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Drew Smith

Part I: Six Reasons Why Your Data Sucks

Drew Smith / January 17, 2014 / 0 Comments

There once was a marketer of trucks,
Who couldn’t report on his marketing bucks.
He pulled out his hair,
Sat down in his chair,
And learned the 6 reasons his data sucks:

We heard of a database deemed substandard,
Upon closer look, turns out they pandered –
To a poor, silly practice,
Painful as a cactus,
Of not keeping field values standard.

Reason #1 – You’re not standardizing field values. This is pretty much at the core of all data management best practices. Having variations for what is essentially the same data value is just messy and can actually get you into trouble. Think about this – you probably have all sorts of variations of ‘United States’ in your database: United States, United States of America, US, USA, etc. What if you need to send an urgent email out to all your customers in the United States and you don’t think to include all possible variations? You could easily miss a portion of your customer base, and they won’t be happy that they didn’t receive that really super important urgent email.

Here are a few fields to consider normalizing, as a starting point: country, state, lead source, job title, and industry.

So, you may ask, how do I go about following such fabulous advice and normalizing my data? Well, my friend, keep reading to find out! In addition to some of the recommendations below, you’ll want to ensure that you have some data management campaigns set up in Marketo to capture any leads who may slip through the cracks of the methods discussed below. Also, make sure your entire teams (marketing and sales) are aware of the agreed upon standard values.

**note to readers: I am aware that line 4 of the above limerick is really lame. Not much rhymes with “practice”, and it IS painful to not have normalized data…so it works, ok?

There once was a marketer who dismissed
The importance of scrubbing a list.
She imported junk,
Her programs all sunk,
Needless to say, sales was miffed…

Reason #2 – You’re not scrubbing lists before you import. You get a list. Maybe you went to a tradeshow. Maybe you purchased a list of people. Maybe you hacked into a top-secret database to get highly coveted data. Regardless, you now have a glorious, shiny spreadsheet just waiting to be imported into Marketo (remember – NOT into your CRM!). So what do you do? Easy peasy. Save as a .csv file, import into Marketo, and take that well deserved coffee break (but put that second donut down – the New Year’s Resolution boogey man is watching), right? WRONG.

That’s not what you do. First off, you eat the second donut, because life is short and donuts are really good. But then, you take the time to scrub the list. I mean, really scrub it.

First, remove any competitors or employees. That’s obvious. Then, find anyone with a job title like “intern,” “student,” or “self-employed.” No offense to all you interns, students or entrepreneurs, but the point I’m trying to make is that you should remove anyone who absolutely does not fit your target profile. For most B2B companies, the biggest time waste for your sales teams is when you present them with sub-par leads.

Then, and this is the most important of all of it. Are you listening? Normalize the data. Ding! Refer to reason number one – make the data coming into your system already nice and squeaky-clean so that your Marketo doesn’t need to do the work for you! So…that means, update all the country values to your company-wide agreed upon standardized values. Do the same for any other field you’ve decided to normalize. Now, and only now, should you import the list into Marketo.

There once was a Marketo form
That didn’t follow best practices or the norm.
Some fields were text,
And left us perplexed,
‘Cuz the junky field values misinformed.

Reason #3 – You’re using text fields on forms instead of pick lists. This is another way to ensure that data coming into your system comes in clean from the beginning. Don’t let someone type in country – that not only leaves you vulnerable to whatever variation of United States of America gets entered, but also is a typo risk! You don’t want to deal with finding values like “‘Murica” in your database and normalizing them after the fact. So, force them to choose from a list of you-approved values.

This is also a great way to easily categorize job titles. You can have a field for job function (sales, marketing, IT, scarf knitter, etc.) and one for job level (manager, director, VP, Jedi master, etc.).
Think how cool this is for lead scoring now – you can totally simplify your scoring rules to look at ONE value in a field instead of something more complicated like job title that contains Vice President, VP, V.P., and so on.

Stay tuned for part 2


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