Leveraging Information Networks for Marketing Brand Alliance Discovery: A Data-Driven Framework for Actionable Insights

Marketing brand alliances are consumer-facing collaborations in which brands are jointly presented through shared offerings or campaigns. Yet, despite their widespread use in practice, no existing framework supports their market-wide, ex ante discovery with execution guidance. We address this gap by formalizing brand alliance opportunity discovery as a new computational design problem characterized by two desiderata that prior work has not jointly addressed: signal fusion and actionability. We propose BANE (Brand Alliance Network Exploitation), a sociotechnical framework that leverages information networks derived from digital user traces to satisfy these desiderata through four phases: (1) predicting alliance opportunities by integrating offline brand attributes, online consumer co-mention signals, and network embeddings; (2) validating predictions through consumer and brand manager surveys; (3) qualifying predicted positive, non-materialized alliance opportunities for actionability across quadrants; and (4) generating marketing-mix execution guidance. Temporal holdout testing demonstrates that BANE predicts future alliances from pre-announcement signals, ablation analysis confirms the incremental value of each signal layer, and stakeholder surveys show that BANE-identified opportunities are perceived as significantly more promising than controls. Our work contributes three salient design insights to the IS literature: multi-layer signal fusion is necessary for identifying truly promising brand alliance opportunities; consumer co-mention networks systematically align with theory-grounded alliance covariates, providing a distinct informational input for alliance prediction; and coupling prediction with qualification and prescription enhances managerial actionability. These insights enable a scalable, data-driven alternative to survey-based alliance search while generating predictive knowledge that can motivate future causal research on the mechanisms underlying brand alliance formation.

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Publication Details

Journal
Information Systems Research
Published
2026-06-29
DOI
https://doi.org/10.1287/isre.2025.1792
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
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article

Leveraging Information Networks for Marketing Brand Alliance Discovery: A Data-Driven Framework for Actionable Insights

Pankhuri Malhotra, Yaxin Cui, Keran Zhao, Daniel Ringel
Information Systems Research
Digital Marketing and Social Media
article

Leveraging Information Networks for Marketing Brand Alliance Discovery: A Data-Driven Framework for Actionable Insights

Pankhuri Malhotra, Yaxin Cui, Keran Zhao, Daniel Ringel
article en

Abstract

Marketing brand alliances are consumer-facing collaborations in which brands are jointly presented through shared offerings or campaigns. Yet, despite their widespread use in practice, no existing framework supports their market-wide, ex ante discovery with execution guidance. We address this gap by formalizing brand alliance opportunity discovery as a new computational design problem characterized by two desiderata that prior work has not jointly addressed: signal fusion and actionability. We propose BANE (Brand Alliance Network Exploitation), a sociotechnical framework that leverages information networks derived from digital user traces to satisfy these desiderata through four phases: (1) predicting alliance opportunities by integrating offline brand attributes, online consumer co-mention signals, and network embeddings; (2) validating predictions through consumer and brand manager surveys; (3) qualifying predicted positive, non-materialized alliance opportunities for actionability across quadrants; and (4) generating marketing-mix execution guidance. Temporal holdout testing demonstrates that BANE predicts future alliances from pre-announcement signals, ablation analysis confirms the incremental value of each signal layer, and stakeholder surveys show that BANE-identified opportunities are perceived as significantly more promising than controls. Our work contributes three salient design insights to the IS literature: multi-layer signal fusion is necessary for identifying truly promising brand alliance opportunities; consumer co-mention networks systematically align with theory-grounded alliance covariates, providing a distinct informational input for alliance prediction; and coupling prediction with qualification and prescription enhances managerial actionability. These insights enable a scalable, data-driven alternative to survey-based alliance search while generating predictive knowledge that can motivate future causal research on the mechanisms underlying brand alliance formation.

Information Systems Research
Northwestern University (US), University of North Carolina at Chapel Hill (US), Pennsylvania State University (US), University of Oklahoma (US)
Partnerships for the goals
Openalex Percentile: Top 4%
Digital Marketing and Social Media
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