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How to make sure that addresses are quality ones


You need to keep your addresses clean because its always about quality over quantity. It’s worth doing because it…

  1. Save money on distribution
  2. Reduces number of ‘return to sender’
  3. Improve your metrics
  4. Marketing list service providers don’t necessarily clean up addresses, so you need to!
Cleaning up – the manual way
  1. Identify typos in addresses and correct them
  2. Find duplicate addresses to delete
  3. Manually work out what County the address belongs to
  4. Move data into different fields to get the first line of address and street info correct
  5. Make sure that postcodes have spacing in the right place
  6. Remove non-address data like email addresses
Cleaning up steps – the DataWand way

We use our magical ‘Address cleaner’ wand, it does all the manual work for you, in seconds.

  1. It auto formats the address into its different components (house number, street, place, town, county, postcode)
  2. It auto corrects the case
  3. It automatically works out County and Country info if missing
  4. Formats postcodes
  5. Deletes email addresses found in the address

And hey presto! all your addresses are cleaned up within seconds ready for your next post mail campaign.

By the way, we speak from experience as we saved ourselves 4 hours by using DataWand to clean up a data list containing 5,000 UK addresses.

P.S. If your mailing labels only support 5 lines of address data you can use DataWand to easily merge address fields together and if you want to remove records because the postcode is incomplete, DataWand’s got an action for that too.

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Merging street address data example

Last week we were asked to merge street address data from two different fields into one because the email campaign software importer that was going to be used only allowed a single street address field.

Microsoft Excel could have done this using a macro but if you don’t have Microsoft Excel or you do and don’t want to write macros (like us), luckily there’s DataWand to the rescue.

We just use DataWand’s ‘Merge columns’ wand, we specify the two fields that we want to merge (e.g. B,C) and hey presto! your preview shows the merged info.

Here’s it in action; before and after:




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How to clean up names

Towards the end of last week we were asked by a potential customer if ….

“our software can automatically specify genders etc? What does it do with ambiguous ones?
One of our clients lists is just a field containing first name and surname, some is capitalised and some is not.”

Yes, we can!

DataWand has a good go at deducing genders (Male / Female) and titles (Mr. / Ms.). If its an ambiguous one (we call it a unisex name), we leave the data as is. (and if we don’t have a reference gender for it we leave it untouched too).
We also have a ‘Title Case’ action which will sort out any name field case issues.

For example if you wanted to extract the gender and titles, here’s what the data input could be and its transformed data results:

kelly mitchell –> Kelly Mitchell,
Ms nancy drew –> Ms. Nancy Drew, Female
Jay fitzgerald –> Jay Fitzgerald,
Russell Rodgers –> Mr. Russell Rogers, Male

(The commas above just represents the separate of data)

DataWand does these data transformations in real-time, its automated and done in moments. We don’t have no gremlins or little imps in the background hand crunching the data. Of course there are some alternatives to using us to extract gender and title information, these include:

– Manually correcting the data and working out what the genders are
– Asking a software coder very nicely to help out to automate the work needed
– Learning how to write Excel macros and do it yourself
– Using Microsoft Word’s Change Case menu option to correct the case

Of course we’re bias and we would choose DataWand everytime to extract gender and title from name information, but the choice is yours!

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What’s in a name?

Here at DataWand we’ve been concocting up rules and brewing algorithms to be able to make very educated guesses as to what someone’s name, title and gender is based on names and email addresses given.

Why? Well, its to help marketeers make their life easier so that they end up with better formed names for their marketing campaigns, be that via email or via post. In the case of email, our aim is to improve the probability of getting pass spam filters and reduce reader irritation! Imagine you receive an email with Dear J, instead of Dear Joanna, wouldn’t that irritate you? And if you’re wondering why post, its to improve the chances of it being delivered to the right final address, especially as if there’s a postal redirection.

In our perfect world, it would have been really nice if all personal email addresses were formatted as:


But, no, that would be too easy and lets not forget that names aren’t unique and some people choose to use only part of their name in their email address. So, back to the real world and normally what happens is we get a range of email addresses which is missing structure and we end up reformatting everything manually.

But now there’s an alternative, DataWand! We’ve built in a couple of nifty actions that we’re currently testing with our beta users.

The first one is ‘Extract details from email address’. It looks at email addresses and automatically extracts forenames and surnames even when its not so nicely formatted and title cases the text for you: –> Jo –> Albert Einstein –> Marie Curie –> J Goodall –> Edwin Hubble

Now, what’s clever (if we don’t say ourselves) is our shiny new ‘Ensure title’ action which will add Mr. or Ms. as a prefix to existing data if no title is present, so you end up with:

Ms. Jo
Mr. Albert Einstein
Ms. Marie Curie
J Goodall
Mr. Edwin Hubble

It may not be 100% perfect, but its getting you closer there while saving you hours of manual data entry work.

Is there anything else that you can think of which will help even more?