intelligencesystems (Williams, 2011 p.57), the...
across the digital marketing industry to measure the commercial contribution of digitalchannels, commonly referred to as t...
Aim and ObjectivesThe aim of this dissertation is to provide a practical business intelligence framework thatallows digita...
9. ConclusionsThe digital marketing industry is quickly becoming a dominant commercial channel in theUK, though it can be ...
10. Future WorkTraditional data warehousing technologies can be deployed to deliver a practical solution tothe challenges ...
11. ReferencesBorle, S., Singh, S. & Jain, D. (2008) Customer Lifetime Value Measurement. ManagementScience. [Online]. Ava...
Inmon, W.H. (2005) Building the Data Warehouse. 4thEdition. Wiley Publishing Inc.Internet Advertising Bureau. (undated) H1...
Whitehorn, M. (2011) Modelling a BI System – MSc Business Intelligence Lecture Notes.Available from: https://my.dundee.ac....
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M sc bi thesis rafael garcia navarro summary

  1. 1. 1The University of DundeeSchool of ComputingMSc Business Intelligence ProjectTitle: End To End Predictive Analytics Implementation in the Digital AdvertisingIndustrySupervisor: Dr. Iain MartinDate: 16thJanuary 2013I declare that the special study described in this thesis has been carried out and thethesis composed by me, and that the thesis has not been accepted in fulfilment ofthe requirements of any other degree of professional qualificationRafael Garcia-Navarro
  2. 2. TABLE OF CONTENTSExecutive Summary ……………………………………………………… 4Acknowledgements …………………………………………………….... 5Certificate …………………………………………………………………. 6Confidentiality Agreement ………………………………………………. 7Table of Figures …………………………………………………………… 8Table of Tables ……………………………………………………………. 81. Overview ………………………………………………………………. 9Introduction …………………………………………………………………… 9The State of Art in the Digital Marketing Agency Industry ………………. 10Aim and Objectives ………………………………………………………….. 122. Methodology …………………………………………………………... 13Background …………………………………………………………………... 13Implemented Data Mining Methodology …………………………………... 153. Business Understanding …………………………………………….. 15Determine Business Objectives ……………………………………………. 15Assess the Situation …………………………………………………………. 16Determine Data Mining Goals ……………………………………………… 16Produce Project Plan ………………………………………………………... 164. Data Understanding ………………………………………………….. 18Collect Initial Data …………………………………………………………… 18Describe Data ………………………………………………………………... 19Explore Data …………………………………………………………………. 20Verify Data Quality …………………………………………………………... 225. Data Preparation ……………………………………………………… 22Select Data …………………………………………………………………… 22Clean and Construct Data ………………………………………………….. 24Data Warehouse Data Modelling ……………………………………… 25Extract Transform and Load (ETL) ……………………………………. 30Integrate and Format Data …………………………………………………. 366. Modelling ………………………………………………………………. 41Select Modelling Techniques ………………………………………………. 41Generating Test Design …………………………………………………….. 41Build Model …………………………………………………………………… 42Assess Model ………………………………………………………………… 46
  3. 3. 7. Evaluation ……………………………………………………………… 47Evaluate Results …………………………………………………………….. 47Review Process ……………………………………………………………… 49Determine Next Steps ………………………………………………………. 498. Deployment ……………………………………………………………. 49Plan Deployment …………………………………………………………….. 499. Conclusions ……………………………………………………………. 5010. Future Work …………………………………………………………… 5111. References ……………………………………………………………. 53AppendicesAppendix A – Project Plan …………………………………………………….... 56Appendix B – Doubleclick Data Definition Tables ……………………………. 57
  4. 4. Executive SummaryThe recognition of data as an organisational strategic asset continues to increase acrossindustries, though digital marketing agencies have not traditionally embraced existingtechnologies to address some of the key areas where more robust approaches are expectedand demanded by clients – channel contribution analysis and campaign response prediction.Traditional technologies such as Microsoft SQL Server, Visual Basic, T-SQL and R can beeffectively used by digital marketing agencies looking to begin the journey to make data acentral component of their business proposition. However, the nature of digital data with itshigh volume, variety and velocity lends itself to the application of emerging technologiesspecifically developed from the ground up to operate in this new paradigm. The assessmentof those is beyond the scope of this research.The aim of this project was to combine the robustness of long established data warehousingdevelopment principles, with the benefits of statistical analysis to address businesschallenges.The Extract, Transform and Load (ETL) component of this project proved to be the mostchallenging as well as intellectually rewarding. The ability to integrate, transform andstructure data for statistical analysis was the key focus and deliverable of this stage.The methodology developed throughout this thesis will form the basis of the businessintelligence and statistical function to be implemented across Neo@Ogilvy UK, and shouldbe seen as an initial step in a complex and ever evolving discipline.Areas of interest and of significant importance have been left untouched due to timeconstraints, but will be further developed by the researcher beyond the academicrequirements to fulfil the MSc in Business Intelligence.
  5. 5. Table of FiguresFigure 1: Last Click Digital Marketing Measurement Model …………………………. 11Figure 2: The Data Mining Process - Mundy, Thornthwaite and Kimball (2011) …... 13Figure 3: Generic Tasks (bold) and Outputs (italic) CRISP-DM Model ……………… 14Figure 4: Doubleclick Digital Marketing Campaign Setup Process Flow ……………... 19Figure 5: Doubleclick Dimension Log Files …………………………………………….... 20Figure 6: Doubleclick AdvertiserActivity/Click/Impression Log Files …………………. 21Figure 7: Platform Performance Issues ………………………………………………… 22Figure 8: DW/BI System Architecture Model …………………………………………… 24Figure 9: User Model …………………………………………………………………….. 25Figure 10: Sun Model …………………………………………………………………….. 27Figure 11: Star Schema ………………………………………………………………….. 27Figure 12: OLAP Schema ……………………………………………………………….. 28Figure 13: ETL Steps …………………………………………………………………….. 30Figure 14: Full Decision Tree Model Output SSAS TotalRevenue …………………. 45Figure 15: Full Decision Tree Model Output SSAS NumberOfProducts …………… 45Figure 16: Partial Decision Tree Model Output SSAS TotalRevenue ……………… 45Figure 17: Decision Tree Model Output SSAS NumberOfProducts ………………… 45Figure 18: Dependency Network Output SSAS TotalRevenue …………………….. 46Figure 19: Dependency Network Output SSAS NumberOfProducts ………………. 46Figure 20: Model Evaluation Graph TotalRevenue ………………………………….. 47Figure 21: Model Evaluation Graph NumberOfProducts ……………………………. 47Figure 22: Solution Deployment ……………………………………………………….. 50Table of TablesTable 1: Project Product Description …………………………………………………… 15Table 2: AdvertiserActivity Other-Data Variables ……………………………………... 23Table 3: dimension description ………………………………………………………….. 26Table 4: dimensional design specification ……………………………………………... 29Table 5: ETL Design Specification ……………………………………………………… 31Table 6: Analytical Dataset Details ……………………………………………………… 39Table 7: Validation Results ………………………………………………………………. 41Table 8: GLM Model Output Rattle (R) NumberOfProducts Vs TotalRevenue …….. 43Table 9: GLM Scoring File Output Dependent Variable NumberOfProducts ………. 44Table 10: Product Categories ……………….……………………………………………. 48
  6. 6. 1. OverviewIntroductionGone are the days when organisations could happily rely on mass reach advertisingchannels such as television to get access to the desired audiences through sixty secondsadvertisements. Media fragmentation, social changes and consumer lifestyle demands haverendered traditional marketing channels ineffective in fulfilling the commercial roletraditionally associated with the marketing industry – generate demand from either newlycreated or existing consumer needs.The nature of the digital marketing industry, where customer interactions can be tracked andmeasured to an unprecedented level, presents both challenges and opportunities for thoseorganisations able to harness the power of data whilst managing and addressing the privacyconcerns of users, businesses and national governments.The proliferation of data sources and its associated volume is presenting significantchallenges to the digital marketing industry as a result of the exponential growth driven bythe social shift around media consumption experienced over the last decade.Businesses are searching for marketing agency partners who can assist them navigatingthrough the complexity of this new data paradigm, and at the same time maximising thereturn on investment across ever growing digital marketing budgets. The World AdvertisingResearch Council (WARC) in its “Adstats: Global adspend forecast” study (2012) projects anoverall 12.3% growth for online marketing expenditure across the 12 key markets (Australia,Brazil, Canada, China, France, Germany, India, Italy, Japan, Russia, UK and US). This isalso corroborated by the Internet Advertising Bureau (2012), the trade association for onlineand mobile advertising in the UK, which reported a 12.6% growth in digital advertisingexpenditure in the first half of the year compared to the first 6 months in 2011.Not only data can provide a competitive advantage to organisations looking to build a datadriven culture for marketing investment decision making but also, through the application ofrobust statistical techniques, it can deliver more meaningful and relevant messages toconsumers to improve both the user experience and the brand perception in the marketplace.The development of the technical data infrastructure and the implementation of the statisticalcapability to improve digital marketing campaigns targeting decisions, and to quantify theactual contribution of digital marketing channels towards a product purchase and/or revenueassociated to the purchase are the central areas of focus for this project.
  7. 7. In line with commonly accepted estimates around the development of business intelligencesystems (Williams, 2011 p.57), the effort required to deliver this project was approximatelysplit as follows - 80-85% of the resources were allocated to the development of the Extract,Transform and Load (ETL from hereon) processes, with the remaining 15-20% dedicated tothe statistical analysis and academic writing.The State of Art in the Digital Marketing Agency IndustryDigital marketing is often referred to with a myriad of different terms – amongst the mostpopular are e-marketing, interactive marketing and online marketing. Brodie, Winklhofer,Coviello and Johnston (2007) formally defined e-marketing as “using the Internet and otherinteractive technologies to create and mediate dialogue between the firm and identifiedcustomers”.This discipline was added by the authors in 2001 to the Contemporary Marketing Practices(CMP) classification originally developed by Coviello, Brodie and Munro in 1997 (Brodie,Winklhofer, Coviello and Johnston, 2007) which included:• Transaction marketing (TM) defined as “using the traditional ‘4P’ approach to attractcustomers in broad market or specific segment”• Database marketing (DM) defined as “using database tools to target customers in aspecific segment or microsegment of the market”• Interaction marketing (IM) defined as “developing personal interactions betweenemployees and individual customers”• Network marketing (NM) defined as “developing relationships with customers andfirms within the network”Whilst some of the core marketing concepts from the traditional disciplines aforementionedremain relevant to digital marketing, the digital marketing agency industry has historicallystruggled to benefit from a closer integration with the natural synergies identified by Covielloet al. (2001). Due to the relevance to this thesis, it is worth highlighting the close relationshipbetween database marketing and e-marketing identified by Coviello when he stated that “eMfocuses on real-time dialogue that is enabled and mediated by information technology, andso builds on and enhances DM. Rather than being a one-way relationship ‘to’ the customerwhere databases are used to personalise communication, the interactive, technology-enabled communication of eM is ‘with’ and ‘among’ many parties”.The adoption of database technologies and advanced statistical tools across the digitalmarketing agency industry to drive targeting decisions and to measure the commercialcontribution (e.g. products purchased/revenue) of various digital marketing channels remainsrelatively low. The state of art of the latter is illustrated by the current methodology used
  8. 8. across the digital marketing industry to measure the commercial contribution of digitalchannels, commonly referred to as the last click model. This flawed concept is presented infigure 1:YahooAdUserGoogleAdThe User is tracked based onthe tracking code associatedto Google’s ad campaignFT.comAdThe User is tracked based onthe tracking code associatedto the FT’s ad campaignThe User is tracked based onthe tracking code associatedto the Yahoo’s ad campaignInteraction 1User clicks on a link ad in GoogleClientProductClientSiteInteraction 2User sees abanner ad in FT.comUser is directed to Client sitebut does not purchaseInteraction 3User clicks on a link adin YahooUser is directed to Client siteand purchases the productYahoo userpurchasesthe productThe Javascript tag in theClient website assigns theproduct purchase to the lastclick – i.e. the last interactiondirecting the user to the clientsiteFigure 1: Last click digital marketing measurement modelWithin the above model, the contribution that interactions 1 and 2 (i.e. Google and FT.comads) might have made towards influencing the user to purchase the product is simplyignored by the current industry standard method used to measure the performance of digitalmarketing campaigns.With regards to targeting decisions, data mining of the ever increasing datasets generatedby the digital marketing platforms is not widely adopted across the industry and presents asignificant opportunity to digital marketing agencies looking to improve the quality andrelevancy of its campaign targeting decisions. Montgomery and Smith (2009) refer to thisnotion as personalisation which “is meant to eliminate tedious tasks for the customer, andallow the marketer to better identify the user’s needs and goals from past behaviour” (p.130).They also recognised that “clickstream is underutilised and it is likely to take years before itspotential is fully leveraged” (p.133).
  9. 9. Aim and ObjectivesThe aim of this dissertation is to provide a practical business intelligence framework thatallows digital marketing agencies to implement a predictive analytics solution to betteraddress the current business challenges experienced across the industry highlighted in thesection above. To this extent, a business based project was deemed as the most suitableapproach to meet the aforementioned objective. An agreement was reached withNeo@Ogilvy, the performance marketing digital agency, to allow access to Client’s digitalmarketing activity data for this purpose. The University of Dundee agreed to and supportedthis proposal.The concentration of this project is around three key areas:• Developing the end data warehouse and associated ETL processes toproductionalise this framework in a high data volume, variety and velocityenvironment in order to enable the statistical analysis of the areas below• Targeting decisions: utilising user cookie level data (leaf level) to develop statisticalmodels to predict future marketing campaign response• Channels commercial contribution: using user cookie level data (leaf level) tostatistically attribute the contribution of each of the digital channels towards a productpurchase and/or revenue associated to the purchaseLeaf data at user cookie level generated by the digital marketing platform Doubleclick,owned by Google, was used as source data for the project. This platform records every userinteraction associated to any digital marketing campaign run by Neo@Ogilvy. This amountsto millions of records on a daily basis across multiple markets and products.The technical development of the above solution is underpinned by exhaustive secondaryresearch of the leading academic and business literature on the field of business intelligenceand analytics.
  10. 10. 9. ConclusionsThe digital marketing industry is quickly becoming a dominant commercial channel in theUK, though it can be argued that its revenue growth is not matched by the use made ofadvanced statistical techniques to address the 2 key areas this thesis focuses on.The ETL component of this project was underestimated by the researcher during the initialproject planning phase, both in terms of data complexity and processing power required.However, the solution developed is deemed to be robust and should be seen as an initialstep towards addressing some of the current data utilisation shortcomings across the digitalmarketing industry. This component of the project was developed using Microsoft SSIS ,VB.net and T-SQL, though the design logic of each step can be adapted to other platforms.The CRISP-DM methodology followed throughout this thesis has provided a structuredframework to progress through the different stages of the project in a logical manner todeliver against the agreed objectives. However, a note of criticism is around areas ofperceived duplication of tasks as highlighted throughout the paper.The technology chosen for data storage and processing presented challenges in its ability todeal with the volume of data generated by the digital marketing platform. With hindsight, apotential alternative might have been a Hadoop platform which “allows for the distributedprocessing of large datasets across clusters of computers using simple programmingmodels” (Hadoop, undated), combined with the programming language MapReduce thatexcels at parallel data processing across very large data sets. This programming languagewas developed as a system “for efficient large-scale data processing presented by Google in2004 to cope with the challenge of processing very large input data generated by Internet-based applications” (Marozzo, Talia & Trunfio, 2012, p.1382).The implemented statistical analysis framework is a suitable option, albeit not the only one,to deal with the way in which the log file data was distributed. It is important for anyonelooking to apply the methodology proposed on this thesis to fully understand how to matchthe statistical technique chosen to the data available.From a resource allocation perspective within the context of the time available by theresearcher, the aim to provide an end to end predictive analytics solution might have beentoo broad a scope for the project. The development of the data warehouse consumed 80-85% of the time dedicated to it, leaving limited margin to offer an in depth assessment of thepros and cons of the different statistical techniques available to address the key objectives.This is an area of keen interest that will be further explored and investigated beyond thisthesis.
  11. 11. 10. Future WorkTraditional data warehousing technologies can be deployed to deliver a practical solution tothe challenges outlined at the start of the project. However, 3 key areas could be of interestto further advance the depth and efficiency of such a solution. These have been outlinedthroughout the difference stages of the CRISP-DM methodology, but for the purpose ofclarity will be summarised below:• Parallel processing technologies: research into how technologies such as Hadoop,MapReduce, Hive, Pig, etc. can improve the processing of extremely large digitalmarketing datasets• Statistical techniques assessment: a thorough understanding of the differentstatistical methods is of critical importance to fully unlock the potential of predictiveanalytics. This thesis has not carried out such an in-depth assessment due to timeconstraints, so it presents an area of great research potential for future work• Recency, Frequency and Intensity: the time dimension is available in the dataset buthas not been utilised in the statistical modelling stage of this project. Given theexpected impact that time decay has on the effectiveness of marketingcommunications, this subject is regarded as a significant opportunity to furtherexpand the predictive power of the statistical models
  12. 12. 11. ReferencesBorle, S., Singh, S. & Jain, D. (2008) Customer Lifetime Value Measurement. ManagementScience. [Online]. Available from:http://web.ebscohost.com/ehost/detail?sid=9a2e89fb-989d-4e50-b86f-d931b5d0b6fc%40sessionmgr15&vid=1&hid=18&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#db=buh&AN=29984784 [Accessed 28 May 2011]Brodie, R.J., Winklhofer, H., Coviello, N.E. & Johnston, W.J. (2007) Is eMarketing coming ofage? An examination of the penetration of e-marketing and firm performance. [Online].Available from:http://www.sciencedirect.com/science/article/pii/S1094996807700191 [Accessed 8September 2012]Bucklin, R.E. & Sismeiro, C. (2009) Click Here for Internet Insight Advances in ClickstreamData. [Online] Available from:http://www.sciencedirect.com/science/article/pii/S1094996808000054 [Accessed 25 July2012]Chapman, P., Clinton. J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. & Wirth, R.(2000) CRISP-DM 1.0. [Online]. Available from:ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf [Accessed 26 May 2012]Delen, D., Cogdell, D., & Kasap, N. (2012) A Comparative analysis of data mining methodsin predicting NCAA Bowl Outcomes. [Online]. Available from:http://www.sciencedirect.com/science/article/pii/S0169207011000914 [Accessed 5December 2012]Duke University (undated) Interpretation in Multiple Regression. [Online]. Available from:http://www.stat.duke.edu/courses/Spring00/sta242/handouts/beesIII.pdf [Accessed 5December 2012]Enke, D. & Thawornwong, S. (2005) The use of data mining and neural networks forforecasting stock market returns. [Online]. Available from:http://www.sciencedirect.com/science/article/pii/S0957417405001156 [Accessed 5December 2012]Faraway, J. (2002) Practical Regression and Anova using R. [Online]. Available from:http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf [Accessed 6 August 2012]Garcia-Navarro, R (2011) Investigating Requirements Analysis. MSc Business Intelligencemodule AC52035, Dundee. University of DundeeGoogle (undated) The foundation for managing online ads. [Online]. Available from:http://www.google.co.uk/doubleclick/advertisers/solutions/ad-serving.html [Accessed 16November 2012]Hadoop (undated) Welcome to Apache Hadoop. [Online]. Available from:http://hadoop.apache.org/#What+Is+Apache+Hadoop%3F [Accessed 16 December 2012]Illinois State University. (undated) SPSS: Descriptive Statistics. [Online]. Available from:http://psychology.illinoisstate.edu/jccutti/138web/spss/spss3.html [Accessed 5 December2012]
  13. 13. Inmon, W.H. (2005) Building the Data Warehouse. 4thEdition. Wiley Publishing Inc.Internet Advertising Bureau. (undated) H1 2012 Internet Advertising worth £2.6 billion.[Online]. Available fromhttp://www.iabuk.net/research/library/2012-h1-digital-adspend-factsheet-0 [Accessed 24November 2012]Jackman, S. (undated) Generalised Linear Models. [Online]. Available from:http://jackman.stanford.edu/papers/glm.pdf [Accessed 5 December 2012]Kimball, R. (2002) The Data Warehouse Toolkit. 2ndEdition. Wiley Publishing Inc.Kimball, R. (2008) The Data Warehouse Lifecycle Toolkit. 2ndEdition. Wiley ComputerPublishingMarozzo, F., Domenico, T. & Trunfio, P. (2012) P2P-MapReduce: Parallel data processing indynamic Cloud environments. [Online]. Available from:http://www.sciencedirect.com/science/article/pii/S0022000011001668 [Accessed 16December 2012]Microsoft. (2012) 2012 SQL Server 2012 Tutorials: Analysis Services - Data Mining. [Online].Available from:https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CFQQFjAA&url=http%3A%2F%2Fdownload.microsoft.com%2Fdownload%2F0%2FF%2FB%2F0FBFAA46-2BFD-478F-8E56-7BF3C672DF9D%2FSQL%2520Server%25202012%2520Tutorials%2520-%2520Analysis%2520Services%2520Data%2520Mining.pdf&ei=UkzcUPS7Aeml0QXpx4HICQ&usg=AFQjCNHNfl2s6R1-UZFvTjCmASHNPlgTAw&sig2=n4TX2YaDd_1vVrYpMNvyVg&bvm=bv.1355534169,d.d2k[Accessed 29 August 2012]Montgomery, A.L. & Smith, M.D. (2009) Prospects of Personalisation on the Internet.[Online]. Available from:http://www.sciencedirect.com/science/article/pii/S1094996809000322 [Accessed 8September 2012]Mundy, J., Thornthwaite, W., & Kimball, R. (2011) The Microsoft Data Warehouse Toolkit.Wiley Publishing, Inc.Office of Government Commerce (2009) Managing Successful Projects with PRINCE2. 5thEdition. TSO@BlackwellOlson, D. & Chae, B., K. (2012) Direct marketing decision support through predictivecustomer response modelling. [Online]. Available from:http://www.sciencedirect.com/science/article/pii/S0167923612001881 [Accessed 5December 2012]Rattle. Graphical user interface for data mining in R. [Online]. Available from:http://cran.r-project.org/web/packages/rattle/index.html [accessed 12 October 2012]Sharma, S., Osei-Bryson, K.M. & Kasper, G. (2012) Evaluation of an integrated KnowledgeDiscovery and Data Mining process model. [Online]. Available from:http://www.sciencedirect.com/science/article/pii/S0957417412002886# [Accessed 5December 2012]
  14. 14. Whitehorn, M. (2011) Modelling a BI System – MSc Business Intelligence Lecture Notes.Available from: https://my.dundee.ac.uk [Accessed 26 February 2011]Williams, G. (2011) Data Mining with Rattle and R. SpringerKimWilliams, G. (undated) Predicted versus Observed. [Online]. Available from:http://datamining.togaware.com/survivor/Predicted_versus.html [Accessed 13 September2012]World Advertising Research Council (WARC). Adstats: Global adspend forecast. [Online].Available from:http://www.warc.com/Content/ContentViewer.aspx?MasterContentRef=d1e68f3e-c4da-48ee-90cd-b37e48763f50 [accessed 5 December 2012]

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