How we do it
A lot of people sell data. Ours is better.
When we started back in 2006, we set out to deliver a great service for a flat monthly fee.
That turned out to be almost impossible.
Data vendors wanted us to charge our customers per record, and they wanted a percentage of each record sold. It took us a year to get one vendor to agree to our new pro-small-business pricing model, and that was only for one dataset. Fortunately, we were also already gathering data ourselves that we could sell however we wanted. And thus, our original, foreclosure-only, service was born.
Since then, we’ve earned the trust of many big vendors for this new pricing model. They’ve realized we aren’t after their lucrative deals with big business, allowing us to add many new data sets cost effectively for our small business customers. We’ve added more of our own data as well.
But cost-effectiveness can’t be your only criteria for data acquisition. We also look for timeliness, accuracy, coverage, and population. To that end we do bake-offs between vendors, we backtest against county records and other sources, we use multiple sources, and we have staff manually backfill as needed.
This multi-prong approach is rare in the big companies and unheard of in small business offerings. Even with all this effort, public records data will never be perfect, but through feedback from thousands of customers, we know it sets us far apart.
Cleaned & Enhanced
No matter the source of the data, we are constantly finding new ways to clean and enhance it.
There are 3,142 counties (and equivalents) in the United States, and each has a different idea about how data should be gathered and stored.
Worse, the number of errors is atrocious. Our background in data science led us to recognize early on that we could fix many issues that would drive our customers crazy, and we built our systems from the ground up with that in mind.
Our first service in 2007 launched with a rules engine that evaluated nearly 200 rules on each new record.
Since that time, we’ve grown from just simple pattern matching to natural language processing, machine learning, and other modern techniques to find and correct errors, normalize data across counties and sources, flag problems for staff to research, and even create new data.
Our estimated loan positions and transfer types are just two examples of data that doesn’t exist directly in public records, but which we instead create from those records.
We also leverage the expertise of best-in-class vendors for certain data models like estimated property values (automated valuation models).
None of this would be possible without a full-time research team that is constantly correcting data, backtesting sources and automations, collecting our own data, backfilling data when sources fall short, and using the insights they gain from all of those activities to create and train new rules and automations.
Connected for Context
The earth isn’t flat. Neither is our data.
Historically, public records have been treated as flat files, that are simply overwritten with new records as they come in.
While that “flat” data is amazingly useful, that approach leaves hundreds of opportunities to gain insights into the relationships between people and properties completely untouched. Our unique OwnerGraph™ ties records together across sources and over time to discover those insights.
We started this work in 2006 with the launch of our best-in-market Transaction History, which showed the relationships between recorded deeds, loans, foreclosures, assignments and other documents over time. From there we added the relationships between those documents and properties, properties and owners, owners to each other, owners to – you get the idea.
From the beginning, we saw that there was often more value in understanding the relationships between these bits of data, then in the data itself. Something all the data companies focused on delivering batches of flat files, sold by the record, have missed completely.
Over 10 years we’ve still barely scratched the surface of what we believe is possible. Our work on the OwnerGraph™ is one of our most exciting endeavors and we can’t wait to show you more.
Simplified & Clarified
Small business owners are experts in their business.
They are typically not expert data scientists, integrators, and marketers, let alone have the time do those things even if they were experts in them.
From day one, we’ve worked not only to bring the best data to small business but to get them out of the business of importing and exporting data, integrating data and systems, building platforms, selecting vendors and all the other tasks necessary to be expert targeted marketers.
Instead, we do it for our customers, making it easy for them to leverage data to connect with customers and grow their business.
Automated & Integrated
All the data in the world is useless unless it leads to insights or actions.
When it comes to taking action, big companies have built their systems around batch processes and big campaigns. We saw a better way. Stream-based, individually matched, and immediately actionable.
From the beginning, we’ve taken an event-driven streaming approach to building our systems. This gives our customers faster access to new data – the events in the life of a person or property which our customer can leverage to grow their business.
Unfortunately, a firehose of data is of very little use to most small businesses, as they have neither the time or ability to digest it all. This is where the power of targeting comes in – it allows our small business customers to focus on the right events to grow their business, using our 250 plus criteria, and easy preset Quick Lists to get them started.
That level of event-based targeting is no small feat. We process hundreds of thousands of events each day, matched to tens of thousands of customer criteria sets, leveraging hundreds of criteria.
The end result? The ability to alert our customers quickly to the best new opportunities, and to automate actions against those opportunities on their behalf. No one else in the industry offers anything close.