Cna yuo raed tihs bolg psot?

Chances are you’ve probably come across this email before:

Aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.

And chances are you can probably understand it. It’s a phenomenon known as Typoglycemia. 

Even when the letters in a sentence are transposed most of us can still read every word – clever huh?

Unfortunately typos in addresses really do matter and while a human might guess what you really meant to type, a computer would not. One missing letter ‘K’ in a customer’s address could mean the difference between their order being delivered to MK1 1AA in Milton Keynes and M1 1AA in Manchester. That’s a long way to go to pick up a missing parcel!

Due to the growing number of different customer touch points, typos and duplicates in customer data is becoming a prevalent problem for businesses in every sector.  Even tools like address validation haven’t been able to solve problems like this, until now.

Fuzzy Matching

Thanks to our new fuzzy matching algorithm, Capture+ enables users to type in even the most obscure address entry and still find and validate the correct address where most validation solutions would simply return a ‘not found’ error.

Similar to when you type gobbledygook into Google and it comes back to you with ‘Did you mean X,Y and Z’, Capture+ will be able to identify and correct common typos, weird spaces and data entry errors to return an accurate postal address. So whether you type High Street, Hiight Street or Hihg Street, Capture+ will still find your address.

What’s really impressive about this new enhancement is its self-learning capabilities. It learns based on previous searches to predict what the user might mean to bring up the most relevant address. For example, you might have a common abbreviation for a town in France, if enough people use it, Capture+ will learn it and return it in search results.

We tihnk it’s cool btu waht do yuo tihnk? Tyr it otu adn lte us knwo belwo.

CTA Fuzzy