While groundhogs and Cupid bank on careful timing during the year’s shortest month, the Pinwheel team has been working day in and day out to deliver new, value-add product updates. This month, we’re excited to share two major announcements: an improved Pinwheel Link user experience and an ongoing commitment to data quality.
It goes without saying that the impetus behind every product update is our dedication to customer success — and this month is no exception. As this update specifically builds on data gathered from real customer scenarios, we’re reminded of the uniquely special opportunity we have to build a fairer financial future together.
Updated Link UX
Last week we launched a handful of exciting updates to the Pinwheel Link user experience that will help users more easily find their employer or payroll provider and boost conversion. Link is a front-end modal that allows users to search across 1,500 providers and 100,000+ employers, authenticate using their credentials, and seamlessly authorize access to their payroll account.
These improvements — which are automatic and do not require any development work — reflect key data we collected from comprehensive user studies and our existing Link experience, spanning various user scenarios like search, user recapture, and selection confirmation.
To make it easier for users to distinguish the two ways to search, we updated the landing screen to separate payroll providers from employers. We’re hoping this will call more attention to providers while highlighting our most selected employers.
Additionally, the payroll provider experience will allow users to see the names and logos for supported providers, helping users more clearly identify their correct provider and furthering them through the Link experience.
While our mappings continue to grow, there may be cases when a user cannot find their employer. In this instance, we included an in-list option for users to search by payroll provider instead. This added opportunity aims to retain users who might have otherwise abandoned the experience.
We realize that extra clarification is needed in certain instances where a user’s employer selection isn’t explicitly clear. For example, users who are bank employees (e.g. Chase, Capital One) might mistakenly select their primary bank rather than their employer when performing a search. Or, users employed by a company like Uber might confuse corporate employment with gig work. To resolve this, we added a confirmation screen after an employer is selected to confirm that the user is, in fact, an employee of that company. If not, they are guided back to perform another search.
Improved data quality
We’re all familiar with Maslow’s hierarchy of needs — if not consciously, the concept itself is inherently understood. The hierarchy states that our most basic human needs are physiological: food, water, warmth, rest. In the realm of financial data, we liken data quality to this most basic need. From good data quality all else is possible, and without it nothing is.
If good data quality is the foundation on which things like verification of income and employment (VoIE) and earned wage access (EWA) are built, then why are so many companies missing the mark? All too often we’re informed by customers that our competitors simply aren’t delivering quality data. That’s why Pinwheel is dedicated to improving ours.
Our team knows how important data quality is to both customers and end users. While much of the groundwork has been laid, we’re working hard to build a system that ensures our data is accurate and comprehensive — in other words, the data we return matches what’s in a user’s payroll system and every piece of available data is retrieved. Having this infrastructure in place will also allow us to detect anomalies, enabling us to identify issues and address them quickly.
We will continue to invest in our data quality system, adding automated rulesets to ensure the infrastructure is current and reflects customers’ needs. One validation rule we’ve already implemented is net = gross – deductions, and it has produced some surprising outcomes. In one instance, we found a consumer’s gross pay came out to less than their net pay due to reverse deductions, which we hadn’t foreseen. Having rules in place to flag these anomalies will ultimately help our customers make better decisions (e.g. better underwriting based on gross pay).
This is just one example of how improved data quality is crucial to any financial data platform. For Pinwheel, our system is already helping us better understand the nuances of payroll data. We hope that by passing these findings on to our customers, we keep the flywheel turning to enable better financial products and services.