IDEAS home Printed from https://ideas.repec.org/a/ids/ijicbm/v12y2016i1p92-127.html
   My bibliography  Save this article

Analysing customer responses to migrate strategies in making retailing and CRM effective

Author

Listed:
  • Gaurav Gupta
  • Himanshu Aggarwal

Abstract

As the world is growing more and more competitive, the customer experience is becoming more important to the businesses. There is a need for appropriately aligned customer-centric strategy to maintain synchronisation between customers' expectations and services provided to them. This can be achieved through customer relationship management (CRM). CRM is a comprehensive strategy and a process of acquiring, retaining, and partnering with selective customers to create superior value for the business by using customer knowledge. A business strategy needs to be designed that reduces the cost by increasing customer loyalty and business profitability. The objective of the paper is to point out the relevant factors that may be helpful to the retailers in increasing their profit, sales and building long-term relationships with the customers. The factors have been extracted from the pool of factors that are surveyed from the customers at the shopping mall, marts and supermarket. The findings may help the retailers in developing their strategies during sales, promotions, marketing, business and customer build ups and maintaining log-term relationships with the customers.

Suggested Citation

  • Gaurav Gupta & Himanshu Aggarwal, 2016. "Analysing customer responses to migrate strategies in making retailing and CRM effective," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 12(1), pages 92-127.
  • Handle: RePEc:ids:ijicbm:v:12:y:2016:i:1:p:92-127
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=73395
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. M. Hemalatha & A.G.V. Narayanan & P. Sridevi & G.S.D.S. Jayakumar, 2013. "An empirical study on the influence of clearance sales shopping characteristics on store satisfaction and loyalty," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 6(2), pages 207-226.
    2. Pankaj Chamola & Prakash Tiwari, 2014. "Customer delight and mood states: an empirical analysis in Indian retail context," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 8(4), pages 543-554.
    3. S. Satapathy & S.K. Patel & A. Biswas & P.D. Mishra, 2013. "A methodology to measure the service quality of online shopping of electronic goods in India," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 6(2), pages 227-247.
    4. Coussement, Kristof & Benoit, Dries Frederik & Van den Poel, Dirk, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers 2009/18, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    5. Arpita Khare, 2012. "Impact of consumer decision-making styles on Indian consumers' mall shopping behaviour," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 5(3), pages 259-279.
    6. Daewoo Park & Ravi Chinta & Rashmi Assudani & Mina Lee & Margaret Cunningham, 2013. "Understanding Indian supply chain management practices," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 7(3), pages 348-358.
    7. Seema & Darshan Kumar, 2014. "An analytical approach to supplier selection problem," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 9(2), pages 164-180.
    8. Pramod Kumar Mishra & B. Raja Shekhar, 2013. "Consumer behaviour, customer satisfaction vis-a-vis brand performance: an empirical study of dairy food supply chain in India," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 7(3), pages 399-412.
    9. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
    10. Jacqueline Williams & Justin Paul, 2014. "Dimensions of shopping preferences by women in India and the USA - a cross country study," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 8(4), pages 519-542.
    11. Rema Gopalan & Sreekumar, 2013. "An empirical assessment of retail service quality in India," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 7(2), pages 240-260.
    12. Arpita Khare & Vrijendra Singh & Sunita Arora & Nishu Jain & Ashish Verma, 2010. "Designing competitive strategy using CRM for Indian primary education," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 3(4), pages 466-487.
    Full references (including those not matched with items on IDEAS)

    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. Gaurav Khatwani & Gopal Das, 2016. "Evaluating combination of individual pre-purchase internet information channels using hybrid fuzzy MCDM technique: demographics as moderators," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 12(1), pages 28-49.
    2. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    3. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    4. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    5. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    6. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    7. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    8. Yanhao Wei & Pinar Yildirim & Christophe Van den Bulte & Chrysanthos Dellarocas, 2016. "Credit Scoring with Social Network Data," Marketing Science, INFORMS, vol. 35(2), pages 234-258, March.
    9. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    10. Mitrović, Sandra & Baesens, Bart & Lemahieu, Wilfried & De Weerdt, Jochen, 2018. "On the operational efficiency of different feature types for telco Churn prediction," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1141-1155.
    11. Vaishali Hemant Pardeshi & Vandana Khanna, 2021. "Factors influencing online apparel shopping orientation among women in Mumbai," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 11(1), pages 163-174, December.
    12. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
    13. Mukesh Kumar & Rakesh D. Raut & Mahak Sharma & Vikas Kumar Choubey & Sanjoy Kumar Paul, 2022. "Enablers for resilience and pandemic preparedness in food supply chain," Operations Management Research, Springer, vol. 15(3), pages 1198-1223, December.
    14. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.
    15. Seungwook Kim & Daeyoung Choi & Eunjung Lee & Wonjong Rhee, 2017. "Churn prediction of mobile and online casual games using play log data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    16. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    17. Matthias Bogaert & Michel Ballings & Martijn Hosten & Dirk Van den Poel, 2017. "Identifying Soccer Players on Facebook Through Predictive Analytics," Decision Analysis, INFORMS, vol. 14(4), pages 274-297, December.
    18. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    19. Mittal, Sheetal & Chawla, Deepak & Sondhi, Neena, 2016. "Segmentation of impulse buyers in an emerging market – An exploratory study," Journal of Retailing and Consumer Services, Elsevier, vol. 33(C), pages 53-61.
    20. Sheetal Mittal & Neena Sondhi & Deepak Chawla, 2018. "Process of Impulse Buying: A Qualitative Exploration," Global Business Review, International Management Institute, vol. 19(1), pages 131-146, February.

    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:ids:ijicbm:v:12:y:2016:i:1:p:92-127. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=235 .

    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.