At Kard, we imagine higher information results in higher rewards — and that begins by understanding what individuals truly purchase.
By categorizing transactions at scale, we’re capable of assist manufacturers goal the fitting clients, issuers improve card utilization, and shoppers get rewarded in ways in which really feel private.
Traditionally, categorizing transaction information was messy and handbook. However with a brand new Databricks-powered method, Kard is now capable of classify billions of transactions shortly, precisely, and flexibly, laying the muse for customized rewards that drive loyalty and long-term worth.
What Kard does
Kard drives loyalty for each cardholder and shopper by means of a rewards market.
Our platform offers manufacturers like Dell, CVS, Allbirds, and Spherical Desk Pizza entry to tens of tens of millions of shoppers by delivering money again gives by means of issuer and fintech banking apps, rewards applications, and EBT platforms. Seeing a ten% or 15% money again provide nudges clients towards a purchase order (usually one which’s larger so as worth).
And on Kard’s pay-for-performance mannequin, manufacturers solely pay when a purchase order happens, guaranteeing ample attain with out the excessive prices or dangers of conventional media shopping for.
Money again rewards profit the issuers and fintechs, too. By providing rewards that customers care about, they improve engagement and utilization amongst their cardholders.
However what makes Kard notably particular is the category-level insights it captures, offering perception with out exposing any PII.
Why category-level insights matter for rewards
Realizing what customers spend their cash on helps manufacturers (and banks and fintechs) perceive their buyer bases in a richer manner. In mixture, the spend patterns Kard collects:
- Gas smarter advertising campaigns — you’ll be able to determine high-intent segments primarily based on conduct. For instance, if a big share of customers commonly use rideshare providers late at evening, banks and types can goal them with weekend-specific cashback gives.
- Inform product design by revealing unmet wants. If information reveals that youthful customers are shifting spend from grocery shops to meals supply apps, a fintech would possibly prioritize rewards tied to convenience-driven classes.
- Encourage new partnerships by surfacing frequent service provider overlaps throughout person cohorts. As an illustration, if frequent vacationers persistently ebook the identical chain of inns and rental automobile businesses, there’s a powerful case for negotiating co-branded rewards or unique perks with these companions.
Categorical patterns get much more highly effective whenever you zoom in on the person.
As an illustration, maybe a selected person spends essentially the most on sports activities playing. A generic retail provide would possibly go unnoticed, however a promo for a betting app might drive instantaneous engagement.
Say a distinct person has decreased spend on groceries however elevated their use of meals supply apps during the last 90 days. That indicators shifting habits — and a possibility to reward comfort over price.
Lastly, one other person flies usually, however at all times with the identical airline. That loyalty could be strengthened with focused rewards, and even upsold to that airline’s premium tier. Different airline manufacturers could not even need to goal that particular person. Or they may solely floor the very best money again gives to enhance their odds of stealing the shopper away from their most popular airline.
With out dependable transaction classes, although, none of those personalization eventualities are attainable.
How rewards platforms traditionally labeled transactions
Categorization is the important thing to unlocking high-ROI go-to-market methods for our manufacturers and issuers, nevertheless it’s tougher than it sounds.
First, you’ve acquired to label all of the transactions. Historically, there’ve been two methods to perform this:
- Have analysts evaluation every transaction, line by line, tagging every one in accordance with a predefined taxonomy. As you would possibly guess, this methodology is tedious, error-prone, and extremely laborious to scale.
- Let customers categorize their very own transactions. Whereas this method leaves much less work for analysts, it additionally riddles the info with inconsistencies. One person would possibly label Domino’s as “quick meals,” one other would possibly name it “pizza,” and a 3rd would possibly tag it “consolation meals,” making it extraordinarily troublesome to attract dependable insights.
As soon as a considerable quantity of transactions are labeled, engineering groups can begin coaching machine studying fashions like LightGBM, XGBoost, or BERT to predict classes for brand new, unseen transactions.
Over time, these fashions might remove the necessity for handbook tagging. Nonetheless, they require upkeep and upgrades as companies evolve and transaction codecs change. Including new class varieties (say, for an rising trade or a brand new shopper vertical) might contain retraining and even re-architecting the mannequin.
To assist our rising enterprise, we wanted a extra streamlined, correct, and versatile method to categorizing the billions of transactions we obtain every month.
How Databricks powers a contemporary categorization method
Working with Databricks, we’ve provide you with a novel, scalable system for transaction categorization:
- Leveraging Databricks AI Capabilities to run batch, agentic workflow that categorizes transactions primarily based upon an internally derived taxonomy.
- The outcomes are constrained with structured output performance, utilizing the json_schema response format with the enum characteristic to restrict errors.
- AI brokers course of incoming transactions in opposition to the required taxonomy, one for every sort of categorization. In a single occasion, we are able to seize high-level classes like Journey, after which determine hierarchical classes like Journey → Airfare and even additional, Journey → Airfare → Regional Airline.
- Inconsistencies are handed all the way down to paths which are evaluated by agent judges, whichallows for re-categorization within the case of errors.
The light-weight prices of this new method have given our staff extra flexibility. If a brand new line of enterprise opens up, we are able to alter our classes immediately — with out having to completely retrain the mannequin. In truth, we simply opened up some new CPG classes to assist a partnership with a well-liked rewards app.
A few of our purchasers have requested that we use their very own class mapping to align with their inside programs. Now, we are able to simply go that different taxonomy straight to our new system and it’ll translate outputs accordingly.
“Having the ability to roll up retailers into their respective classes gives us a whole lot of leverage with clients,” says Chris Wright, Kard workers machine studying engineer.
“For instance, we are able to inform retailers that customers inside their class usually discover provide varieties x, y, and z work greatest. We will additionally assist retailers goal a phase of customers who’ve bought with them up to now and had a current acceleration in spend inside, say, meals supply or journey share. And we are able to inform our clients who they’re competing with of their class and area to allow them to refine their campaigns accordingly.”
What’s subsequent for Kard and Databricks: hyper-personalization
Transaction classes could seem to be a behind-the-scenes element. However the agility we get from the Databricks AI Capabilities-powered categorizer makes it attainable for us to maneuver quick with out breaking our information basis, and believe within the scalability of the answer.
Plus, it additionally opens the door to new sorts of services and products for Kard clients, like:
- Customized card gives primarily based on shifting meals or journey habits
- Stickier rewards for loyal clients of a selected service provider
- Good nudges primarily based on time-of-day or seasonal conduct
- Service provider-funded cashback applications focused by phase, not simply demographics
- Earned factors applications (for manufacturers and issuers)
By investing in smarter categorization now, we’re laying the groundwork for a very customized rewards expertise that enhances buy frequency, will increase AOV, and sustains buyer loyalty for manufacturers and issuers alike.
Conclusion
On this weblog submit, we confirmed how Databricks AI Capabilities are powering information enrichment for Kard’s categorization pipeline. This allows personalization at scale, and drives loyalty and worth at a fraction of the trouble it could usually take.
Concerned about studying extra? Attain out to certainly one of our specialists immediately!
About Kard
Kard is a New York-based fintech firm based in 2015 that gives a rewards-as-a-service platform for banks, neobanks, and card issuers. Its API allows monetary establishments to shortly launch and customise cardholder rewards applications, connecting customers to 1000’s of retailers and types throughout the US. Kard’s platform is designed to drive buyer loyalty and engagement by making it simple for cardholders to earn rewards on on a regular basis purchases. The corporate is backed by main buyers and serves over 45 million cardholders by means of its issuer and companion community.
