A friend of mine recently moved into a new build flat earlier this year. The move went quite smooth in terms of shifting and lifting his belongings. What didn’t go well was him having to do what all responsible adults do; take out insurance on his flat, car and gadgets.
Being a novice to the insurance industry he took the safest step and decided to use regulated UK insurance companies to apply for insurance quotes. Shockingly he was not able to proceed with the quotation process on most websites. This was because the automatic address lookup could not find his address. All the websites either asked him to call the call centre to proceed or not offer any suggestion at all.
This type of experience is not shocking to a data quality consultant. I come across this sort of user experience story almost every day in different verticals whether it’s insurance, retail or charities. There are about 200,000 addresses that get created or removed every month in the UK PAF files. These files are a key source in providing the reference data to the automated address lookup API's. This example above only uses the normal UK PAF files. To correct this problem of missing addresses, the relevant companies can ask their PAF lookup providers for enhanced datasets to top up normal PAF; for example Not Yet Built, Multi Residency etc.
These datasets would be able to find the addresses with much more ease and allow end customers to proceed with quotations which will then in return have higher chances of turning into a sale.
To find out how much business you might be losing due to customers not being able to proceed due to an “address not found error message”, you can put monitoring tools on your website to capture the input details of the address that could not be found.
Going back to my friend’s insurance nightmare experience, he was successfully able to buy insurance from a provider by calling the call centre. Just keep in mind, not all your users prefer talking to a person on the phone as an alternative.