What Happens When Buyers Intentionally Share Their Data?
Reward apps, like Fetch, are empowering consumers by transforming the way targeted advertising works.
Targeted advertising, often known for its lack of precision, can be both comical and frustrating. Imagine buying a gift for a baby shower and suddenly being bombarded with diaper ads. This phenomenon reflects the challenges of personalized marketing. The original intent behind data-driven advertising was for brands to connect directly with their ideal customers, showing ads relevant to their interests and avoiding wasting their budget.
However, finding the right audience for an ad has been a constant challenge. For years, marketers have relied on demographic and contextual third-party data to target their advertising. However, as people demand greater control over their personal information, the industry has adopted privacy-focused practices. This transition, while necessary, has made it difficult for marketers to trust that their ads will reach the right people.
As a result, many retailers are venturing into the advertising network business. First-party and zero-party purchase data have become the new standard for targeting ads. However, understanding consumer behavior remains complicated. Knowing what a person buys at a store does not provide a complete picture, which can lead to absurd situations like receiving offers for products that do not correspond with their actual needs.
One way to address this dilemma is to involve consumers in the data collection process. The Fetch rewards app is betting that by including consumers in the marketing data cycle, they will achieve a more comprehensive view of buying behavior. By incentivizing people to share their purchase history through receipt uploads, they are looking to create a database that encompasses all types of retailers.
The proposal for users is clear: upload receipts in exchange for points that can be redeemed for gift cards. Those receipts, often accumulating in pockets or slipping between wallet pages, represent an unprecedented opportunity to access real buying behavior.
Transforming those pieces of paper into usable data is no simple task. However, Fetch has harnessed artificial intelligence to address this need. Unlike many AI applications that have produced inaccurate results, the organization has developed a machine learning system capable of reading and cataloging receipts from nearly any store.
Since receipts lack standardization and present different formats and abbreviations, interpreting them has been challenging. To tackle this, Fetch dedicated two years to manually cataloging products and receipts from various stores. Now, their AI can interpret items like "GRN BN 16OZ" as a pack of green beans or "MLK 2% OG 128OZ" as a gallon of organic milk.
This system becomes increasingly precise as each new piece of data helps improve it. In the last year, Fetch processed over 3 billion physical receipts and 360 million electronic receipts, with users sharing over 85 percent of their purchases, from groceries to restaurant expenses.
With this complete and up-to-date database, marketers can then customize specific offers in the Fetch app. This not only allows consumers to earn points for rewards but also creates a valuable connection between brands and their best customers.
Of course, the collection of personal data raises security concerns. To mitigate this, Fetch uses "data clean rooms," cloud environments designed to anonymize shared information. This ensures that the data is used only by the companies to which consumers choose to provide it, without being linked to personally identifiable information.
By securely sharing their purchase information, a bridge is established between the physical and digital worlds, offering a more comprehensive perspective on consumer behavior. Fetch is promoting the idea that rewards are a more effective way to advertise, placing control in the hands of consumers and ensuring relevance.
With this approach, both marketers and consumers are expected to move closer to the initial promise of targeted advertising: presenting offers to people who genuinely want to see them. Perhaps then, people will stop receiving irrelevant ads until they really need them.