This is probably the first blog post where I am bragging about how cool our product is, but yet I believe sometimes (very rarely) when you’re seeing so many happy customers telling you how you’ve been able to help them address their data enrichment use cases, it’s OK to share them with the world.
Since we launched our data enrichment tool a year and a half ago, we’ve been seeing so many B2B marketing professionals and sales operations leaders approaching us with a whole slew of different data enrichment and data validation use cases projects. The majority of them though could be summarized as “We’ve got a list of customer domains and we need more information about their business”.
This is a very typical use case for data enrichment vendors on the market – you can see folks from Clearbit, Bambora, MARCOM Robot, Reachforce and others addressing those use cases quite easily. The match rates that are considered appropriate are around 70-80%, meaning that you should expect on average 7 out of 10 accounts to be enriched. But where everybody seems to be struggling is data enrichment based not on the domain name, but on the company name.
Data enrichment vendors are struggling when customers need enrichment based on company name
The use case is basically “We’ve got a list of customer names and we need more information about their business.” Wait… domain name or company name… seems like it’s a negligible difference, but in fact it’s a dramatic one. The data enrichment vendors are showing very low match rates (less than 25%) and increased number of false positives making their customers unhappy. But why is such a sophisticated use case and what should you take into account if you need to enrich company information based on the company name?
Here’s the thing, too many companies have very similar names. And thus all legacy (sometimes they’re being referred to as industry standards) automated enrichment tools will be returning back a very high percentage of false positives thus negatively impacting the match rates.
Too many companies have very similar names
The reason for that is because those legacy tools are merely crawlers that are really good at collecting information, but they’re lacking the correlation mechanisms to be able to compare and analyze the data they’re collecting. Here’s an example - ACME and ACME Corporation could be the same company, but for any data enrichment engine identifying this could be a non-trivial task.
It’s fascinating to see our customers getting excited about the match rates of 70-80% with this exact use case. Many of them almost gave up on the idea because other vendors we had been returning back very low match rates. The answer to the question why is very simple – not only does MARCOM Robot Data Enrichment Engine have a powerful crawling technology collecting data from multiple independent sources, but it also analyzes and correlates the collected information to provide you with the clean data you need for sales and marketing purposes.