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Information Design in Affiliate Marketing

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Abstract

The recent massive proliferation of affiliate marketing suggests a new e-commerce paradigm which involves sellers, affiliates and the platforms that connect them. In particular, the fact that prospective buyers may become acquainted with the promotion through more than one affiliate to whom they are connected calls for new mechanisms for compensating affiliates for their promotional efforts. In this paper, we study the problem of a platform that needs to decide on the commission to be awarded to affiliates for promoting a given product or service. Our equilibrium-based analysis, which applies to the case where affiliates are a priori homogeneous and self-interested, enables showing that a minor change in the way the platform discloses information to the affiliates results in a tremendous (positive) effect on the platform’s expected profit. In particular, we show that with the revised mechanism the platform can overcome the multi-equilibria problem that arises in the traditional mechanism and obtain a profit which is at least as high as the maximum profit in any of the equilibria that hold in the latter.

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Notes

  1. E.g., according to a Business Insider Report from 2018, affiliate marketing is projected to generate $8.2 billion revenue in the US by 2022 [52].

  2. Alternatively, we can assume that the number of potential followers of each partner is a priori probabilistic. This would require some technical changes in the analysis, primarily adding expectation calculations in the equations, however will not change the claims and proofs.

  3. All partners are characterized with the same promotion cost as this is usually the (quite standard) cost of time it takes for uploading a post or the reputation loss associated with promoting the product in the generated content.

  4. Notice that by setting \(p_B=1\) the model changes into (and the analysis and all proofs become applicable to) a pay-per-lead scheme.

  5. While we rely on the assumption of homogeneous partners where a follower is indifferent between the partner whose affiliate link she clicks, an alternate scenario is however possible where the follower chooses from the partner that is the most reputed/reliable according to the follower. In such cases the equilibrium computation becomes combinatorial due to the assignment of different probability of purchase for each follower and does not add much in terms of insights.

  6. Even the partners need to make promotion decisions on a daily basis driven by the above mentioned considerations of time spent, reputation loss and commissions to be earned.

  7. For example, it is easy to know how many readers a blog post has reached [42] or to predict exposure of future posts.

  8. For example, Lobel et al use this kind of equilibrium in their referral-programs based model [35].

  9. An i-based promoting partners pure equilibrium necessarily exists for any \(i>0\), as the increase in Expose(i) due to an increase in i is a decreasing function. Fig. 3 which depicts Expose(i) as a function of the number of promoting partners, i, visualizes this assertion.

  10. The calculation for the M value that maximizes the platform’s expected profit with mixed strategies is more complex, yet as we prove later on it is unnecessary as it is dominated by a pure-strategy equilibrium.

  11. An example where M is not fully within the control of the platform is when the platform offers a fixed M for all products or services listed on its website.

  12. Meaning that we do not even need to provide information about how many others have received an affiliate link. Instead we only provide information about how many times the listing was uniquely viewed.

  13. Since Expose(i) increases at a decreasing rate in i, \(Expose^{marginal}(i)\) decreases in i.

  14. To be completely accurate, any other commission function according to which a subset of j arriving partners, where j is the integer part of the solution \(j'\) to \(\frac{c}{Expose^{marginal}(j')p_B}=G\), are being offered \(\frac{c}{Expose^{marginal}(l)p_B}\) (where l is their order of arrival within the sequence) and the remaining partners being offered zero will result in the same expected profit in its SPNE.

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Acknowledgements

Preliminary results of this research appear in Proceedings of the First International Conference on Distributed Artificial Intelligence (DAI'19). This research has been partly supported by the ISRAEL SCIENCE FOUNDATION (Grant Nos. 1162/17 and 1958/20) and the EU project TAILOR under Grant 992215.

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  1. Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel

    Sharadhi Alape Suryanarayana, David Sarne & Sarit Kraus

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Suryanarayana, S.A., Sarne, D. & Kraus, S. Information Design in Affiliate Marketing. Auton Agent Multi-Agent Syst 35, 23 (2021). https://doi.org/10.1007/s10458-021-09509-7

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