{"id":21164,"date":"2026-10-27T17:18:04","date_gmt":"2026-10-27T21:18:04","guid":{"rendered":"https:\/\/advertisingweek.com\/aw360\/?p=21164"},"modified":"2026-10-27T17:18:05","modified_gmt":"2026-10-27T21:18:05","slug":"an-outfit-a-netflix-connection-and-six-weeks-later","status":"publish","type":"post","link":"https:\/\/advertisingweek.com\/aw360\/news\/an-outfit-a-netflix-connection-and-six-weeks-later\/21164\/","title":{"rendered":"An Outfit, a Netflix Connection, and Six Weeks Later"},"content":{"rendered":"\n

By Becky Johnson<\/a>, Advertising Week Writer & Podcast Host<\/em><\/p>\n\n\n\n

Buying an outfit from a television show should not require knowing a senior executive at Netflix. Yet that was the route one shopper took, according to Bryan Quinn<\/a>, CEO and Co-Founder of Shopsense AI<\/a>, who joined Modern Marketing and Measurement to discuss the work still needed to connect media, shopping, and measurable business results.<\/p>\n\n\n\n

Bryan met the shopper, a former colleague, at CES in 2024. She was wearing an outfit she had seen on Emily in Paris, and getting it had taken some effort. A connection at Netflix led to the show\u2019s head of wardrobe, who supplied a retailer link. Six weeks later, she had the outfit.<\/p>\n\n\n\n

The interest was already there, as was the willingness to buy. What was missing was a reasonable way to get from one to the other.<\/p>\n\n\n\n

Understanding the Cart<\/h2>\n\n\n\n

Shopsense builds the underlying technology for shoppable experiences across television, articles, and other digital environments. Its work includes interpreting content, identifying relevant products, and improving the product data those experiences depend on.<\/p>\n\n\n\n

Bryan draws a useful distinction between general-purpose AI and the company\u2019s approach: \u201cGeneral purpose models are really built to understand the world around us, where we built our models to understand the cart.\u201d The focus is on identifying shopping intent within content and connecting it with products that make sense in that moment.<\/p>\n\n\n\n

That requires keeping up with changing preferences. Bryan describes himself as the least fashionable person on his team, but as a father of two daughters, he gets regular reminders that what is popular today may not be popular tomorrow. Our discussion moves through Birkenstocks, Stanley cups, and an urgent household switch from Doc Martens to Ugg boots, all examples of how quickly demand can move.<\/p>\n\n\n\n

Bryan says Shopsense trains its model daily with new signals and works with more than 1,000 retailers across categories. In testing a new version of its model earlier this year, he says, the company saw consumer click-through rates rise by almost 25%. That is an engagement result, and it makes his point about the importance of keeping recommendations relevant.<\/p>\n\n\n\n

Relevance Is Part of the Experience<\/h2>\n\n\n\n

A recommendation has to earn its place in the shopping experience. Bryan describes testing what happens when products are selected for their relevance compared with those favored for better commercial terms, and says consumer engagement drops when the product fit suffers.<\/p>\n\n\n\n

That finding informs how he thinks about advertising more broadly. An ad can help someone discover something they want, provided it fits what they are doing at that moment. As Bryan puts it, \u201cPeople don\u2019t hate ads, they hate interruption.\u201d<\/p>\n\n\n\n

Context provides part of that understanding, and knowledge of the customer can make the recommendation more useful. Bryan sees the strongest opportunity where those two come together, but says the teams responsible for them often work separately. Search, social, and programmatic may each have useful information without a shared way to apply it.<\/p>\n\n\n\n

Measure the Moment Appropriately<\/h2>\n\n\n\n

That understanding of the customer\u2019s situation also needs to carry into measurement. A person searching a retailer\u2019s app for teeth whitening products has expressed an intent to shop. A television viewer may be discovering a product, considering it, or simply enjoying the show.<\/p>\n\n\n\n

Bryan cautions against judging shoppable TV by the same immediate-response benchmarks used for on-site retail media. Relatively few viewers may act while watching, but a relevant product or shopping opportunity can still influence what they consider later. He argues for examining how that exposure works with marketing activity closer to the purchase.<\/p>\n\n\n\n

The format can create confusion about who should pay for it and how it should perform. Bryan says marketers may see a product listing in a television experience and assign it to a retail media budget, bringing retail media expectations along with it. That approach can overlook the different role television plays in the decision.<\/p>\n\n\n\n

He sees a broader challenge in the way budgets are divided at the outset. Separate allocations go to channel specialists or different agencies, each making decisions within its own remit. Understanding the combined effect requires those groups to share what they are learning and consider how their work supports the same customer journey.<\/p>\n\n\n\n

Making Product Information Work Across Retailers<\/h2>\n\n\n\n

Behind the recommendation is another practical task: helping systems interpret product information consistently across retailers. Shopsense works with feeds that describe and organize products in different ways, so its models need to recognize those differences when identifying suitable recommendations.<\/p>\n\n\n\n

Bryan points to stock availability as one example. Retailers can use different fields or terms to indicate that a product is available, and a recommendation system needs to understand each version. Shopsense has developed its data cleaning and standardization to account for those variations and make the information usable across shopping experiences.<\/p>\n\n\n\n

For retailers, that makes product information an important part of discovery. Clear descriptions and current availability help a system connect an item with what someone wants. The work behind the scenes supports the experience customers see, from a search result to a product suggested alongside a show.<\/p>\n\n\n\n

Bryan also sees room to improve how shoppers express what they want. He describes wanting to explain a look, find visually similar products, and refine the options through an exchange. That would give shoppers more flexibility than trying to translate an idea into keywords and a series of filters.<\/p>\n\n\n\n

The Expertise Is Already on Set<\/h2>\n\n\n\n

Television already gives people ideas about what to buy. Bryan points to his own Yellowstone-inspired wardrobe as an example, and sees an opportunity to make the expertise behind a show\u2019s look more accessible to its audience.<\/p>\n\n\n\n

A head of wardrobe understands why particular pieces work together and how they express a character\u2019s style. Bryan imagines shopping agents informed by that knowledge, helping viewers adapt a look to their own preferences or an upcoming trip. It is an idea he would like to develop, and one that would require closer collaboration between production and advertising teams.<\/p>\n\n\n\n

Those teams often have limited contact, he says, despite the connection between what appears on screen and what audiences become interested in buying. Bringing them together could help brands and media companies build shopping experiences around the details viewers already care about.<\/p>\n\n\n\n

The Emily in Paris shopper had the interest, the intent, and eventually the outfit. A more connected experience could have helped her find it without working her way through a Netflix executive and a wardrobe department. That is a useful place to focus the technology: making an existing desire easier to act on, then measuring how each part of that experience contributes.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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