Mohamed Elshazli A. Zidan, Anouar Ben Mabrouk, Nidhal Ben Abdallah, Tawfeeq M. Alanazi
arXiv 20 Mar 2024 · Finance — Mathematical Finance · 1 citations (OpenAlex)
arXiv:2403.13361 · PDF · DOI · OpenAlex · Extracted main text
Marketing is the way we ensure our sales are the best in the market, our prices the most accessible, and our clients satisfied, thus ensuring our brand has the widest distribution. This requires sophisticated and advanced understanding of the whole related network. Indeed, marketing data may exist in different forms such as qualitative and quantitative data. However, in the literature, it is easily noted that large bibliographies may be collected about qualitative studies, while only a few studies adopt a quantitative point of view. This is a major drawback that results in marketing science still focusing on design, although the market is strongly dependent on quantities such as money and time. Indeed, marketing data may form time series such as brand sales in specified periods, brand-related prices over specified periods, market shares, etc. The purpose of the present work is to investigate some marketing models based on time series for various brands. This paper aims to combine the dynamic mode decomposition and wavelet decomposition to study marketing series due to both prices, and volume sales in order to explore the effect of the time scale on the persistence of brand sales in the market and on the forecasting of such persistence, according to the characteristics of the brand and the related market competition or competitors. Our study is based on a sample of Saudi brands during the period 22 November 2017 to 30 December 2021.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Michis, A.A. Regression Analysis of Marketing Time Series: A Wavelet… (2009) 45 | 0.644 | 4 | 1 | 100% |
| 2 | Michis, A.A. Wavelet Analysis of Marketing Time Series. Berkeley Ele… (2009) | 0.644 | 4 | 1 | 100% |
| 3 | Arfaoui S, Ben Mabrouk A and Cattani C (2021) Wavelet analysis Basic concepts and applications, CRC Taylor-Francis, Chapmann & Hall, Boca Raton, 1st Ed., April 21, 2021 | 0.644 | 2 | 2 | 100% |
| 4 | Arfaoui S, Ben Mabrouk A and Cattani C (2021) Fractal analysis Basic concepts and applications, World Scientific, 1st Ed., December 2021 | 0.644 | 2 | 2 | 100% |
| 5 | Michis, A.A.; Sapatinas, T. Wavelet Instruments for Efficiency Gains… (2007) 4 | 0.511 | 2 | 1 | 100% |
| 6 | Armstrong, G.; Adam, S.; Denize, S.; Kotler, P. Principles of Market… (2014) | 0.405 | 1 | 1 | 100% |
| 7 | Bronnenberg, B.J.; Mela, C.F.; Boulding, W. The Periodicity of Prici… (2006) 477–493 | 0.405 | 1 | 1 | 100% |
| 8 | Dekimpe, M.G.; Hanssens, D.M. Time-series Models in Marketing: Past,… (2000) 183–193 | 0.405 | 1 | 1 | 100% |
| 9 | Dekimpe, M.G.; Hanssens, D.M. Empirical Generalizations about Market… (1995) G109–G121 | 0.405 | 1 | 1 | 100% |
| 10 | Deleersnyder, B.; Dekimpe, M.G.; Sarvary, M.; Parker, P.M. Weatherin… (2004) 347–383 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.