1 Dr. Olav Laudy: An overview of analytics capabilities and work performed Key projects* Opportunities Communication Trust Proof of Concept Automotive & Manufacturing Banking Retail Telecom Transport B2B Other Education Operationalization Sharing Collaboration $ C T P O S & Legend * Details can be found in the appendix $ C P O S & C P & C T P S & $ C T P S T O & $ C T P S & C T O S T O & $ C T P O S & C T P S & $ C P O S $ C P S C T P S & C T P O C T P S $ T P O $ C P S $ C P S & $ T P S & $ C T P S C P & C T S C T S C T S C T S $
Overview of analytic capabilities and work performed
Data scientist/analytics expert/predictive modeler with 12+ years worldwide experience in Telco/Banking/Insurance/Manufacturing.
Designing predictive strategies, building predictive models with and for customers, engaging them on the way to become a predictive enterprise. Highly motivated, quick-witted, eager to learn and able to think outside of any given box. Excellent analytical skills and communicative ability. Hands-on mentality, comfortable with a wide range of (statistical) programming languages and IT systems. Focus on direct financial result for customer. PhD in Bayesian modeling.
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� Value creation through:
Driving revenue and client engagement
• Identifying opportunities to use data in order to drive business results. Opportunities
• Creating compelling story lines around the use of data to drive the adoption of analytics.
Communication
• Gaining trust by explaining data science concepts in business terms. Trust
• Quickly turning around innovative data science solutions using IT technology to prove ROI
Proof of Value
• Embedding data science results in IT systems to generate continuous value. Operationalization
• Training data scientists to drive the widespread use of data in order to improve business results.
Sharing
• The ability and desire to collaborate with a wide range of people with different skill sets to manufacture advanced value chains.
Collaboration
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Dr. Olav Laudy: An overview of analytics capabilities and work performed
APPENDIX1-sliders of the key projects
Note: for all projects hold that • The slide material is original and developed for the purpose of the project.• The author of this document is the lead designer of the analytical methods in the projects mentioned.
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Assorting slow moving automotive parts for a large Canadian retailer
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A large Canadian retailer in automotive parts(600 stores, 30k SKU’s in store, 300k SKU’son order) was looking to use external data andanalytics in order to improve the assortment ofslow moving automotive parts. Over thecourse of a year, a large scale predictionsystem was built and an education aroundanalytical processes was implemented tomake the organization accept and allow data&analytics to be the driver of the assortmentplanning.
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Creating an analytical Center of Competence for a large Canadian retailerWith the assortment model in development,the Canadian retailer was looking to furtheradvance the smart use of data. In order tostreamline the process, an analytical Center ofCompetence (CoC) was created. Theprocesses and governance around the CoCwas captured in template documents and wasdemonstrated by artifacts made available inan analytical repository.
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Innovation on data & model understanding for a large Canadian retailer
Gaining insight in the predictions (orrecommendations) done by the analyticalassortment model is very hard due to thehuge number of predictions (30M). Usingthe latest insights from a TopologicalData Analysis (TDA), various methodswere created that could be used tovisually spot the predictions of interest insuch a way that by zooming in, thedetails of the SKU at hand could beunderstood and appropriate action couldbe taken.
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Container arrival prediction for a major Chinese portA large Chinese port was looking to betterorganize the containers on the storageyard, so that the containers could be loadedmore effectively on the vessel. In order tomeet this requirement, it was desired toknow when containers would arrive frommainland China, up till the level of containerdestination and container size. The model,based on historical arrival patternsoutperformed their current process by 30%.
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Event based marketing model for a major Philippine bankThe client was looking for an event basedmarketing system. In such a system, based ontransactional patterns, one tries to detectcustomer life events, such that the appropriatecommercial offering can be made at the righttime. A total of 10 events were defined and itwas carefully shown how the customer canchoose to parameters of the method in order toalign the detection of events with the availableworkforce.
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Wealth advisor product model for a large Singaporean bankWith an IBM team developing the first version ofa product category recommendation model, thecustomer expressed their concern around thesimplicity of the models. In order to address theconcern, a second generation model (personallevel simulation) was created. In addition, theclient was presented with the right educationalmaterial in order to understand the progressionand requirements of analytical processes. Theconcern of the customer was effectively relieved.
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1 year mortgage forecasting based on transaction data for a large Dutch bank
The client was interested in understanding howcustomer level bank transaction data (BigData) could be used to predict the purchase ofa mortgage one year out. The model in placeused customer profile data and aggregatedfeatures from transactions (total balance). Anovel method was created to use the B-account number (the transfer-to number) as apredictor in the model. The new model vastlyoutperformed the model in place.
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Guided diagnostics model for a premium German car manufacturerIn order to diagnose car problems, workshoppersonnel uses search trees. The search tree ismanually put together by the manufacturer.Building the search trees is a cumbersome task,and subject to constant change. Furthermore it isnot optimal (in terms of shortest route andminimum costs). A system was developed, basedon statistical models and principles that proposestests and asks questions to the workshoppersonnel in order to as quickly and cheapdetermine the root cause of the issue at hand.
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Car options recommendation model for a premium German car manufacturerWhen buying a car, to choose from the 2000possible extra options requires an expert.However, the online car configurator allowsconsumers to configure a full car, including allthe possible options. The recommendationmodel used Point of Sale (PoS) data togenerate recommendations based on carconfigurations of customers with similar carprofiles. The model is scored on a nightly basisand emails are sent with the top 5recommendations.
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Repeat repair model for a premium German car manufacturerA premium car comes with high expectations. Abreak-down during the warranty period issometimes unavoidable, and this is understoodby the customer. A second break-down within a30 day period is, however, quite unforgivable, asshown by studies of customer satisfaction. Themodel at hand predicts, during the workshopvisit of the first break-down, for which cars thissecond break-down is likely to happen. It urgesworkshop personnel to pay extra attention to thecar to prevent a repeat repair.
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Text mining dealer satisfaction for a premium German car manufacturerService to a car, as well as the experiences ofbuying a new car are evaluated using aquestionnaires. The closed questions wereused to generate a reports, leaving out the richinformation available from the open answers.This project used text mining to createadditional reports in order to better understandthe voice of the customer. It was found thatcustomers mention crucial importantexperiences outside the closed questions.
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Lead pre-selection model for a premium German car manufacturer
A premium car German manufacturer collecteddata sources with client details from across tocompany (marketing, events, dealer provided,etc), wondering if it would be wise to offer thosecustomers a test drive. A lead pre-selectionmodel was created that is scored every night,selecting customers who are likely buying a car.Those customers are offered a test drive. A 2-year later model validation follow-up showed thatthe model was still as strong as was shown inthe initial project.
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Driving analytical progression in an major Indian telecom providerA large telecom provider in India challenged IBMon the progress made in the IBM hostedanalytical program. Although the quality of thework performed was high, IBM failed tocommunicate the results back to the client.Creating a reference framework and showing theclient how the analytical results could drive theirbusiness, put IBM back in the right spot,eventually leading to a 2B USD 10 year contract.
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Detecting fraudulent and undesirable customer behavior for major US telecom providerIn a large transformation project, the customerexpressed the need to use analytical proceduresin order to earlier detect and prevent fraud. Thedata consisted of machine levels connectiondetails of mobile devices. A series of 15 differentapproaches were developed that could be usedin order to detect fraudulent and undesirablebehavior. The material is developed usingsimulated data so it can be used as a templatefor other projects as well as for learning material.
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Warranty modeling Proof of Concept for large Swedish car manufacturerThe client was looking to advance their datascience practice in the area of warrantyprocesses and quality control. Data of carfailure was provided and IBM was requested todemonstrate the various analytical capabilities.Three different models were developed,showing the application of analytics as well asthe ease of use of the IBM tooling. As a result,the client understood and appreciated IBM’sanalytical capabilities and has involved IBM insubsequent conversations around the topic.
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Proof of Concept segmentation model for a large Asian stock exchangeThe client expressed interest in the IBM’sanalytic capabilities. Data was provided ofcustomers doing investments with the requestto showcase how to better understand thecustomers. A segmentation, followed by amicro-segmentation was performed and it wasshown how the understanding of the segmentscould be made actionable. The client wasdelighted by the approach and during the PoC,they were already looking to implement theactionable findings
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Account rationalization model for a large US based payroll service provider “For which clients (companies) would haveeducational services the most impact in terms ofparticipation rate and contribution to a 401Kplan?”, was the question of this payroll provider. Aseries of 3 analytical approaches showed a clearanswer to this. First, the under or over-performingof a company was expressed in terms ofopportunity. Second, the effect of educationalservices was modelled and third, an accountrationalization process was proposed in order tomost effectively targeting the right companies.
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B2B churn model for a large US based payroll service provider The client expressed concerns regarding theprogress and understanding of a B2B churnproject executed by IBM. Apart from rebuildingthe model, the client was educated on theunderstanding of analytical processes, the validityof the model and the applicability of the model inthe business context. With a deployed predictionmodel in place, outputting to a client leveldashboard, the stake holders were able toconvince the organization to adopt this new formof thinking.
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Proof of Concept for combining retail and telecom data for a large Thai conglomerate The client was interested in the art of the possiblewhen combining telecom Call Detail Records(CDR, who calls who) data and retail data (whobuys what), including the link between these dataon individual customer level. By creating abehavioral profile, a wealth of insights cameavailable, resulting a clearly defined business caseabout the potential results of an analyticalprogram. The client was delighted to see thatmuch detail and insight coming from their data andexpressed further interest in working with IBM.
mobile
mobile
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Warranty analytics Proof of Concept for a Belgium manufacturer
A large manufacturer in Belgium requested aProof of Concept to showcase IBM’s capabilitiesin warranty analytics. Both structured andunstructured data of machine failure wasprovided. The proof of concept demonstrated afrom the ground up build warranty analyticsapplication containing components: ‘failure trendanalysis’ , ‘root cause analysis’ and ‘text analysisof failure description’. The warranty system hasbeen used in demonstrations to various otherclient (using simulated data)
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Predicting hit performance for a large US based music rights management companyThe client engaged IBM’s assistance to kick-start a predictive analytics program. Utilizingradio performance data over the past threeyears the client was able to accurately predicta song’s decaying performance over time andmoderately predict when a song will peakwithin its current lifetime. These techniqueswill allowed to company to better estimate theadvances for royalties to be paid to the artists.
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Teaching material: incorporating business knowledge in data mining modelsIn physical sciences, relations between naturalquantities are often being able to be expressedby complex formulas. It can be hypothesizedthat similar guiding principles lay on the basisof observations in the business world. In thisstudy, a clearly (business) understandable datagenerating mechanism is simulated, and viaCRISP-DM methodology, it is demonstratedthat sufficiently complex statistical models willuncover this relation, provided that the rightbusiness knowledge is injected.
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Teaching material: basics application of analytics in business processesIt is often found there is a large gap between theleaders of a business, who do not understanddata science principles and the data scientists,who are incapable of explaining what they aredoing in clearly understandable business terms.This study shows the application of data miningin the marketing area in a simple and illustrativeway, such that it drives the adoption of analyticsin the top of an organization. The material isused as a start of an opportunity scopingsession to guide the thinking about analytics.
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Teaching material: Watson 2 Modeler
In an attempt to promote the use of Bluemix foranalytical modeling purposes, a demo wascreated that showed how using the statisticalpackage R can be used to connect to the UserModeler service in Bluemix. The R code wascaptured in a custom dialog in IBM SPSSModeler for non-technical users. The materialcomes with a presentation showing the purpose,the advantage and the technical details of theapproach.
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Teaching material: opportunity scoping workshop
This workshop focusses on the process of gettingan aspiring analytics client to think about analyticsin the right way, building up towards an effectiveopportunity discovery session. In the workshop,the processes around model building/evaluationand scoring are discussed as well as theexpectations around model effectivity and usecases where models will likely work or are whenthey are more challenging. This workshop hasbeen conducted at multiple clients and has beenthe successful kickoff of multiple larger projects.
