IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v43y2013i1p48-61.html
   My bibliography  Save this article

Hewlett Packard: Delivering Profitable Growth for HPDirect.com Using Operations Research

Author

Listed:
  • Rohit Tandon

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Arnab Chakraborty

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Girish Srinivasan

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Manav Shroff

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Ahmar Abdullah

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Bharathan Shamasundar

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Ritwik Sinha

    (Hewlett Packard Global Analytics, Bangalore-560093, India)

  • Suresh Subramanian

    (Hewlett Packard, HPDirect.com, Cupertino, California 95014)

  • Dave Hill

    (Hewlett Packard, HPDirect.com, Cupertino, California 95014)

  • Prasanna Dhore

    (Hewlett Packard Corporate Marketing–Customer Intelligence, Palo Alto, California 94304)

Abstract

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 traffic 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 fulfillment 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.

Suggested Citation

  • 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, INFORMS, vol. 43(1), pages 48-61, February.
    • Handle: RePEc:inm:orinte:v:43:y:2013:i:1:p:48-61
    DOI: 10.1287/inte.1120.0661
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.1120.0661
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.1120.0661?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Peter S. Fader & Bruce G. S. Hardie & Ka Lok Lee, 2005. "“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 24(2), pages 275-284, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ayan Chatterjee & Debmallya Chatterjee, 2024. "A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(2), pages 337-361, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Oest, Rutger & Knox, George, 2011. "Extending the BG/NBD: A simple model of purchases and complaints," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 30-37.
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. Chen, Yanyan & Mandler, Timo & Meyer-Waarden, Lars, 2021. "Three decades of research on loyalty programs: A literature review and future research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 179-197.
    4. Angelovska, Nina, 2021. "Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(2), pages 156-170.
    5. Jian Wang & Murtaza Das & Amar Duggasani, 2016. "Predicting and measuring customer lifetime values for apartment tenants," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 2(2), pages 103-120, October.
    6. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
    7. Albers, Sönke, 2012. "Optimizable and implementable aggregate response modeling for marketing decision support," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 111-122.
    8. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    9. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    10. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    11. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    12. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    13. Yan Yan & Nicholas Resnick, 2024. "A high-performance turnkey system for customer lifetime value prediction in retail brands," Quantitative Marketing and Economics (QME), Springer, vol. 22(2), pages 169-192, June.
    14. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    15. Brighton, Henry, 2020. "Statistical foundations of ecological rationality," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-32.
    16. Joanna Holub-Iwan, 2021. "Management Information Systems of Public Health Behaviors based on Evidence in Medicine and Health Management," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 623-643.
    17. Rust, Roland T. & Kumar, V. & Venkatesan, Rajkumar, 2011. "Will the frog change into a prince? Predicting future customer profitability," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 281-294.
    18. Michael Haenlein, 2011. "A social network analysis of customer-level revenue distribution," Marketing Letters, Springer, vol. 22(1), pages 15-29, March.
    19. Donald R. Lehmann & Jeffrey R. Parker, 2017. "Disadoption," AMS Review, Springer;Academy of Marketing Science, vol. 7(1), pages 36-51, June.
    20. Park, Chang Hee & Yoon, Tae Jung, 2022. "The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆," Journal of Retailing, Elsevier, vol. 98(4), pages 647-666.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:43:y:2013:i:1:p:48-61. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.