Academic Journal
Forecasting Fashion Sales With Social Media Signals: Insights From Amazon, Twitter, and Google Trends.
| Τίτλος: | Forecasting Fashion Sales With Social Media Signals: Insights From Amazon, Twitter, and Google Trends. |
|---|---|
| Συγγραφείς: | Rudna, Olena1 (AUTHOR) olena.rudna@njit.edu, Rudniy, Alex2 (AUTHOR), Park, Arim3 (AUTHOR) |
| Πηγή: | Journal of Forecasting. Aug2026, p1. 18p. 4 Illustrations. |
| Θεματικοί όροι: | *Sales forecasting, *Forecasting, *Data analytics, Demand forecasting, Trend analysis |
| Εταιρία/Οντότητα: | Amazon.com Inc. 884745530 AMZN |
| Reviews & Products: | Twitter (Web resource) |
| Περίληψη: | ABSTRACT This study validates that fashion features identified from social media are actionable for demand planning. Building on our prior work establishing the Twitter Trend Tool (3Ts) for discovering trending apparel attributes without prior knowledge, we demonstrate that incorporating Twitter‐derived features into forecasting models improves out‐of‐sample accuracy. Unlike Google Trends, which requires knowing what features to search for, our approach extracts new, rising, and existing fashion trends in real time. We integrate weekly Amazon Sales Rank with weekly counts of Twitter posts and Google Trends indices for 77 matched fashion‐attribute time series across 21 popularity buckets. Using a four‐pass experimental design that applies identical model families (VECM, VAR, and NNETAR) across univariate and multivariate settings, we isolate the contribution of social signals from modeling methodology. Across 4620 forecasts, the combined Twitter‐plus‐Google‐Trends model achieves rank‐1 accuracy for all 77 time series, with VECM attaining a mean RMSE of 3.47 versus 91,034 for the univariate baseline (97.7% improvement). In head‐to‐head comparison, neither Twitter nor Google Trends dominates alone (approximately 51%–57% win rate); their complementary information content drives the dramatic forecast improvement when combined. We discuss managerial implications for planning cycles, feature discovery, and trend‐responsive assortment. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Forecasting is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
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| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Forecasting Fashion Sales With Social Media Signals: Insights From Amazon, Twitter, and Google Trends. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rudna%2C+Olena%22">Rudna, Olena</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> olena.rudna@njit.edu</i><br /><searchLink fieldCode="AR" term="%22Rudniy%2C+Alex%22">Rudniy, Alex</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Arim%22">Park, Arim</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Forecasting%22">Journal of Forecasting</searchLink>. Aug2026, p1. 18p. 4 Illustrations. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Sales+forecasting%22">Sales forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Demand+forecasting%22">Demand forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Trend+analysis%22">Trend analysis</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="DE" term="%22Amazon%2Ecom+Inc%2E%22">Amazon.com Inc.</searchLink> <searchLink fieldCode="DN" term="%22884745530%22">884745530</searchLink> <searchLink fieldCode="TK" term="%22AMZN%22">AMZN</searchLink> – Name: SubjectProduct Label: Reviews & Products Group: Su Data: <searchLink fieldCode="PS" term="%22Twitter+%28Web+resource%29%22">Twitter (Web resource)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: ABSTRACT This study validates that fashion features identified from social media are actionable for demand planning. Building on our prior work establishing the Twitter Trend Tool (3Ts) for discovering trending apparel attributes without prior knowledge, we demonstrate that incorporating Twitter‐derived features into forecasting models improves out‐of‐sample accuracy. Unlike Google Trends, which requires knowing what features to search for, our approach extracts new, rising, and existing fashion trends in real time. We integrate weekly Amazon Sales Rank with weekly counts of Twitter posts and Google Trends indices for 77 matched fashion‐attribute time series across 21 popularity buckets. Using a four‐pass experimental design that applies identical model families (VECM, VAR, and NNETAR) across univariate and multivariate settings, we isolate the contribution of social signals from modeling methodology. Across 4620 forecasts, the combined Twitter‐plus‐Google‐Trends model achieves rank‐1 accuracy for all 77 time series, with VECM attaining a mean RMSE of 3.47 versus 91,034 for the univariate baseline (97.7% improvement). In head‐to‐head comparison, neither Twitter nor Google Trends dominates alone (approximately 51%–57% win rate); their complementary information content drives the dramatic forecast improvement when combined. We discuss managerial implications for planning cycles, feature discovery, and trend‐responsive assortment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Forecasting is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/for.70204 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Sales forecasting Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Demand forecasting Type: general – SubjectFull: Trend analysis Type: general – SubjectFull: Amazon.com Inc. Type: general – SubjectFull: Twitter (Web resource) Type: general Titles: – TitleFull: Forecasting Fashion Sales With Social Media Signals: Insights From Amazon, Twitter, and Google Trends. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rudna, Olena – PersonEntity: Name: NameFull: Rudniy, Alex – PersonEntity: Name: NameFull: Park, Arim IsPartOfRelationships: – BibEntity: Dates: – D: 28 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02776693 Titles: – TitleFull: Journal of Forecasting Type: main |
| ResultId | 1 |