Enhanced sales forecasting model using textual search data: Fusing dynamics with big data

Abhishek Borah and Oliver Rutz

International Journal of Research in Marketing, 2024, vol. 41, issue 4, 632-647

Abstract: Forecasting sales is an essential marketing function, and, for most businesses, sales are driven by own and competitive activities. Most firms use their own marketing efforts or a selection of their competitor’s marketing efforts for forecasting sales. Due to data availability limitations, data on the full set of competitors are rarely used when forecasting sales. The emergence of online search data provides access to a novel data source on own as well as never-before observed competitive activities. We propose a novel regularized dynamic forecasting model utilizing all available competitive search data in a market vs. constructing ad-hoc and potentially subjective smaller competitive sets. Our model addresses the inherent statistical issue that arises when including a large number of competitive effects and parsimoniously utilizes all competitive data. We demonstrate our model using data from the US automobile industry over a twelve-year period and forecast car-model sales for 14 exemplary car-models utilizing multiple search measures for all 374 potential competitive car-models. We show that our model fits and forecasts sales better than models not leveraging the full competitive search data, e.g., using subjective sets of relevant competitors or narrowly defined category competitors. We also find that market research done via novel large-language models (also called LLMs) to obtain a narrower set of competitors does not outperform our proposed model that includes the full set of competitors.

Keywords: Online search; Competition; Bayesian Elastic Net; Sales; Forecasting (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167811624000454
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijrema:v:41:y:2024:i:4:p:632-647

DOI: 10.1016/j.ijresmar.2024.05.007

Access Statistics for this article

International Journal of Research in Marketing is currently edited by Roland Rust

More articles in International Journal of Research in Marketing from Elsevier
Bibliographic data for series maintained by Catherine Liu ().