Review of Professional Management- A Journal of New Delhi Institute of Management

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A Predictive Analytical Study on Factors Enhancing Customer Acquisition and Retention


Affiliations
1 Scholar, New Delhi Institute of Management, India
2 Associate Professor, Department of Business Analytics, New Delhi Institute of Management, India
3 Assistant Professor, Department of Business Analytics, New Delhi Institute of Management, India
 

CRM (Customer Relationship Management) Systems have long been used for strengthening relationships with customers thereby ensuring retention and enhancing business. Data stored in the CRM software can be analyzed to provide deep insights into the customer behavior thus influencing future products and services. Predictive Analytics are a branch of Business Analytics that helps in analyzing the current data, with the help of statistical tools, data mining algorithms, modelling tools, AI or machine learning, to make effective predictions for the future. This paper studies the impact of predictive analytics applied onto the CRM data of the sample Organization (name concealed owing to secrecy issues), which is among the front runners in the Instrumentation Industry in India and has been providing best quality Instruments and allied services through leading edge global technology. This paper examines the significant factors which help in winning a deal by using logistic regression in the reference Organization. Data are obtained from the Customer Relationship Management software provided by the company. The results presented in this paper confirm that the CRM data can be used to predict the probability of winning a deal. It also helps to find factors which are impacting 'Win' or 'Loss' of the opportunity/deal so that businesses can take precautionary measures to avoid potential loss of opportunity. Such analysis is helpful in the creation of new sales tactics, improvement of winning proportions and thereby enhancing sales.

Keywords

CRM, Predictive Analytics, AI, Logistic Regression.
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Abstract Views: 310

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  • A Predictive Analytical Study on Factors Enhancing Customer Acquisition and Retention

Abstract Views: 310  |  PDF Views: 230

Authors

Sahil Dudeja
Scholar, New Delhi Institute of Management, India
Rinku Dixit
Associate Professor, Department of Business Analytics, New Delhi Institute of Management, India
Shailee Choudhary
Assistant Professor, Department of Business Analytics, New Delhi Institute of Management, India

Abstract


CRM (Customer Relationship Management) Systems have long been used for strengthening relationships with customers thereby ensuring retention and enhancing business. Data stored in the CRM software can be analyzed to provide deep insights into the customer behavior thus influencing future products and services. Predictive Analytics are a branch of Business Analytics that helps in analyzing the current data, with the help of statistical tools, data mining algorithms, modelling tools, AI or machine learning, to make effective predictions for the future. This paper studies the impact of predictive analytics applied onto the CRM data of the sample Organization (name concealed owing to secrecy issues), which is among the front runners in the Instrumentation Industry in India and has been providing best quality Instruments and allied services through leading edge global technology. This paper examines the significant factors which help in winning a deal by using logistic regression in the reference Organization. Data are obtained from the Customer Relationship Management software provided by the company. The results presented in this paper confirm that the CRM data can be used to predict the probability of winning a deal. It also helps to find factors which are impacting 'Win' or 'Loss' of the opportunity/deal so that businesses can take precautionary measures to avoid potential loss of opportunity. Such analysis is helpful in the creation of new sales tactics, improvement of winning proportions and thereby enhancing sales.

Keywords


CRM, Predictive Analytics, AI, Logistic Regression.

References





DOI: https://doi.org/10.20968/rpm%2F2019%2Fv17%2Fi1%2F145647