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Abstract
Brands usually invest in a portfolio of digital ad products for brand consideration and conversion, and their performance is commonly evaluated with ad-attributed metrics. However, these metrics limit the measurement of advertising effectiveness within a short time window, typically of two weeks. Therefore, they could underestimate the total effect if some ad products' efficacy lasts beyond the measurement period. In particular, this could understate the impact from ad products aimed at awareness and consideration. In addition, this bias could manifest in product categories where shoppers' involvement is high because they are making deliberate purchase decisions. To solve these problems, the Vector Autoregressive Moving Average with Exogenous variables (VARMAX) model is applied, which allows flexibility in the length of the advertising measurement window, and thus can empirically quantify how long the effect of each ad lasts without `a priori` restrictions. For 15 US brands across three verticals (Hardlines, Softlines and Consumables) on Amazon, it was found that within the two-week attribution window, upper/middle-funnel ad products only materialise 30–50 per cent of the total effects, compared to lower-funnel at 60–90 per cent. Based on these results, it is recommended that advertisers and publishers lengthen the attribution window, and especially track their upper and middle-funnel ad products for at least a month to capture their longer-term effects.
The full article is available to subscribers to the journal.
Author's Biography
Vivian Qin received her doctoral degree in marketing from Fuqua School of Business, Duke University. She joined Amazon in April 2021. Currently, she is the team lead for data science within analytics and insights of Amazon Ads. She also advises the global research programme, and is the adjunct professor of marketing analytics at University of Maryland – College Park.
Citation
Qin, Vivian (2023, June 1). Lengthen your attribution window: Which digital ads have most long-term impact?. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 9, Issue 1. https://doi.org/10.69554/WPUS1821.Publications LLP