Predicting Efficiency of Direct Marketing Campaigns for Financial Institutions

  • Sneh GajiwalaEmail author
  • Arjav Mehta
  • Mitchell D’silva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


All marketing campaigns are dependent on the data that their clients provide. These datasets include everything from their name, number, their salary, the loans they’ve already taken, the money they have in their account etc. These datasets are huge and it is impossible for a human to analyze the patterns in the client deposits and whether the campaign will be a success or not. This paper introduces prediction of the success rates of the campaigns with the help of various machine learning predictive algorithms: Random forest, KNN and KNN using tensor flow framework. In the recent years, the success of general campaigns led by financial institutions have been declining and to step up their game and increase campaign effectiveness they need to target the clients very specifically. The predictive results obtained, with the highest accuracy, help in increasing the target audience and ensure that it will be a success for the financial institutions in picking out clients who will subscribe to the different marketing schemes. This paper uses multiple accuracy parameters to predict the potential success a direct marketing campaign will have on specific clients.


Direct marketing KNN Random Forests TensorFlow Sensitivity Specificity 


  1. 1.
  2. 2.
    Investopedia, Direct Marketing. Accessed 04 Oct 2018
  3. 3.
    Elsalamony, H.A.: Bank direct marketing analysis of data mining techniques. Int. J. Comput. Appl. 85(7), (2014). ISSN 0975–8887.
  4. 4.
    Syarif, S., Anwar, Dewiani: Trending topic prediction by optimizing K-nearest neighbor algorithm. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT) (2017).
  5. 5.
    Imandoust, S.B., Bolandraftar, M.: Application of k-nearest neighbor (KNN) approach for predicting economic events: theoretical background. Int. J. Eng. Res. Appl. 3(5), 605–610 (2013). Scholar
  6. 6.
    Alkhatib, K., Najadat, H., Hmeidi, I., Shatnawi, M.K.A.: Stock price prediction using K-nearest neighbor (KNN) algorithm. Int. J. Bus. Humanit. Technol. 3(3), 32–44 (2013). Scholar
  7. 7.
    Analytics Vidhya, K - Nearest Neighbour. Accessed 04 Oct 2018
  8. 8.
  9. 9.
    Medium, Random Forest Algorithm. Accessed 05 Oct 2018
  10. 10.
    Towards Data Science, Random Forest Algorithm. Accessed 05 Oct 2018
  11. 11.
    Tensorflow, TensorFlow Guide. Accessed 07 Oct 2018
  12. 12.
    TensorFlow, Keras. Accessed 07 Oct 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sneh Gajiwala
    • 1
    Email author
  • Arjav Mehta
    • 1
  • Mitchell D’silva
    • 1
  1. 1.Dwarkadas J Sanghvi College of EngineeringMumbaiIndia

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