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How Predictive Analytics Transforms Dell's Marketing Strategy
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How Predictive Analytics Transforms Dell's Marketing Strategy

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In November 2010, Elizabeth Press and Jack Chen from EMEA & Global Business Intelligence and Sayantika Bhaduri and Sumanth Suresh from Dell Global Analytics started building statistical models to …

In November 2010, Elizabeth Press and Jack Chen from EMEA & Global Business Intelligence and Sayantika Bhaduri and Sumanth Suresh from Dell Global Analytics started building statistical models to predict the propensity to buy for account targeting in European campaigns. Beyond the incremental revenue and improved conversion rates in strategic areas that we achieved, this initiative transformed customer analytics and account targeting for Dell’s strategic priorities and leveraged expertise by means of cross-functional cooperation with teams ranging from product development to sales specialists.

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  • 1. March 2012 June 2012Predictive analytics transforms Dell’s marketing strategyCase study of how a unique marketing strategy based on statistical analysis ofcustomer relationships delivered significant incremental Enterprise revenue forDELL EuropeA paper by Elizabeth Press, Sayantika Bhaduri and Sumanth Suresh
  • 2. Dell‟s Transformation JourneyFrom Computer Hardware to IT SolutionsDell was founded in 1984, during the height of what IT industry insiders call the “PC/Client Server Era”,a time when units of hardware sold was the key indicator of success. The 2000‟s and the advent of cloud computing and virtualization has heralded the “Virtual Era” for the IT industry. Application of IT as an enabler of business has become the core value-add of IT in the "Virtual Era." Hardware has become commoditized. Thus demand for IT islinked heavily to the evolution of customers‟ industries. In order for IT manufacturers to positivelydifferentiate themselves, they need to be able to best address the infrastructure and application needsof their customers.Marketing for IT SolutionsDeeper Customer Understanding is the keyWith the transformation from being a PC-manufacturer to becoming an IT solutions provider, Dell neededto revise the go-to-market model in order to be successful in the “Virtual Era.” Business Intelligenceneeded to create effective targeting methods for deepening relationships with existing customers andwinning new customers. Instead of relying on direct relationships with the customer as a strategicadvantage, Dell needed to gain a deep understanding of demand for the products and proactivelyapproach customers with solutions. This prompted a new approach to the way marketing campaignswere carried out.
  • 3. Challenges Outcomes Providing Solution is necessary in Dell Positioned itself as a global the virtual era solutions provider We are looking through the prism Expanded our view of the customers of Client Server Analytical Engine with advanced analytics Our direct business model was no Enhanced targeting in key longer a strategic business multifaceted business areas is a advantage strategic advantageAnalytics Enabled Marketing Campaigns ChallengesCustomer Insights are incorporated into CampaignsLeveraging the power of analytics allowed us to take a holistic and solutions-based approach to customertargeting in our marketing campaigns. Previously, we had used rules-based criteria to identifyprospective customers. We were able to replace the rules-based criteria by solutions-based factors. Byusing statistical modeling, we analyzed a large number of explanatory factors and identified mostimportant indicators of purchase intent.Customer Targeting for Marketing CampaignsAnalytics and EMEA BI teams combine their expertise Dell Analytics and EMEA BI Marketing started to work on the marketing transformation initiative in November 2010. Leveraging the teams combined experience in strategy consulting, risk management and predictive analysis, the Business Intelligence and Analyticsteams jointly developed this method of targeting. Based on the team‟s collective experience in thefinancial services and IT industries, this tailored methodology is comprehensive and deep in its analysis,yet intuitive and easy to understand for the business.
  • 4. Approach to Customer Selection for CampaignsA deeper look at predictive analytics for customer selectionOur earlier targeting methodology was very successful in the “PC/Client Server Era,” where unit sales were the main goal. The “Virtual Era” however demanded a holistic view of customers, as the goal was not only unit sales, but deeper engagement and stronger relationships. It required us to proactively approach our Public and Large Enterprise customers with solutions to their business problems. Predictive analysis enabled us to understand what solutions customers would need and when they would need those solutions.Predictive Analytics for Customer Selection“Give me the best customer to call” is the universal ask of all marketers and sales makersThe selection of customers for campaigns in the new solutions-based approach needed to be centered onaddressing the customer‟s needs. Furthermore, the approach needed to clearly differentiate betweencustomers who might genuinely require a product against those who don‟t in order to enable the salesteams to better understand the likely customer requirements and contact customers with the maximumlikelihood to purchase.
  • 5. Among the various analytical approaches to customer selection, a logistic regression was proposedconsidering the “Give me the best customer to call” ask of marketers. Logistic regression models thelikelihood of a customer purchasing a certain product. The final outcome was a list of customers withtheir individual propensity scores (likelihood or chance of purchasing a specific product). Higherpropensity scores indicated a higher likelihood of response to sales calls.Implementation of Logistic Regression  Preliminary preparation for Modeling Once the objectives, outcomes and methods were fixed, we identified the various data categories (financial, service quality, product related and market related) for building the model.  Searching for trends and patterns in the data – Exploratory Analysis & Hypotheses Exploratory analysis of the data revealed patterns pertaining to purchase cyclicality and buying trends which were further explored and validated with the help of business managers and sales teams. We also identified certain customer sub-segments within the population who were more inclined to buy the target products. This enabled us to frame specific class and category variables as inputs to the model.  Model Variables - Creation, Reduction & SelectionWe began with a list of 500+ model variables for every product modeled. The variable creationincorporated various features of the data such as customer RFM characteristics and customerfirmagraphics (such as employee size, industry type). An important feature in our variable creationprocess was the involvement of business stakeholders. We had multiple rounds of discussions with theproduct managers, solution specialists, sales and BI teams to build variables likely to impact purchasedecision. We were able to identify several significant product affinity variables using insights fromvarious stakeholders. By identifying product affinities using the inputs of Solution Specialists we wereable to bring a „solution‟ basis to model building. The overall list of 500+ variables was reduced to asmaller set of 25+ modeling variables by applying multiple statistical, business and sense check filters.  The Logistic Regression Model – Model Selection & Customer ScoringThe 25+ modeling variables were tested in various combinations and different models were iterated.Statistical tests and out-of-sample validations were used to identify the better performing models. Theiterations were also shared with stakeholders to seek their feedback regarding the business significanceof the statistically significant variables. We also used sign tests and checked individual variable weights
  • 6. to avoid heavy loading on any single factor. The final model with 5-7 variables was selected based onfulfillment of all the above criteria.  Customer ScoringThe customers were scored using the selected model and the end result was a purchase likelihood scorefor customers to buy a specific product. The final customer selection was made after excluding thecustomers with whom we have lost a deal in the recent past (last 6 months) or who are already in adiscussion with the Sales team.Monitoring Performance and ROI MeasurementIntuitive metrics of campaign effectivenessIn order to effectively monitor and communicate the performance of predictive marketing methodsacross the organization, we created intuitive and simplified metrics to measure the incremental revenueimpact. We tracked the incremental revenue over sales targeted activities by measuring two metrics,conversion and average order value. Conversion measured the targeting efficiency of the campaignswhile average order value measured the revenue derived from converted customers.Business Impact of Predictive AnalyticsROI, Strategic Customer Insights & Ideation FrameworkUsage of predictive analytics for campaign targeting went from an innovative idea to a strategy-changingpractice within a year. The implementation of predictive analytics has impacted the business in threefundamental ways:  Dell grew incremental revenue and improved sales effectiveness  Sales specialists and management received strategic insights about customers  A continuous ideation framework which promoted a structured approach to incorporation of new ideasDelivered significant campaign ROIDell increased revenue and improved targeting effectiveness in strategic enterprise products such asservers and storage. The sales specialist organization used the output of the statistical models foridentifying their targets, enabling an effective and transparent quota setting method for SalesSpecialists.
  • 7. Provided Strategic Input for the Business “The Pecking Order”The insights gained during the modeling process • Solution Buyers: Customers who areenabled framing of marketing strategy and highly likely to buy a specific product as High part of an overall Solutionsupported the Marketing and Sales teams in • Hardware Buyers: Customers whounderstanding their customers better. One are highly likely to buy a specificexample of business insight gained through Medium productmodeling was that the customer scores revealed a • Non Buyers: Customers who are“Pecking Order” which further validated the Low unlikely to buy a specific product„solution‟ approach used in the model building.Additional insights that we have presented tomanagement have included:  Identification of seasonal trends  Firmographic niches for the different products.  Customer trend analysis.These findings have been inputted into executive level strategic decision making and implemented intransforming the go-to-market model.Developed a continuous ideationframework Research/ ExpertWe have created and documented a Input, Feedback from last iterationprocess in our team to enablecontinuous application of the insight Strategic Insights to Hypotheseswe gained through analysis into Executive, ROI Formulation, measurement Exploratory Analysisstrategy.The purpose is to enable continuous Continuousstructured brainstorming and new ideas Ideationin the organization. IT is a very Framework Presentation ofdynamic industry and impacting Model Building and Hypotheses, Analysis Extraction of Insightsinnovation processes have created a findingsstrategic advantage for Dell. Our Open Discussionsprocess takes a cross-functional and with larger teams - Product, Sales, BIthus interdisciplinary approach toensure a comprehensive look at theissue to enable optimal insight and decision making.
  • 8. And Dell‟s customers are more satisfied…Our solutions- based approach has enabled us to sharpen our focus on the customer and providing themwith the Power to Do More. This approach can be applied by other companies in almost any industry.Successful implementation of the principles of comprehensive analytics, collaborative team work withsales and marketing, straightforward metrics and communication, coupled with a process of continuousideation and strategic insight can help companies build better relationships with their customers.
  • 9. About the Authors:Elizabeth Press (Elizabeth_press@dell.com) is a Research Sr. Advisor in the EMEA Public and Large EnterpriseBusiness Intelligence Team and based out of Frankfurt, Germany. She has a BA in International Relations from TuftsUniversity and an MSc in International Economics and Business from the Stockholm School of Economics. She hasworked in strategy consulting and the finance & technology industries.Sayantika Bhaduri (Sayantika_bhaduri@dell.com) is an Advisor with the Marketing and Sales analytics team in DellGlobal Analytics and based out of Bangalore, India. She holds a Masters in Mathematics from IIT Kanpur and hasworked in Marketing Analytics for technology industry.Sumanth Suresh (Sumanth_suresh@dell.com) is a Sr. Analyst with the Marketing and Sales analytics team in DellGlobal Analytics and based out of Bangalore, India. He has a Masters in Engineering from IIT Madras and has workedin consulting and analytics.About Dell Global AnalyticsDell Global Analytics seeks to improve Dell‟s bottom line through the leveraged use of analytics touching allaspects of Dell‟s business operations. We offer a wide range of analytics services covering management reportingand dash boarding of key business metrics, forecasting and predictive customer response modeling andoptimization of key business processes. Value for Dell is unlocked by the application of sophisticated data analysis,statistical and mathematical techniques under a Six-Sigma framework of business process improvement.The range of supported Dell functions includes Dell Supply Chain, Pricing, E-Commerce, Contact Center Operations,Dell Financial Services, Marketing and Sales.Office:Dell International Services India Pvt. LtdDivyashree Greens,Survey No 12/1, 12/2A, 13/1A, Challaghatta, Varthur Hobli,Bangalore 560071, INDIAAbout Customer Insight & Business Intelligence, EMEA, Public and Large EnterpriseCustomer Insight & Business Intelligence drives the provision of live, relevant and timely business intelligenceinformation and customer insight to the Public and Large Enterprise sales and marketing leadership teamsthroughout EMEA. We also provide targeting and strategic insight for EMEA-wide campaigns.We are located at various locations throughout EMEA.