Disciplinary Grounding

Traditionally, affiliate marketing has been confined to e-commerce, where online publishers drive traffic to retail websites in exchange for commissions. BuyBlvd is pioneering a new model that extends affiliate marketing into physical retail, allowing brands to track how online consumer engagement translates into in-store purchases. By leveraging third-party user data from external platforms along with Point-of-Sale information from their stores, BuyBlvd can attribute in-store sales to digital interactions, helping local retailers optimize their marketing efforts and gain deeper insights into consumer behavior. This approach not only enhances retail attribution accuracy but also empowers small businesses with the kind of data-driven strategies previously available only to large-scale retailers. To support this business model, this project will dive deeper into what is needed on the digital side, and develop a digital media strategy to position the company along with major players in the fashion technology space.

Theoretical Foundations and Relevant Readings

Affiliate marketing has traditionally been an e-commerce strategy, where publishers earn commissions for directing online traffic to retailers (Duffy, 2005). However, the expansion of online-to-offline (O2O) attribution has enabled brands to track how digital interactions influence in-store purchases. BuyBlvd’s approach aligns with this emerging model, using third-party consumer data to link digital engagement with local store visits.

One of the primary challenges in this space is accurate sales attribution. Last-click attribution—where credit for a sale goes to the last digital touchpoint—is common in affiliate marketing, but it fails to account for the complexity of omnichannel consumer journeys (Li & Kannan, 2014). Instead, multi-touch attribution (MTA) models, which consider multiple consumer interactions across platforms, offer a more comprehensive approach to tracking conversions (Berman, 2018).

Using affiliates helps mitigate the attribution challenge by distributing credit more equitably across multiple digital touchpoints. Unlike traditional last-click attribution, which often oversimplifies the consumer journey, affiliate marketing operates on predefined commission structures that incentivize partners based on measurable contributions to in-store sales (Duffy, 2005). By leveraging an affiliate network, BuyBlvd can ensure that marketing efforts are rewarded not just for direct conversions but also for the role they play in influencing purchasing behavior over time.

Additionally, affiliates generate diverse consumer touchpoints, from content-driven recommendations to localized promotions, creating a richer data set for multi-touch attribution (MTA) modeling. Instead of relying solely on digital ad impressions or online clicks, BuyBlvd can use its affiliates’ tracked engagements—such as blog referrals, email campaigns, or social media interactions—to understand the incremental impact of each marketing channel (Berman, 2018). This allows for a more accurate distribution of credit and helps local retailers optimize their outreach strategies. By integrating third-party consumer data, BuyBlvd can refine its attribution models to better capture O2O conversions, ensuring that local businesses compensate marketing partners based on actual in-store impact rather than an oversimplified digital metric. This data-driven approach enhances transparency, encourages long-term partnerships, and ultimately strengthens the relationship between digital engagement and physical retail success.

Omnichannel Retail and Consumer Behavior

Consumer purchasing behavior has become increasingly fragmented across digital and physical channels, creating both challenges and opportunities for modern retailers (Brynjolfsson, Hu, & Rahman, 2013). These researchers reference, “the blurring lines between physical and digital worlds,” (p.1) and this shift requires businesses to rethink how they engage consumers. Omnichannel retailing, which integrates online, mobile, and in-store experiences, has emerged as the dominant model for modern commerce (Verhoef, Kannan, & Inman, 2015). Studies show that customers who interact with multiple channels—including social media, email, apps, and brick-and-mortar locations—tend to spend more and display stronger brand loyalty (Lemon & Verhoef, 2016).

BuyBlvd’s affiliate model is specifically designed to leverage these fragmented, yet connected consumer journeys. By tracking interactions across digital platforms and mapping them to in-store outcomes, BuyBlvd helps local retailers understand where and how customers are engaging before they make a purchase. This aligns with the core idea behind omnichannel marketing: not just meeting the customer where they are, but understanding the full sequence of touchpoints that influence a purchase decision.

This strategy is deeply rooted in the Uses and Gratifications Theory (Katz, Blumler, & Gurevitch, 1973), which theorizes that media consumers are not passive viewers but active participants who seek out content that satisfies specific psychological, emotional, and social needs. This framework provides a strong foundation for understanding how and why consumers engage with platforms like BuyBlvd. For instance, users may turn to local affiliates for social identity reinforcement or aspirational content, fulfilling needs for personal integration or social interaction. Others may use the app to efficiently locate in-stock products, meeting utilitarian needs such as information-seeking or problem-solving—core gratifications outlined by the theory (Rubin, 2002).

BuyBlvd leverages these motivations by designing content strategies and platform features that map directly onto these user desires. Influencer and affiliate content speaks to the need for social connection and inspiration, while real-time inventory access and geo-located search tools fulfill a desire for immediacy, convenience, and control over the shopping process. These capabilities not only help satisfy user expectations but also drive behavior that leads to in-store visits and purchases. The theory suggests that the more directly a platform meets users’ needs, the more likely they are to engage repeatedly and develop platform loyalty—something BuyBlvd seeks to cultivate among both shoppers and affiliates (Sundar & Limperos, 2013).

Moreover, as Lemon and Verhoef (2016) explain, successful digital platforms must not only understand what the customer experiences but also how they interpret and integrate that experience over time and across touchpoints. BuyBlvd embodies this by offering a seamless blend of online content discovery and offline action—giving users a sense of continuity and satisfaction that reinforces the brand relationship. This integrative strategy is especially powerful in an age when consumers expect brands to operate across multiple channels with consistency and coherence.

By incorporating real-time consumer data—such as location, platform engagement, and inventory status—BuyBlvd delivers actionable insights that improve personalization, inventory management, and promotional timing (Wedel & Kannan, 2016). Kannan and Li (2017) further support this approach, noting that "digital technologies have made it possible to tailor experiences to individual customers in real time," a practice that is becoming essential in a fragmented consumer landscape.

The BuyBlvd case acts as a practical application of both academic theory and emerging retail trends. It allows small businesses to benefit from marketing strategies once reserved for e-commerce giants, while respecting privacy and ensuring transparent data use. This positions BuyBlvd as not just a platform, but a strategic partner in reshaping local commerce.