What kind of matching logic is used to match InsideView data against a customer database?
Learn about the matching logic InsideView uses to help companies achieve higher match rates and accuracy.
- InsideView uses advanced matching logic to provide higher match rates and precision
- InsideView enables companies to reduce fields on web forms by enriching data on the backend based on core fields like first name, last name, and email address
- InsideView can match company data based on as few fields as company name and location
One of the things I think is rare about InsideView is that you have pretty flexible matching logic for the customer. Some companies limit to specific field values as an input to get an output. I was surprised to see that you guys can be pretty flexible. Can you tell us a little bit about what the minimum fields you need are to get an output from enrichment?
So for a company, literally we just need the name of the company. It will produce a higher scoring match if we have more fields, such as the company and the country, or the company and the URL, or the company and the stock ticker. The more you can feed into it, the more of an accurate match you’re likely to get. If you just give me Acme Printing, we’ve got so many Acme Printings in our database that I don’t really know exactly which one you’re looking for. I’ll give you a really quick example of that: We had a customer come to us recently and one of the records they had in this large dataset was a Fitbit, LTD in San Francisco, California USA. Well, actually, we know there is no Fitbit, LTD in the United States. Fitbit, LTD is actually the UK subsidiary of Fitbit, Inc, located in San Francisco. It’s the corporate headquarters of Fitbit. So when a customer gives us a name that is not congruent with the address, we have to be able to figure out what they are really talking about. Do they really want the US company? Or do they really want the UK company? Which has more weight in the algorithm – is it the name of the company or the company plus the address? That’s just one small example of the types of challenges that we have in matching.
But clearly more than just URLs, a lot of people don’t have URLs. If you can only do a URL match you’re not going to match on nearly as many as we can. You need to use other fields.
Yeah, I agree. And RingLead actually has a unique application that upends websites based on rules. It’s a crawling technology. It’s accurate, obviously handpicking is better, but it’s so important for everybody watching to have flexibility in matching. What that means is you’re going to find less mismatches first, and second, the more flexibility you have within that logic, which it sounds like you guys really have a lot of flexibility, you’ll get both scores on match rates, and then you’ll find more matches in general because the tool will be looking at more attributes.
I was going to say, on contacts, we need First Name, Last Name, Company, or First Name, Last Name, Company, and Email Address. We will be able to match on either of those and return about 40 fields of information just from that. So we could return the company information, the Job Level, the Job Function, the Industry Classification, Size of the Company. So one of the things we do a lot is just append web forms. A lot of companies have an InsideView enrich running on their web forms so that they only have to ask for one, or two, or three fields of information. They can get a much higher completion rate because people don’t really like to fill out much information manually, as you know. So the less you ask, the more likely they’ll complete it, and then we can match it on our backend.