  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.
