At Kard, we consider higher information results in higher rewards — and that begins by understanding what folks really purchase.
By categorizing transactions at scale, we’re capable of assist manufacturers goal the suitable 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 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 a rewards market.
Our platform provides manufacturers like Dell, CVS, Allbirds, and Spherical Desk Pizza entry to tens of hundreds of thousands of shoppers by delivering money again provides by issuer and fintech banking apps, rewards applications, and EBT platforms. Seeing a ten% or 15% money again supply nudges prospects towards a purchase order (typically one which’s greater 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 significantly 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 method. In combination, the spend patterns Kard collects:
- Gasoline smarter advertising campaigns — you’ll be able to establish high-intent segments based mostly on habits. For instance, if a big share of customers recurrently use rideshare companies late at evening, banks and types can goal them with weekend-specific cashback provides.
- 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 widespread service provider overlaps throughout consumer cohorts. For example, if frequent vacationers persistently e book the identical chain of lodges and rental automobile companies, 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.
For example, maybe a particular consumer spends probably the most on sports activities playing. A generic retail supply would possibly go unnoticed, however a promo for a betting app may drive prompt engagement.
Say a unique consumer has decreased spend on groceries however elevated their use of meals supply apps over the past 90 days. That alerts shifting habits — and a chance to reward comfort over value.
Lastly, one other consumer flies typically, however at all times with the identical airline. That loyalty will be strengthened with focused rewards, and even upsold to that airline’s premium tier. Different airline manufacturers might not even need to goal that particular person. Or they could solely floor the best money again provides to enhance their odds of stealing the client away from their most popular airline.
With out dependable transaction classes, although, none of those personalization situations 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, but it surely’s tougher than it sounds.
First, you’ve received to label all of the transactions. Historically, there’ve been two methods to perform this:
- Have analysts overview every transactionline by line, tagging each in response to 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 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 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 spanking new, unseen transactions.
Over time, these fashions may get rid of the necessity for guide tagging. Nevertheless, they require upkeep and upgrades as companies evolve and transaction codecs change. Including new class sorts (say, for an rising trade or a brand new consumer vertical) may 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 give you a novel, 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 sort of categorization. In a single occasion, we will seize high-level classes like Journey, after which establish hierarchical classes like Journey → Airfare and even additional, Journey → Airfare → Regional Airline.
- Inconsistencies are handed all the way 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 method have given our workforce extra flexibility. If a brand new line of enterprise opens up, we will alter our classes straight away — with out having to completely retrain the mannequin. Actually, 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 methods. Now, we will 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 provides us a variety of leverage with prospects,” says Chris Wright, Kard workers machine studying engineer.
“For instance, we will inform retailers that customers inside their class usually discover supply sorts x, y, and z work finest. We are able to additionally assist retailers goal a phase of customers who’ve bought with them previously 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 might look like a behind-the-scenes element. However the agility we get from the Databricks AI Features-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 for Kard prospects, like:
- Customized card provides based mostly on shifting meals or journey habits
- Stickier rewards for loyal prospects of a particular service provider
- Good 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 customized rewards expertise that enhances buy frequency, will increase AOV, and sustains buyer loyalty for manufacturers and issuers alike.
Conclusion
On this weblog publish, 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 trouble it will usually take.
Fascinated about studying extra? Attain out to certainly one of our specialists 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 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 its issuer and associate community.
