Predicting and Forecasting of Sales in Business Software after Adverting and Study of Effectiveness in India

Abstract

The usage of the net and social media have modified client behavior and the ways wherein groups conduct their business. Social and virtual advertising offers significant opportunities to organizations via decrease charges, improved emblem recognition and increased income. But, massive demanding situations exist from negative electronic word-of-mouth as well as intrusive and stressful online brand presence. This newsletter brings collectively the collective insight from several main specialists on issues regarding virtual and social media advertising. The experts’ views provide a detailed narrative on key elements of this critical subject matter in addition to perspectives on more precise issues such as synthetic intelligence, augmented fact marketing, virtual content control, cell advertising and advertising, b2b advertising and marketing, electronic word of mouth and moral troubles therein. This studies gives a vast and well timed contribution to each researchers and practitioners within the shape of challenges and opportunities wherein we highlight the constraints in the modern-day research, outline the research gaps and develop the questions and propositions that may assist boost expertise in the area of digital and social advertising and marketing.

Country : India

1 Mr. Nagraj Kishanrao Tondchore

  1. Director, Trav Ticket Services Pvt Ltd. Pune, Maharashtra, India

IRJIET, Volume 6, Issue 6, June 2022 pp. 230-232

doi.org/10.47001/IRJIET/2022.606035

References

  1. Patrick Bajari, Denis Nekipelov, Stephen P Ryan, and Miaoyu Yang. Machine learning methods for demand estimation. American Economic Review, 105(5):481–85, 2015.
  2. Kris Johnson Ferreira, Bin Hong Alex Lee, and David Simchi-Levi. Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1):69–88, 2016.
  3. Ankur Jain, Manghat Nitish Menon, and Saurabh Chandra. Sales forecasting for retail chains, 2015.
  4. Grigorios Tsoumakas. A survey of machine learning techniques for food sales prediction. Artificial Intelligence Review, 52(1):441–447, 2019.
  5. Xiaogang Su, Xin Yan, and Chih-Ling Tsai. Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3):275–294, 2012.
  6. Toby J Mitchell and John J Beauchamp. Bayesian variable selection in linear regression. Journal of the American statistical association, 83(404):1023–1032, 1988.
  7. Zheng Li, Xianfeng Ma, and Hongliang Xin. Feature engineering of machine learning chemisorptions models for catalyst design. Catalysis today, 280:232–238, 2017.
  8. Xinchuan Zeng and Tony R Martinez. Distribution-balanced stratified cross validation for accuracy estimation. Journal of Experimental & Theoretical Artificial Intelligence, 12(1):1–12, 2000.
  9. Konstantinos Sechidis, Grigorios Tsoumakas, and Ioannis Vlahavas. On the stratification of multi-label data. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 145–158. Springer, 2011.
  10. Chris Rygielski, Jyun-Cheng Wang, and David C Yen. Data mining techniques for customer relationship management. Technology in society, 24(4):483–502, 2002.
  11. Krzysztof J Cios, Witold Pedrycz, Roman W Swiniarski, and Lukasz Andrzej Kurgan. Data mining: a knowledge discovery approach. Springer Science & Business Media, 2007.