At Kard, we consider higher information results in higher rewards — and that begins by understanding what folks truly purchase.
By categorizing transactions at scale, we’re in a position to assist manufacturers goal the best prospects, issuers improve card utilization, and shoppers get rewarded in ways in which really feel private.
Traditionally, categorizing transaction information was messy and guide. However with a brand new Databricks-powered strategy, Kard is now in a position to classify billions of transactions shortly, precisely, and flexibly, laying the inspiration for personalised 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 thousands and thousands of shoppers by delivering money again affords by means of issuer and fintech banking apps, rewards applications, and EBT platforms. Seeing a ten% or 15% money again provide nudges prospects 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
Figuring out what customers spend their cash on helps manufacturers (and banks and fintechs) perceive their buyer bases in a richer means. In combination, the spend patterns Kard collects:
- Gasoline smarter advertising and marketing campaigns — you possibly can determine high-intent segments based mostly on habits. For instance, if a big share of customers frequently use rideshare providers late at night time, banks and types can goal them with weekend-specific cashback affords.
- Inform product design by revealing unmet wants. If information exhibits 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 widespread service provider overlaps throughout consumer cohorts. As an example, if frequent vacationers persistently e book the identical chain of resorts 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 while you zoom in on the person.
As an example, maybe a particular consumer 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 special consumer has decreased spend on groceries however elevated their use of meals supply apps over the past 90 days. That indicators shifting habits — and a possibility to reward comfort over price.
Lastly, one other consumer 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 best money again affords to enhance their odds of stealing the client away from their most well-liked airline.
With out dependable transaction classes, although, none of those personalization situations are potential.
How rewards platforms traditionally labeled transactions
Categorization is the important thing to unlocking high-ROI go-to-market methods for our manufacturers and issuers, however it’s more durable than it sounds.
First, you’ve received to label all of the transactions. Historically, there’ve been two methods to perform this:
- Have analysts assessment every transaction, line by line, tagging each in keeping with a predefined taxonomy. As you would possibly guess, this technique is tedious, error-prone, and extremely arduous to scale.
- Let customers categorize their very own transactions. Whereas this strategy leaves much less work for analysts, it additionally riddles the information with inconsistencies. One consumer 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 tough 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 spanking new, unseen transactions.
Over time, these fashions might get rid of the necessity for guide tagging. Nonetheless, they require upkeep and upgrades as companies evolve and transaction codecs change. Including new class varieties (say, for an rising business or a brand new shopper vertical) might contain retraining and even re-architecting the mannequin.
To help our rising enterprise, we wanted a extra streamlined, correct, and versatile strategy to categorizing the billions of transactions we obtain every month.
How Databricks powers a contemporary categorization strategy
Working with Databricks, we’ve provide you with a singular, scalable system for transaction categorization:
- Leveraging Databricks AI Features to run batch, agentic workflow that categorizes transactions based mostly 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 towards the required taxonomy, one for every kind of categorization. In a single occasion, we will seize high-level classes like Journey, after which determine hierarchical classes like Journey → Airfare and even additional, Journey → Airfare → Regional Airline.
- Inconsistencies are handed right down to paths which can be evaluated by agent judges, whichallows for re-categorization within the case of errors.
The light-weight prices of this new strategy have given our workforce extra flexibility. If a brand new line of enterprise opens up, we will alter our classes immediately — with out having to completely retrain the mannequin. The truth is, we simply opened up some new CPG classes to help a partnership with a preferred rewards app.
A few of our shoppers have requested that we use their very own class mapping to align with their inner programs. Now, we will simply cross that various taxonomy straight to our new system and it’ll translate outputs accordingly.
“Having the ability to roll up retailers into their respective classes affords us plenty of leverage with prospects,” says Chris Wright, Kard employees machine studying engineer.
“For instance, we will inform retailers that customers inside their class sometimes discover provide varieties x, y, and z work finest. We will additionally assist retailers goal a phase of customers who’ve bought with them up to now and had a latest acceleration in spend inside, say, meals supply or trip share. And we will inform our prospects 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 look like a behind-the-scenes element. However the agility we get from the Databricks AI Features-powered categorizer makes it potential for us to maneuver quick with out breaking our information basis, and have faith within the scalability of the answer.
Plus, it additionally opens the door to new sorts of services for Kard prospects, like:
- Customized card affords based mostly on shifting meals or journey habits
- Stickier rewards for loyal prospects of a particular service provider
- Sensible nudges based mostly on time-of-day or seasonal habits
- 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 personalised rewards expertise that reinforces buy frequency, will increase AOV, and sustains buyer loyalty for manufacturers and issuers alike.
Conclusion
On this weblog submit, we confirmed how Databricks AI Features are powering information enrichment for Kard’s categorization pipeline. This allows personalization at scale, and drives loyalty and worth at a fraction of the hassle it could usually take.
Excited about studying extra? Attain out to one in every of our consultants as we speak!
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 permits 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 straightforward for cardholders to earn rewards on on a regular basis purchases. The corporate is backed by main traders and serves over 45 million cardholders by means of its issuer and companion community.
