Predictive Analysis of Retail Promotion Strategies in the Context of Consumer Shopping Behavior
Keywords:
Predictive Analysis, Promotional Effectiveness, Shopping Interest, Multiple Linear Regression, Consumer BehaviorAbstract
In this paper, we examine the impact of various promotional strategies on consumer shopping interest, focusing on the Alfamart retail chain in Lhokseumawe City, Indonesia, which saw rapid expansion from five to fifteen stores between 2017 and 2023. Despite this growth, expected sales increases have not been met, raising concerns about the effectiveness of current promotional tactics. Utilizing multiple linear regression analysis, we investigate the influence of three specific strategies, Promo Spesial Mingguan, Serba Gratis, and Tebus Murah on shopping interest across the 15 stores. Findings reveal that Tebus Murah is the most effective strategy in boosting shopping interest, showing the smallest error margin between predictive and actual sales figures. This study provides comprehensive insights into the broader effects of promotional strategies on consumer interest, highlighting the need for Alfamart to focus on optimizing the Discounted Redemption approach to maximize sales. The predictive system developed serves as a strategic tool for identifying effective promotions, forecasting sales, calculating return on investment, and analyzing consumer behavior. Our results underscore the value of predictive analysis in refining promotional strategies, enabling Alfamart to adopt a more targeted and efficient marketing approach to enhance sales performance.
References
[1] Rizaty, M. A. (2023). Jumlah Toko Retail Di Indonesia Sebanyak 3,98 Juta Pada 2022. Data Indonesia.
[2] Mishra, M., Kushwaha, R., & Gupta, N. (2024). Impact Of Sales Promotion On Consumer Buying Behavior In The Apparel Industry. Cogent Business & Management, 11(1), 2310552.
[3] Lin, A. (2024). Walmart Sales Prediction Using Multiple Linear Regression . Highlights in Science, Engineering and Technology, 107, 124-129.
[4] Kotler, P., & Keller, K. L. (2020). Marketing Management. 15th ed. Pearson.
[5] Widayati, C. C., Ali, H., Permana, D., Riyadi, M., Mercu, U., & Jakarta, B. (2019). The Effect Of Visual Merchandising, Sales Promotion And Positive Emotion Of Consumers On Impulse Buying Behavior. Journal of Marketing and Consumer Research, 60, 56-65.
[6] Ian Wooton, & Zhenyu Cui. (2022). The Effect Of Online Advertising On Consumer Buying Interest In Online Selling Applications With Customer Satisfaction As An Intervening Variable (Study From Member Of United Kingdom Medical Doctor Department). Medalion Journal: Medical Research, Nursing, Health And Midwife Participation, 3(3), 82–100. Https://Doi.Org/10.59733/Medalion.V3i3.21
[7] Muhajir, A. (2024). Predictive Analytics in Marketing: Contribution to Marketing Performance. Management Studies and Business Journal (PRODUCTIVITY), 1(3), 447-460.
[8] Kumar, V., & Garg, M. L. (2018). Predictive analytics: a review of trends and techniques. International Journal of Computer Applications, 182(1), 31-37.
[9] Fikry, M., & Inoue, S. (2023). Optimizing Forecasted Activity Notifications With Reinforcement Learning. Sensors, 23(14), 6510.
[10] İnce, M. N., & Taşdemir, Ç. (2024). Forecasting Retail Sales For Furniture And Furnishing Items Through The Employment Of Multiple Linear Regression And Holt–Winters Models. Systems, 12(6), 219.
[11] Almestarihi, R., Ahmad, A. Y. A. B., Frangieh, R., Abu-Alsondos, I., Nser, K., & Ziani, A. (2024). Measuring The ROI Of Paid Advertising Campaigns In Digital Marketing And Its Effect On Business Profitability. Uncertain Supply Chain Management, 12(2), 1275-1284.
[12] Ramachandran, K. K. (2023). Evaluating ROI In Digital Marketing Campaigns: Metrics, Measurement, And Insights. International Journal Of Management (IJM), 14(7).
[13] Behrisch, M., Blumenschein, M., Kim, N. W., Shao, L., El‐Assady, M., Fuchs, J., ... & Keim, D. A. (2018, June). Quality Metrics For Information Visualization. In Computer Graphics Forum (Vol. 37, No. 3, Pp. 625-662).
[14] Sakar, C. O., Polat, S. O., Katircioglu, M., & Kastro, Y. (2019). Real-Time Prediction Of Online Shoppers’ Purchasing Intention Using Multilayer Perceptron And LSTM Recurrent Neural Networks. Neural Computing And Applications, 31(10), 6893-6908.
[15] Maulud, D., & Abdulazeez, A. M. (2020). A Review On Linear Regression Comprehensive In Machine Learning. Journal Of Applied Science And Technology Trends, 1(2), 140-147.
[16] Acar, E. (2020). Air Temperature Estimation For Batman City With Simple And Multi-Linear Regression Models Utilizing Meteorological Parameters. European Journal Of Technique (Ejt), 10(2), 242-251.
[17] Teck Weng, J., & Cyril De Run, E. (2013). Consumers' Personal Values And Sales Promotion Preferences Effect On Behavioural Intention And Purchase Satisfaction For Consumer Product. Asia Pacific Journal Of Marketing And Logistics, 25(1), 70-101.
[18] Lichtenstein, D. R., Ridgway, N. M., & Netemeyer, R. G. (1993). Price Perceptions And Consumer Shopping Behavior: A Field Study. Journal Of Marketing Research, 30(2), 234-245.
[19] Rane, N. L., Achari, A., & Choudhary, S. P. (2023). Enhancing Customer Loyalty Through Quality Of Service: Effective Strategies To Improve Customer Satisfaction, Experience, Relationship, And Engagement. International Research Journal Of Modernization In Engineering Technology And Science, 5(5), 427-452.
[20] Sa'adah, L. (2023). Working Capital Management Analysis To Assess ROI. In Multidiscipline International Conference (Vol. 2, No. 1, Pp. 134-141).
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