Summary:
Swish secures $2.3 million in seed funding to revolutionize product sampling by giving away full-size samples during online grocery shopping.
The startup uses AI and proprietary algorithms to treat products as media, targeting customers based on real purchase data for higher conversion rates.
In a case study, Swish drove over 4,000 purchases from 1,100 returning customers after sampling, proving the model's effectiveness.
Sampling is timed intelligently—e.g., when customers buy complementary items or are about to run out—avoiding waste and increasing relevance.
Data privacy is prioritized; only aggregate insights are shared with brands, with no storage of personal information.
The New Era of Product Sampling
Advertisers typically serve ads to try and sell products. But what if they skipped over the ad completely and instead just handed products straight to the consumer?
That’s the innovative idea behind Swish, a startup that partners with retailers to provide full-size samples of CPG (consumer packaged goods) products to people doing their grocery shopping online. It’s like an extreme form of sampling that cuts out the middleman.
On Friday, September 26th, 2025, Swish announced $2.3 million in seed funding led by BD Ventures, a new venture firm co-founded by Jonathan Opdyke, who also co-founded retail exchange HookLogic (which he sold to Criteo in 2016).
How Swish Works: Product as Media
Like many other programmatic platforms, Swish uses AI to understand a consumer’s interests and purchase habits. What makes Swish unique is that it considers the product itself to be a form of media, according to CEO and Founder Adam Stave.
Swish has partnerships with retailers—including one with California-based Stater Bros. Markets—and uses a combination of OpenAI models, Azure cloud infrastructure, and proprietary algorithms to analyze customer behavior. The platform then adds free samples of targeted products to a customer’s cart before checkout, aiming to drive conversions directly.
Although giving away full-size samples isn’t cheap, the strategy has proven effective. In a recent collaboration with a leading cream cheese brand, Swish gave away 3,600 containers to customers who hadn’t bought that brand in the previous 12 months. Around 1,100 of those customers returned and bought over 4,000 containers, boosting revenue for both the brand and retailer.
The Benefits of Smart Sampling
Traditional sampling is often expensive and lacks personalization, requiring brands to create smaller, single-use products. Swish addresses this by "looking in every basket" to learn customer habits, such as consumption rates, and determining the optimal timing for samples.
For instance, a salsa company might target people buying frozen burritos without salsa, or those who bought salsa recently and are about to run out. Sometimes, samples are offered when customers are buying a competitor’s product, allowing direct comparison at home.
Data Insights and Privacy
Swish’s AI tracks patterns over time to infer purchase habits, but no personally identifiable information is stored. Brands receive aggregate insights, like how many sample recipients later bought the product, while individualized data remains with the retailers.
This approach provides more valuable insights than traditional demographic targeting, helping to eliminate waste by focusing on likely converters. As BD Ventures’ Opdyke noted, Swish aims to "eliminate waste in the equation" and could upend traditional CPG marketing, making sampling more efficient and accessible.
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