People often ask us: “If I don’t have confidence in my data quality, how could predictive scoring possibly work?” Many businesses delay lead scoring initiatives while they wade through cumbersome data clean-up projects – leaving major revenue-driving opportunities on the table in the meantime.
But at Infer, our platform was designed to handle bad data, so we could ensure quick implementations and provide immediate value. We knew this was necessary in order to solve predictive scoring end-to-end. All that we really need to build the model is historical outcomes (which prospects converted and which prospects went on to become customers). We don’t require super clean customer profile data, which is a good thing because almost nobody has it. We’ve got you covered there.
Infer can match whatever limited lead data you have with hundreds of other signals that we collect. With nothing more than a company name or an email address, we can build a complete picture of that prospect or customer to base our scoring algorithms on. This is because we’ve struck numerous data deals and we’re constantly crawling the web for new information. And to ensure we’re matching the right customers with the right signals, we apply deep data science, similar to how Google finds the best search results. For example, our machines know that “IBM” and “International Business Machines” are the same thing.
Now back to your data clean-up project. Instead of doing your data cleansing first in hopes of being able to do better scoring, we recommend doing the scoring first with the data you have. That way, you’ll know which are your most important leads and can focus data cleanup efforts there. There’s no sense in spending time and money augmenting fields and cleaning up data for a prospect who is not a fit for your product anyway.
If you have questions, feel free to reach out and we’d be happy to help.