operation research paper
This article was downloaded by: [132.174.254.97] On: 25 February 2017, At: 11:20 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Interfaces Publication details, including instructions for authors and subscription information:
http://pubsonline.informs.org Hewlett Packard: Delivering Profitable Growth for HPDirect.com Using Operations Research Rohit Tandon, Arnab Chakraborty, Girish Srinivasan, Manav Shroff, Ahmar Abdullah, Bharathan Shamasundar, Ritwik Sinha, Suresh Subramanian, Dave Hill, Prasanna Dhore, To cite this article:
Rohit Tandon, Arnab Chakraborty, Girish Srinivasan, Manav Shroff, Ahmar Abdullah, Bharathan Shamasundar, Ritwik Sinha, Suresh Subramanian, Dave Hill, Prasanna Dhore, (2013) Hewlett Packard: Delivering Profitable Growth for HPDirect.com Using Operations Research. Interfaces 43(1):48-61. http://dx.doi.org/10.1287/inte.1120.0661 Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact [email protected]. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. Copyright © 2013, INFORMS Please scroll down for article—it is on subsequent pages INFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics.
For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Vol. 43, No. 1, January–February 2013, pp. 48–61 ISSN 0092-2102 (print) —ISSN 1526-551X (online) http://dx.doi.org/10.1287/inte.1120.0661 © 2013 INFORMSHewlett Packard: Delivering Pro table Growth for HPDirect.com Using Operations Research Rohit Tandon, Arnab Chakraborty, Girish Srinivasan, Manav Shroff, Ahmar Abdullah, Bharathan Shamasundar, Ritwik Sinha Hewlett Packard Global Analytics, Bangalore-560093, India {[email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]} Suresh Subramanian, Dave Hill Hewlett Packard, HPDirect.com, Cupertino, California 95014 {[email protected], [email protected]} Prasanna Dhore Hewlett Packard Corporate Marketing–Customer Intelligence, Palo Alto, California 94304, [email protected] Hewlett Packard (HP) entered the online consumer sales business with its launch of HPDirect.com, a portal that allows consumers to purchase HP products (e.g., desktop and notebook computers, printers, accessories, supplies) online. This paper describes operations research solutions to a variety of problems in the e-commerce value chain. HP's objective was to use these solutions to grow its share in the online sales market. First, we identify and quantify the impact of key drivers of online traf c to enhance our market planning and budget allocation process. Next, we apply Bayesian modeling and Markov chain methods to predict which customers are most likely to buy which product, and when and through which marketing channel they are likely to make a purchase. Finally, we use a hybrid forecasting approach combining time-series and regression modeling to predict customer orders for optimizing warehouse inventory holding and ensuring timely ful llment of customer orders. Since 2009, the integration of these solutions into HP's marketing planning and warehouse operations processes has helped to generate an additional $117 million in revenue for HPDirect.com.
Key words : HP; online sales; e-commerce; demand modeling; customer targeting; marketing spend optimization; forecasting; time series; Bayesian modeling; linear discriminant analysis; Markov analysis; linear programming; analytics. H ewlett Packard (HP) is the world's largest provider of information technology infrastruc- ture, software, services, and solutions. It caters to indi- viduals, businesses, and nonpro t organizations of all sizes in 170 countries. Traditionally, the company has marketed its products and services through an indi- rect channel (i.e., third parties), which comprised its sales networks and strong retail partnerships. Follow- ing the Internet revolution of the 1990s, HP revisited its marketing strategy; as a result, it entered the online consumer sales business to seize opportunities from the worldwide boom in e-commerce and to comple- ment its strengths in retail selling. In January 1998, HP launched HPDirect.com (http:// www.HPDirect.com), its online sales channel in the United States, to allow consumers to purchase prod- ucts online from HP and have them delivered to their homes. These products include desktop and notebook 48 THE FRANZ EDELMAN AWARD Achievement in Operations Research Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 49computers, printers, accessories (e.g., mice, adapters), and supplies (e.g., ink, paper, toner). Given its pri- mary dependence on the indirect channel (i.e., brick and mortar retail stores), establishing HPDirect.com as a successful route to market became a core chal- lenge for HP, because the company had to be care- ful not to alienate key retail partners such as Best Buy and Walmart. Moreover, HP was facing stiff competition from Dell, the rst company to build a major online personal computer (PC) sales base in the United States. By 2002, more than 50 percent of PC-related sales were direct, with Dell established as the leading player in the Internet-based direct channel (Rosenthal 2003). Clearly, HP had to enhance its direct marketing strategy to retain its overall leadership in the PC market. In 2008 HPDirect.com management was given the mandate to grow its online sales by 150 percent over a three-year period, within limited marketing budgets.
To improve sales, HPDirect.com marketing teams had to rst increase traf c (i.e., attract a greater number of visitors to the online portal). To convert these visitors into customers, the team had to develop a suitable targeting strategy—a way to focus marketing efforts on visitors most likely to buy. It evaluated the buying potential of each visitor based on that visitor 's brows- ing behavior. For visitors who had previously bought from HP, their demand for the products had to be esti- mated, based on their past purchasing patterns. Mak- ing these estimations was a complex task, given the broad portfolio of HP product categories. The mar- keting teams had to estimate demand for each prod- uct category and assess which product category each potential customer would be likely to buy. Furthermore, HP had to ensure that it could meet the generated demand adequately and on time. This required highly accurate order forecasts to ensure that the third-party warehouses, which house and ship the products on behalf of HP, could ef ciently manage inventory. Inaccurate order forecasts could result in understaf ng, leading to order cancellations and cus- tomer dissatisfaction, or overstaf ng, leading to ware- house penalties and increased costs. Determining which marketing channel to adopt to reach different customers for different products was also a challenge. A number of marketing channel options are available, including emails and news- letters, comparison-shopping sites (e.g., cnet.com, zdnet.com), af liate sites (e.g., fatwallet.com, commis- sionjunction.com), paid search results (e.g., sponsored links in google.com, yahoo.com, and bing.com), and HP's corporate website (HP.com). This entire effort had to be executed on an optimized marketing budget.
Summarily, the HPDirect.com team was faced with the threefold task of acquiring new customers, devel- oping recently acquired customers into loyalists by offering them the right product choices at the right time and through the right marketing channels, and retaining loyalists by ensuring a satisfying and seam- less user experience through cost-ef cient strategies. To meet these challenges, HP had to gain a bet- ter understanding of its customer base, including cus- tomer purchase preferences and expected purchase behavior. It had traditionally relied on ad hoc busi- ness rules, framed by marketers' perceptions, for driv- ing marketing decisions. These decisions were not backed by robust data; because they were primar- ily based on judgment, they were not optimal. Thus, HP had an immediate need to reengineer its exist- ing processes, to equip them with objective insights into customers' purchase preferences, using more sci- enti c methods. As Suresh Subramanian, then vice president and general manager of HPDirect.com, acknowledged, “HP's ability to launch and drive an e-commerce business pro tably within a multichan- nel environment depends heavily on the application of OR techniques and advanced analytics to optimize daily business decisions.” Solutions The data scientists in HP Global Analytics (GA) addressed this need for a reliable and data-driven analytical approach. They used mathematical pro- gramming, Bayesian modeling, regression modeling, and time-series forecasting techniques to develop a set of data-driven solutions for customer acquisition, development, and retention. The rst set of solutions, Solution A, enables the HPDirect.com team to understand the impact of key marketing activities on online traf c. A multiple lin- ear regression (MLR) model helps identify the key drivers of online traf c and quanti es their relative impact. In addition, traf c (i.e., number of visitors to the online portal) to each product category WebDownloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR 50 Interfaces 43(1), pp. 48–61, © 2013 INFORMSpage is predicted using time-series forecasting tech- niques. A budget optimization framework, based on linear programming (LP), further helps the marketing teams in allocating their budgets across the various marketing channels. This framework seeks to maxi- mize total revenue, based on the return on marketing spend of each marketing channel and product cate- gory combination.
The second set of solutions, Solution B, helps the marketing teams to identify which customers are most likely to purchase, and when. It provides a thor- ough understanding of the technology needs of these customers, enabling the teams to target them with differentiated messaging, through the most suitable channel (e.g., email, printed mail). It also provides product recommendations based on the customers' historical purchases with HP. The third set of solutions, Solution C, helps to increase ef ciency of downstream warehouse oper- ations by improving the accuracy of customer order forecasts, using a hybrid forecasting approach that combines time-series modeling with MLR. We describe each solution in detail below.
Solution A Solution A comprises demand-generation models for traf c and optimizing return on investment. Cus- tomers can choose to buy technology products from either the online channel or retail stores. HP mar- keters must choose which products to promote, which customer segments to target, and which marketing channels to use. The variety of such choices and lim- ited marketing budgets make developing an opti- mal marketing strategy a challenge. Solution A helps address these challenges by (1) identifying the key drivers of online traf c and quantifying their contri- butions, and (2) optimally allocating the marketing budget to different marketing channels.
Identifying Key Drivers of Online Traf c and Quantifying Their Contributions Customer visits to HPDirect.com originate from both paid and unpaid traf c sources. Unpaid sources include search results from websites (e.g., google.com, bing.com, yahoo.com) and redirects from HP's corporate website (HP.com). Paid sources include af liate websites (e.g., fatwallet.com, commissionjunc- tion.com), paid search results (e.g., sponsored links in google.com, yahoo.com, bing.com), comparison shopping sites (e.g., cnet.com, zdnet.com), emails, and newsletters. To identify the key drivers of traf c to HPDirect .com, we developed traf c forecast models—one for each of the four main product category Web pages:
notebooks, desktops, printers, and accessories. Each product category has multiple Web pages associated with it. The objective of the models is to forecast visits across the set of all Web pages associated with each product category. The models are based on a combi- nation of time-series forecasting and regression tech- niques, and are developed using statistical software, such as SAS Enterprise Guide, Oracle's Crystal Ball, and JMP. We list below the major steps adopted in this new methodology. Step 1.Visualization. We rst visualize the histor- ical traf c data (i.e., the number of online visits) consolidated at a weekly level, over two years, to detect seasonality and trends. We observe that traf c peaks during major sale seasons (Black Friday, Cyber Monday, Christmas holidays) and troughs before the start of the back-to-school season.
Step 2.Data codi cation. To relate the seasonality and trends observed to speci c marketing activities and sale seasons, we codify all the marketing activ- ities and sale seasons over the two-year time frame.
For example, the presence of a speci c marketing activity or a major sale season, such as back-to- school or Christmas, during a given week is codi ed as a binary variable with a value of 0 (absent) or 1 (present), and the number of HP notebook promo- tions in a given week is codi ed as an ordinal variable with values 1 121 31 0 0 0 1 n . This is done to enable the inclusion of these variables in an operations research (OR) statistical modeling exercise later. Step 3.Time-series modeling. Time-series models, autoregressive integrated moving average (ARIMA), are used to generate traf c forecasts for each of HPDirect.com's main product category Web pages.
The basic principle is to identify the main compo- nents of Web traf c—trend and seasonality—and to then model the two components based on historical values of Web traf c.Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 51Step 4.MLR models. Parallel to the time-series modeling effort, we develop regression models for each product category Web page to understand the in uence of key drivers on traf c. The data from the codi cation step were used as the independent variables and the weekly traf c data as the depen- dent variables. The statistically signi cant variables were then identi ed through an iterative process. For example, the notebook traf c regression model (with a R-square value of 0.7) had the following types of independent variables with a statistically signi cant impact on traf c to the notebook category pages: •marketing spend on search and display advertisements; •use of coupons on af liate sites; •holiday events; and •presence of desktop- or supplies-related promo- tions in the email message and the number of such email campaigns over the forecast period. The rst three signi cant variables had a positive impact on traf c; however, the fourth had a negative impact. Step 5.Model integration. We take the output of the time-series model as the baseline traf c forecast.
The integration of the regression models (Step 4) and the weekly baseline traf c forecasts (Step 3) provides the recalibrated traf c forecasts based on upcoming planned marketing activity. The models are integrated as follows: (1) rst, the coef cients of the signi cant variables in the regression equation are expressed in percentage terms to re ect their relative contribution in explaining the regression forecast; (2) binary vari- ables created earlier are used to estimate the relevance of these signi cant variables; for example, if availabil- ity of coupons was a signi cant variable, then the user marks a 1 against the binary variable created for that variable; and (3) these binary values are multiplied by their percentage contributions. The summation of these products across all the signi cant variables pro- vides the factor to multiply by the baseline ARIMA forecast to generate the nal forecast. Step 6.Model validation. To validate the forecast results, we partition the historical traf c data into training and test sets, and apply the model devel- oped using the training dataset to the test set to validate results. Step 7.User interface. Model coef cients from the regression models and the baseline traf c forecasts from the time-series models are embedded in a spreadsheet model (i.e., tool) to allow marketing plan- ners to develop alternate marketing budget allocation scenarios. This solution replaced the legacy approach based on simple moving average forecasts, raising the fore- cast accuracy from a previous value of 35 percent, which we measured in terms of 1 mean abso- lute percent error (1-MAPE), to 60 percent for each 12-week forecast period. We considered a few other approaches to Web traf c forecasting, including a neural network-based approach to decompose the time series of Web traf c into unique components that can be used to predict future patterns (Aussem and Murtagh 2001). Genetic algorithms have also been used to forecast Web traf c, as Chen (2011) illus- trates. However, to facilitate user adoption, ease of implementation, and the need to quantify and include the impact of marketing activity on Web traf c, we adopted the approach we described in the previous paragraphs. These traf c forecast models enable HPDirect.com management to understand the impact that key mar- keting activities have on Web traf c. The marketing team uses these demand-generation models to plan Web traf c scenarios at the beginning of each quar- terly planning cycle and during off-cycle planning exercises. Since 2009, HPDirect.com has used these models, which have undergone quarterly refreshes since we originally developed them.
Optimally Allocate Marketing Budget To enable the HPDirect.com marketing and plan- ning teams to ef ciently allocate their budgets across online marketing activities (e.g., search marketing, af liate marketing, email marketing) for promot- ing various product categories, we developed an LP-based marketing budget optimization framework.
This framework seeks to maximize total revenue based on the return on marketing spend for each mar- keting activity and product category combination. We took the following approach. 1.We looked at the optimization effort as a con- tinuous process that starts with collecting information on the historical return on investment (ROI), which is de ned as revenue from a marketing vehicle per dollar spent on that vehicle, associated with each individ-Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR 52 Interfaces 43(1), pp. 48–61, © 2013 INFORMSual marketing vehicle; we then forecast expected ROI from each vehicle and used the expected ROI to allo- cate across vehicles. ROI forecasts are generated using time-series forecasting techniques. Forecasts of rev- enue and expenditure are generated separately prior to computing the forecasted ROI. 2.Next, we developed a multidimensional opti- mization model to optimize revenue; the model accepts the following as input: expected ROI, con- straints that operate based on marketing vehicle (i.e., the user-speci ed minimum and maximum invest- ment allocation bands by vehicle), and overall bud- getary constraints. 3.For the second and subsequent budget alloca- tions, we used the ROI values to date to revise forecasts that eventually determine future budget allocations.
As with the traf c forecast model, this methodol- ogy evolved in phases. To simplify the optimization problem across all the phases, ROI is assumed to be a linear function of spend. The marketing manager responsible for managing traf c to the website from the particular source (e.g., search marketing, af liate marketing, email marketing, display advertising), in consultation with the HPDirect.com's director of mar- keting, prescribes the maximum and minimum spend constraints. Phase 1 of the solution used a simple LP approach, developed in MS Excel, as a proof of concept of the optimization idea. Although Phase 1 of the approach used ROI point values to prescribe budget allocations, Phase 2 sought to recognize that variability in ROI is a reality and recommendations for budget alloca- tion must use an ROI that is most relevant for the period. These differences arise because the set of ROI data points is a time-series distribution with its inher- ent seasonality and trend components. In Phase 2, we took steps to do the following.
1.Remove the seasonality and trend from the ROI; 2. t a distribution to the ROI data points (with seasonality and trends removed); 3.perform Monte Carlo simulation runs based on the parameters of the tted ROI distribution; 4.add back seasonality to the outcomes of the simu- lation runs with seasonality and trend components; and 5.use the adjusted (i.e., expected) ROI values to determine budget allocations using the linear program that could maximize overall revenue for the period in question. In contrast to a judgment-driven allocation, the Phase 2 approach is now providing valuable guidance and direction to HPDirect.com's director of marketing on how best to allocate the marketing budget across various marketing vehicles. The appendix describes the LP formulation used to allocate the marketing budget.
Solution B Solution B, the intelligent cube, is a customer- targeting solution that improves the effectiveness of the direct marketing campaigns. HPDirect.com's mar- keting team uses email and traditional direct printed mail as its primary communication medium to tar- get customers with its product offerings. Tradition- ally, these campaigns have been broad based with limited use of available customer information. This has resulted in contacting customers too frequently, leading to poor conversion rates (number of peo- ple who buy a product from HPDirect.com divided by the number of people who received an email or printed mail) and increased numbers of customers who unsubscribe (number of people who opt out from being contacted by HP for marketing offers divided by the number of people who received an email or printed mail) from HP mailing lists. HP GA, in collaboration with HP's corporate marketing cus- tomer intelligence (CM-CI) team, developed an inno- vative targeting framework that helps the business to optimally engage with our customers by identifying the customers most likely to purchase a product and offering them meaningful product choices at the right time using the most appropriate messaging (content of the emails and printed mails based on the cus- tomer 's life stage, needs, and attitude toward technol- ogy products). We call the data repository that brings together all these OR-based solutions and models the intelligent cube. HPDirect.com's marketers use the intelligent cube for effective customer engagement.
The OR-based models and solutions that comprise the intelligent cube are built using SAS software. They use all the customer information available to HP (and legally acceptable for marketing purposes), including customer demographics, psychographic (e.g., interestDownloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 53in electronic goods, interest in music) data, transac- tion history, campaign response (number of emails opened divided by number of emails sent), Web behavior (number of online visits, time spent per visit), and customer service data (number of service tickets raised for a product category, problem resolu- tion rate). These models provide a multidimensional view of expected customer behavior across dimen- sions such as product purchase, purchase timing, and the expected marketing channel used for the pur- chase, and serve as the basis for planning and exe- cuting customized marketing messages and product offers. The speci c dimensions of the intelligent cube are listed below. •Customer segmentation: This helps the market- ing team to understand to whom it should sell (whom to sell). It creates customer segments based on atti- tudes toward technology. These attitudes are inferred using a primary research survey on a sample of customers and later extrapolated to the entire cus- tomer base using statistical techniques (i.e., discrim- inant function analysis). The segment information helps in driving differentiated messaging to HP cus- tomers based on their latent preferences for technol- ogy products. •Product needs: The marketing team at HPDirect.com must offer the most appropriate set of products to its customers to increase revenues, improve customer satisfaction, and enhance loyalty.
This dimension helps the team to understand what it should sell (what to sell), making logical next- product recommendations to customers based on their historical purchase patterns. These next-product recommendations are based on Markov chain mod- els; the model estimates a transition matrix, which gives the probability that a customer will buy a product, based on that customer 's historical product purchases.
•Purchase timing: The marketing team sends mul- tiple marketing communication emails to customers.
The timing of these emails is important because all customers do not respond to such communications.
This dimension helps the team to understand when it should sell to a customer (when to sell), predict- ing the time that the customer is most likely to make the next purchase. Historical product transaction data are used to develop Bayesian hierarchical models that estimate the rate of purchase and probability of churn (i.e., an estimate of whether a customer will cease to buy from HP) to result in a nal-purchase probabil- ity at a speci c time in the future. Each customer is assigned a propensity-to-purchase score, which helps the marketing team to tailor the campaign timing. •Marketing channel preference: Customers have different response propensities to marketing channels (i.e., email or printed mail). This dimension helps the marketing team to understand how it should sell (how to sell), predicting the most appropriate chan- nel to use in targeting customers. Historical campaign response data are used to build response models for both email and printed mail to help the team identify the right channel of communication to customers. Each customer is scored on each dimension to cre- ate a secure intelligent cube customer data reposi- tory, in which customer segments and the product recommendations are coded as categorical variables for ease of use. Marketing managers have access to this data repository, which helps them create targeted customer lists in a much shorter time as compared to legacy processes. Application of the intelligent cube helps in marketing the right product, at the right time, with relevant messaging, and via an appropri- ate marketing channel. The implementation of the data repository involves a close collaboration between HP GA, HP CM-CI, HPDirect.com, and HP's infor- mation technology (IT) organizations. GA and CM-CI teams build, validate, and test individual models.
The IT team then implements these models on pro- duction servers and refreshes the scores monthly.
HPDirect.com's marketing teams use these scores for effective customer engagement. The scores for each dimension are converted into binary variables for ease of use. For example, the purchase-timing probabil- ity scores are arranged in descending order; the top 30 percent of scores are then coded as 1 (i.e., high likelihood of making a purchase), and the remain- ing 70 percent are coded as 0 (i.e., low likelihood of making a purchase). The other scores are similarly converted into binary variables. Table 1 provides an example from a January 2011 campaign of how the intelligent cube solution is used to design the target customer list for a personal computers (PC) conver- sion at HPDirect.com. The example illustrates how theDownloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR 54 Interfaces 43(1), pp. 48–61, © 2013 INFORMSCustomer Buy-in-next- Buy-through- attitudinal three-months Buy-PC email Customer segment ag (0/1) ag (0/1) ag (0/1) ID (whom to sell) (when to sell) (what to sell) (how to sell) 123 Digital techie 1 0 1 456 Sentimental 1 1 1 traditionalist 789 Successful adapters 0 0 1 Table 1: The table illustrates an intelligent cube; of three possible cus- tomer segments, ID 456 receives the email for the PC campaign because this customer satis es (i.e., 1D yes ) all three criteria: product purchase likelihood, purchase time frame, and email channel preference.
intelligent cube solution helps the marketers to iden- tify the customers who are most likely to buy a PC in the next three months and, of those identi ed, the customers who prefer to be contacted by email. Based on this information, marketers identify the right set of customers to whom they should market the prod- ucts, thus avoiding contacting the wrong set of cus- tomers (i.e., those who would not be interested in these products). The effectiveness of the intelligent cube solution in marketing campaigns is measured by using a test-and-control methodology. For the PC conversion campaign, we select the test group based on the appli- cation of the intelligent cube solution (see Table 1); the control group consists of randomly selected cus- tomers. The results show that the test group per- formed better across all key metrics, including email open rate (i.e., number of emails opened divided by number of emails sent), email click rate (i.e., num- ber of emails on which customers clicked divided by number of emails sent), conversion rate, and sales per message delivered (i.e., total sales divided by number of emails sent) (see Table 2). We continually validate and test the intelligent cube solution. Each key dimension of the intelligent cube solution is explained brie y below.
Customer Segmentation The rst step in developing a framework for effective customer engagement is to understand customer atti- tudes and technology needs. We de ned six customer segments based on responses to survey question- naires, which we collected by surveying a represen- tative sample of HP customers. Next, we classi ed Circulation Sales per (messages Open Click Conversion message Message sent) (%) rate (%) rate (%) rate (%) delivered Intelligent cube 20 11 096 0 094 4 083 $0 008 test group Control group 80 8 023 0 073 1 099 $0 003 Percent lift 45 29 143 184% (increment over control) Table 2: To measure the effectiveness of the intelligent cube solution, we compare the key metrics across the test and control groups for the PC conversion campaign, which used this solution to target to prospective PC buyers. The table shows that all the key metrics improve. The met- rics are de ned as follows: open rate Dnumber of emails opened/number of emails sent; click rate Dnumber of emails clicked/number of emails sent; conversion rate Dnumber of orders/number of emails sent; sales per message delivered Dtotal sales/number of emails sent.
each HPDirect.com customer into one of these six segments using a statistical algorithm that includes demographic and transactional variables as predic- tors. We evaluated several classi cation techniques, including classi cation and regression trees, k-nearest neighbor, and multinomial logistic regression; we chose linear discriminant analysis (LDA) (Hastie et al.
2001) because it offered the best classi cation accu- racy (62 percent). The appendix provides details of using LDA for customer segmentation. Having clas- si ed the customers into one of the six segments, we developed detailed pro les for each segment based on a vast array of internal data available. In a June 2010 marketing campaign to introduce new products, the marketing team applied this segmentation scheme.
To measure the impact, we followed a test-versus- control approach. The control group comprised half the customers; these customers received generic email messages (with no input from segment pro les). The test group (the other half) received segment-speci c customized messages based on each customer 's seg- ment pro le (e.g., product preferences, interests). Key metrics, including sales, average order size, conver- sion rate, and sales (in dollars) per delivered message, were computed across the test and control groups to measure the effectiveness of customized messaging.
The test group performed better across all key met- rics. Table 3 illustrates incremental bene ts across key metrics from this campaign.Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 55Incremental Incremental Incremental dollar Incremental Incremental average conversion sales per message orders (%) sales (%) order size (%) rate (%) delivered (%) 26.77 42.45 12.37 27.04 42.45 Table 3: The table illustrates bene ts for a new-product introduction cam- paign, with messaging based on the attitudinal customer segmentation scheme. The values shown are incremental (i.e., (test-control/control) 100 ). The data show that differentiated messaging to our customers leads to sales increases.
Because these results were encouraging, the seg- mentation scheme was also used extensively during a December 2010 holiday campaign; the objective was to deliver increased sales to HPDirect.com by improv- ing conversion rates and sales per message deliv- ered. The results showed a 58 percent incremental conversion rate, that is, (test group conversion rate minus control group conversion rate) divided by (control group conversion rate), and 33 percent incre- mental dollar (sales) per printed mail, based on the test-versus-control methodology (see Table 4).
Product Needs We use historical transaction behavior to predict the most likely product that a given customer will pur- chase next from HPDirect.com. For example, a con- sumer purchasing a desktop computer from HP might need a printer next; such propensities can be iden- ti ed by mining and analyzing transaction patterns of all HP customers. We built the model using a Markov chain methodology, where we assume that a customer 's product choice is a function of that customers' past HP purchases. Based on historical Circulation Sales per (messages Conversion message Message sent) (%) rate (%) delivered Test group 95 4 005 $13 05 Control group 5 2 06 $10 01 Percent lift 58 33% (increment over control) Table 4: The table compares key metrics for test-vs.-control samples for a December 2010 printed-mail holiday campaign; messaging was based on attitudinal segments. Percent lift highlights the improvement in con- version rates and sales per message delivered. product transactions, we developed a probability matrix—the probability of buying a particular prod- uct X, given that the customer has purchased Y. The conditional probability P Y X is estimated as the pro- portion of transaction pairs in which Xfollowed Y in all transactions that followed Yand is assumed to be stationary for the period of interest. For cus- tomers making only one transaction with HP, we use their last transaction to predict their next prod- uct purchase ( rst-order Markov chain); for customers making multiple transactions, we consider their last two transactions (second-order Markov chain). We then estimate the propensity of each customer pur- chasing 1 of 20 possible subcategories of products (based on HPDirect.com's internal product classi- cation). Because marketing campaigns are typi- cally executed at the product subcategory level, the product-recommendation model is developed at this level (e.g., notebook: everyday computing).
The product-recommendation model was tested during a December 2010 holiday season promotional activity in which customers were offered attractive discounts in all major product categories. Although the test group was sent speci c emails featuring the top-three product recommendations based on the model, the remaining customers received generic emails featuring a variety of HP products. Table 5 shows that the product-recommendation model pro- vided excellent results, with incremental bene ts across all key metrics.
Purchase Timing The ability to predict which customers are likely to make a purchase with HP in the near future Circulation Sales per (messages Open Click message Message sent) (%) rate (%) rate (%) delivered Intelligent cube test group 3 24 06 5 07 $0 053 Control group 97 8 06 1 04 $0 008 Percent lift 184 306 549% (increment over control) Table 5: The table compares key metrics for test-vs.-control examples for a sales campaign that used the product-recommendation model. This data show that applying the model's product recommendations led to improved campaign results.Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR 56 Interfaces 43(1), pp. 48–61, © 2013 INFORMSgreatly enhances the effectiveness of marketing cam- paigns. Estimating a customer 's repurchase propen- sity involves addressing two fundamental questions. 1.What is the likelihood that the customer will churn (i.e., stop purchasing from HP)?
2.What is the rate at which the customer purchases HP products? We model these two factors, probability of churn and transaction rate, using transaction variables (e.g., timing, frequency of purchases) to determine each customer 's repeat-purchase propensity. Developing this propensity is a two-step process, as we describe below.
Step 1.We build a Bayesian hierarchical model to estimate the values of churn probability ( p) and rate of transaction ( ‹). A Markov chain Monte Carlo (MCMC) algorithm is employed to sample from the posterior distribution of the parameters ( pand ‹5. The prior distributions of the following parameters are chosen using historical data: •number of transaction made by the jth customer follows a Poisson process with rate parameter ‹ j; and •probability of dropping out after each purchase is binary with probability p j.
The medians of the posterior distributions, sampled by the MCMC algorithm, are used as estimates of ‹ j and p j (Pal et al. 2010).
Step 2.To simplify the computational complexity of the Bayesian hierarchical model, we develop a scal- able regression-based approximation of the same; we build two polynomial regression models to compute the parameter estimates of ‹ j and p j.
Once we obtain from the regression model the esti- mated values of ‹ j and p j for each customer, we com- pute the probability of a customer making a purchase from HP in the next kperiods. The Purchase-Timing Model section in the appendix provides details.
Figure 1 highlights the advantages of the proposed framework when compared to random targeting, based on an out-of-sample validation where cus- tomers are scored according to their repeat propensity and then tracked into the future to see if they actually make a repeat purchase. By targeting the top 40 per- cent of customers suggested by the repeat-scoring model, we are able to capture (predict accurately) 75 percent of all likely repeat buyers (see Figure 1). Figure 1: The diagonal line denotes random targeting (e.g., if 10 percent of customers are targeted randomly, we can expect to capture 10 percent of repeat purchasers). The curved line above the diagonal line shows the percentage of purchasers that the model will successfully capture if we target the top Xpercent of customers based on the results of the repeat- scoring model.
Marketing Channel Preference HPDirect.com primarily uses two marketing medi- ums to target its customers—email and conventional printed mail. Contacting customers through an inap- propriate medium may result in decreased response rates and higher opt-out rates. Therefore, determining the optimal mix and channel type to use for targeting is important. We build individual logistic regression response models (Hosmer and Lemeshow 2000) for each channel and score all customers based on these models. To build these models, we use campaign response data, which consist of information such as the channel used to make the purchase (e.g., email, printed mail) and other data sources (e.g., demo- graphics, psychographics). We assign each customer a propensity score, which denotes the likelihood of buying via the respective channel. For a particular customer, if the propensity score obtained for pur- chase through an email channel is higher than that obtained for the printed-mail channel, then we infer that the customer is more likely to respond to an email campaign than to a printed-mail campaign. We built the channel response models by splitting the data into development and validation samples using a 70:30 ratio (i.e., we built the model using the devel- opment sample and then tested it on the validation sample). Some variables that stood out as signi - cant in the model include the customer 's length of relationship with HP, history of buying electronics items, and credit card usage. Using the email response0 20 40 60 80 100 10 20 30 40 50 60 70 80 90 100 Random targetingRepeat scoring % Customers targeted % Repeaters captured Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 57model, HPDirect.com was able to capture 66 per- cent of the responders by targeting 30 percent of cus- tomers, and the printed-mail response model helped capture 71 percent of responders by targeting 30 per- cent of the customers. Figure 2 illustrates the lift of both models.
Modeling a binary response (buy or no buy) is a common marketing practice with many available tools, including logistic regression, decision trees, neural networks, and random forests. We chose logis- tic regression because it provides probability scores (of making a purchase) on a continuous scale and can scale easily over millions of observations.
Solution C Solutions A and B address the application of OR tech- niques to improve the sales drivers for the online sales channel. In this section, we discuss Solution C, which improves downstream warehouse operations by increasing demand predictability. To ensure that HP delivers products to its cus- tomers on time, the third-party warehouse that main- tains and ships inventory to US customers on behalf of HP must receive highly accurate order forecasts.
Inaccurate forecasts result in inadequate inventory, potentially causing customer dissatisfaction and order cancellations, increased costs from holding inventory, or warehouse penalties because of overstaf ng or understaf ng. Creating an accurate forecast is compli- cated because the forecast must consider both current trends and the surge from the demand-generation activities. Orders received depend on factors that we classify into three broad categories: 1.seasonality, including day of week, month of year, and back-to-school season; and 2.special events, including Black Friday, Cyber Monday, and tax holidays; 3.marketing events, including holiday sales, pro- motional emails, coupons, and ads on af liate sites. Therefore, any forecast produced must account for the speci c marketing activity or event during the period in question, in addition to the seasonality for that duration. The efforts to develop order forecasts for HPDi- rect.com have spanned multiple phases. Initially, we developed a simplistic multiple linear regres- sion (MLR) model, including 90 independent vari- ables that primarily comprised warehouse order data.
To incorporate the effects of marketing activities on warehouse orders and to improve forecast accuracy, we then developed a hybrid forecasting approach that combines time-series modeling with MLR as part of Phase II. The resultant model provided a fore- cast accuracy improvement of 15 percent (in terms of MAPE). This model is much simpler; its 49 indepen- dent variables and 15 lagged variables produced an adjusted R–square of 72 percent. We collected histori- cal data for over 80 variables each day for 30 months, and applied these three steps in the model-building process. Step 1.Create a baseline forecast using the ARIMA technique. To arrive at the baseline order forecasts, we used the Box-Jenkins method, as we describe below.
1.Remove the outliers from the cleansed time- series data (i.e., historical order quantities) through normalization. 2.Eliminate the nonstationarity of data by taking the log of the differenced series ( O tƒ O tƒ 1) and obtain- ing Log 4O t=O tƒ 15 . O tand O tƒ 1 refer to orders at times t and tƒ 1.
3.Import the resulting data into SAS software and apply the ARIMA procedure.
4.Back transform the forecast obtained to obtain the baseline order forecast. Step 2.Build a regression model to incorporate the impact of marketing activities and seasonality factors, as we describe below.
1.Create lagged variables (up to a three-day lag) for the marketing variables (e.g., email).
2.Drop statistically insigni cant variables and iter- atively estimate the regression coef cients; we chose the model with the greatest adjusted R-square and the least number of variables. 3.Validate the model using the validation data set.
The regression coef cients of the nal regression model and the baseline order forecast from the previ- ous step are embedded in a user-friendly interface to enable the end user to estimate the nal forecast.
Step 3.The end user enters the relevant promotion- related variables into the tool to determine the nal recalibrated forecast.Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR 58 Interfaces 43(1), pp. 48–61, © 2013 INFORMSFigure 2: In each model, the diagonal line denotes random customer targeting. The dotted curved line above the diagonal line shows the percentage of purchasers the model will successfully capture (predict) if we target the top Xpercent of customers based on the model's suggestions.
To arrive at the nal recalibrated forecast the user does the following: 1.Inserts the baseline forecast for the quarter in the forecast column. The output is regenerated and provided to the operations team at the start of each quarter. 2.Enters the values for the various marketing activities (e.g., discount, sale, campaigns) in the cor- responding columns to determine the nal forecast. HPDirect.com and the third-party warehouse cur- rently use this solution. Because the variables in u- encing orders vary over time, we refresh the model quarterly, introducing new variables, as required, to improve forecast accuracy.
Impact and Bene ts By using OR in HP's decision-making process across its e-commerce value chain, the company has undergone a transformational change in managing its online store. This has resulted in quanti able nan- cial bene ts, operational improvements, and a differ- ent operating paradigm. The work presented in this paper, addressing customer targeting, optimization of marketing budgets, and improved order forecasting, has resulted in additional revenue of $117 million over three years. This impact comes from •an increase of 2.6 percent in annual traf c, result- ing in $44 million in incremental sales; •a 60 percent conversion rate lift and a 15 percent increase in average order size, resulting in $63 million in additional revenues; and •a cost reduction of $2 million through better inventory management; this equates to $10 million in incremental sales, assuming a sales margin ratio of 5:1. Suresh Subramanian and Dave Hill, HPDirect.com key business stakeholders, have validated this impact.
Mr. Subramanian, who is now global vice president of customer and market insights, says, “The operations research-based solutions are at the core of all oper- ational decision at HPDirect.com and have helped build a strong management culture of rapid testing and optimization. Since 2009, these solutions have driven an award-winning customer experience and incremental revenue of $117 million” (Figure 3). In addition to the quanti able bene ts, these solu- tions have resulted in many soft gains. Many external Figure 3: From 2009 to 2011, the application of the OR techniques has contributed incremental sales of $117 million to HPDirect.com.Email model Printed-mail model Model-Dev Random % Customers targeted % Customers targeted % Repeaters captured % Repeaters captured Model-Dev Random $25$35 Revenue impact ($million) $57$117 2009 2010 2011 Total Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 59groups have recognized HP for the effectiveness of its campaigns; these include MediaPost Communications (2011) and Web Marketing Association (2009a, b). HP also won an honorable mention for its success story of creating value through deployment of OR solutions (Shared Services and Outsourcing Network 2011).
Some of the solutions showcased here have competed and won in HP internal competitions, including HP TechCon (organized by HP Labs worldwide) and HP Parasparam (India), and also resulted in a patent ling for HP.
Conclusion The OR solutions developed and deployed at HPDi- rect.com re ect a cultural shift in how a fast-paced and dynamic business can be managed; they are increasingly seen as best-practice cases within HP. The OR techniques applied by the data scientists at HP to solve business issues in its e-commerce business are novel, and provide a powerful and scalable set of solutions to improve key business drivers and ensure pro table growth. These solutions directly in uence HP's business strategy of selling more through its e-commerce model. Although the tools discussed in this paper have been successfully implemented in HP's US e-com- merce business, they are scalable and portable across different geographies, business units, customer seg- ments, and cross-sales motions (i.e., retail versus online). HP plans to expand to 23 countries glob- ally under an integrated e-commerce platform; these well-tested solutions should be readily applicable and yield additional revenue in the coming years.
Appendix Marketing Budget Optimization HP's LP-based marketing budget optimization formulation follows.
Objective function Maximize total sales DROI 1x SPEND 1C ROI 2x SPEND 2 C C ROI nx SPEND n0 Constraints SPEND41 1 21 31 0001 n5 MAX SPEND 41 1 21 31 0001 n5 , SPEND 41 1 21 31 0001 n5 MIN SPEND 41 1 21 31 0001 n5 , n X i D 1 SPEND i maximum total budget, n X i D 1 SPEND i minimum total budget, where 11 21 31 0 0 0 1 n represent all marketing channel and product category combinations; SPEND nis the suggested budget allocation for combina- tion n(i.e., the decision variable); ROI nis the ROI for combination n; ROI is de ned as the ratio of revenue from a marketing vehicle and dollars spent on that vehicle; MAX SPEND nand MIN SPEND nrepresent maximum and minimum boundaries on SPEND n.
Customer Segmentation We constructed discriminant functions using purchase transactions, customer demographics, and lifestyle attributes on the sample data, such that Lk D p X i D 1b ik x iC c1 where kD 11 0 0 0 1 6 (customer market segments), L k is the discriminant function for the kth segment, b ik values are discriminant coef cients, x i values are the predictors, and c is a constant. We estimate b ik by LDA using SAS soft- ware. Each customer is scored and classi ed into one of the groups using Arg Max i( L 11 0 0 0 1 L i1 0 0 0 1 L k). The model achieved correct classi cation of 62 percent. After scoring HPDirect.com's entire customer base, we validated the pro- les of the scored segments against the original pro les and found that the segments were valid.
Purchase-Timing Model Our repeat-purchase framework is a solution to the prob- lem of predicting future purchase patterns of customers, as Fader et al. (2005) discuss. Although our model uses the same input variables (i.e., rst transaction date ( t 1 ), last transaction date ( t x ), and the number of transactions ( x)) as Fader et al. do, we develop a full Bayesian implementation that gives us total control of the output parameters and is scalable to millions of customers. The probability of customer jmaking a purchase in the next kperiods is given by 41 ƒ p j54 1ƒ exp 8ƒ k‹ j951 (1) where kmay be any unit of time (e.g., 30 days, six months, or one year), and p j and ‹ j are parameter estimates for the probability of churn and rate of purchase, respectively.
Although the Bayesian hierarchical model was effective and correct in identifying customers who are likely to be repeat purchasers, it was not ef cient in scoring millionsDownloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR 60 Interfaces 43(1), pp. 48–61, © 2013 INFORMSof customers (each customer requiring hundreds of itera- tions of the MCMC). HP has millions of customers, includ- ing individual consumers, micro businesses, and small and medium-size businesses.
The computational complexity of the Bayesian hierar- chical model means that although the results are promis- ing, they are not ready for use in HP's operations. Hence, we perform the second step, developing a fast, scalable regression-based approximation to this model. The Bayesian hierarchical model depends only on three variables for each customer, t 1 , t x , and x. In addition, a property of a good scoring algorithm is that two customers with the same values of t 1 , t x , and xhave similar scores. Therefore, any suf ciently parameterized regression model will capture all patterns, including nonlinear effects and interactions. We use two polynomial regression models for the param- eter estimates of ‹ j and p j. To accomplish this, we rst estimate ‹ j and p j for three million customers using the Bayesian model, and then use the logarithms of estimates as responses in two polynomial regression models with pre- dictors t 1 , t x , and xand an interaction variable (frequency of buying) derived from t 1 , t x , and x0To guide our choice of the degree of polynomial t, we explore the nonparamet- ric t from a generalized additive mode (GAM), where the response is modeled as a sum of spline functions of individ- ual predictors (Xiang 2001). A polynomial t is faster than a GAM, and easily accommodates scoring large databases.
Pal et al. (2012) provide further details of our solution.
Once the estimated values of ‹ j and p j for each cus- tomer have been obtained from the regression model, they are entered into Equation (1) to obtain the probability of each customer making at least one purchase in the next k periods.
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Xiang D (2001) Fitting generalized additive models with the GAM procedure. Accessed February 17, 2012, http://www2.sas .com/proceedings/sugi26/p256-26.pdf. Rohit Tandon is the Vice President and WW Head of Global Analytics at Hewlett Packard Company (HP) and leads HP’s 1200+ analytics organization globally. He has a master’s degree in computer application from the Institute of Management and Technology, Ghaziabad, and is a com- puter science graduate of St. Stephen's College, Delhi Uni- versity. He is a Certi ed Master Black Belt in Six Sigma and a Certi ed Quality Leader in Six Sigma. Arnab Chakraborty is the Director—Global Analytics at Hewlett Packard Company (HP). He is responsible for part- nering with senior executive leadership teams within HP and drives the deployment of analytics solutions across sales, marketing, and supply chain domains to HP’s busi- ness groups. He holds an MBA from National Institute of Industrial Engineering, Mumbai, India, and a degree in mechanical engineering from National Institute of Tech- nology, Rourkela, India. He is also certi ed in production and inventory management from APICS (the association of operations management). Girish Srinivasan is Senior Manager—Customer Insights at Hewlett Packard’s (HP) Personal Systems Group. He has experience deploying analytics solutions globally in nan- cial services, technology, and Pharma companies. At HP he applies the latest in data management technology and advanced analytics techniques to uncover evolving cus- tomer preferences and market trends. He earned master’s degrees in industrial engineering and applied statistics from Pennsylvania State University and a bachelor’s degree in mechanical engineering from Bangalore University. Manav Shroff is Senior Manager—Global Analytics at Hewlett Packard Company (HP). He is responsible for driving customer and market analytics across HP’s busi- nesses and customer segments. He earned a degree in com- merce from St. Xavier’s College, Calcutta, and an MBA inDownloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
Tandon et al.:
Delivering Pro table Growth for HPDirect.com Using OR Interfaces 43(1), pp. 48–61, © 2013 INFORMS 61marketing and nance from Kirloskar Institute of Advanced Management Studies. He is a GE-certi ed Green Belt and a Lean coach. Ahmar Abdullah is a database marketing/analytics pro- fessional with six years of experience in customer-focused advanced and predictive analytics, primary research, strate- gic analytic insight, and deep B2C customer knowledge. He earned a bachelor’s degree in chemical engineering from Zakir Husain College of Engineering and Technology (Ali- garh Muslim University) and a master’s in business admin- istration from Goa Institute of Management.
Bharathan Shamasundar is an analytics professional with over eight years of experience across nancial plan- ning and analysis, risk, and marketing analytics at Hewlett Packard and GE. He holds a bachelor’s degree in commerce from Bangalore University, a master’s in nance from the Institute of Chartered Financial Analysts of India, and an MBA from the Indian School of Business, Hyderabad. Ritwik Sinha holds a bachelor’s degree in statistics from Presidency College, Calcutta, and a master’s in statistics from Indian Statistical Institute, New Delhi. His expertise lies in building analytical models for customer targeting. Suresh Subramanian is VP—Customer Insights at Hewlett Packard’s Personal Systems Group. He has over 17 years of experience in middle and upper management at high-tech Fortune 200 companies, including a seven- year stint at Gateway, Inc., as vice president, marketing. He earned a master’s degree and a PhD in business adminis- tration and an undergraduate degree in engineering. Dave Hill is director of Hewlett Packard’s consumer exchange business, which encompasses the US e-commerce business and consumer call center. He holds a bachelor of science degree in civil engineering, an MBA, and a profes- sional engineering license and chartered nancial analyst designation. Prasanna Dhore is a customer-centric marketing execu- tive with broad experience encompassing key senior man- agement roles in marketing, CRM, customer engagement and development, analytics, and strategic planning across technology, nancial services, and publishing. He holds an MS in statistics and OR from New York University’s Leonard Stern School of Business, an MBA from Kansas State University, and a BS in mechanical engineering. He received his chartered nancial analyst designation in 2004.Downloaded from informs.org by [132.174.254.97] on 25 February 2017, at 11:20 . For personal use only, all rights reserved.
