Good Data Hygiene Habits Don’t Cost a Thing. Missed Opportunities Cost a Lot.

Estimated reading time: 10 minutes

Under 30% of marketers are confident in the data integrity of their lead records.1

That means over 70% of marketers (more than two-thirds…two-thirds!) lack confidence in their MAP and CRM data. And yet, surveys rank qualified leads as the top goal of companies, targeted messaging as the greatest benefit of MAPs, and productivity as the most important objective when it comes to marketing automation strategy2 — ambitions made possible with reliably accurate data.

So let’s dive into all things data hygiene. I’ll start by discussing the financial aspect of dirty data. Because really, why do anything if you don’t see the business cents sense? Then, I’ll focus on tactics for making good data hygiene part of your everyday marketing practice.

money in piggy bank

Good Data Hygiene Can Reduce Your Cost Per Lead (CPL)

Imagine the money you spend on marketing campaigns. Imagine your projected ROI. Now imagine that ROI falling short because incomplete, duplicate, or invalid data rendered your newly obtained lead information useless. Not only that, but those bad/unusable leads now take up space in your database; they might’ve skewed metrics on your campaign performance, or they may contribute to your database record limit.

For net new leads you can market to, tally the hours your team spent trying to standardize and format data for import. How many of your unusable leads seemed like ideal prospects — if only they had contact information?

Now consider the friction these issues cause between your marketing and sales departments. Good data hygiene is an important element of better marketing and sales alignment.

Depending on regional regulations and how your leads were obtained, try to imagine the potential fines and/or legal fees your organization could face for privacy violations or non-compliance.

The activities your marketing group uses to obtain leads may cost money, but the scenarios laid out above could cost you more. And costs add up.

As of the past year or so, the annual cost of an enterprise’s dirty data, which often translates into bad leads, can be $4 million and up.3

Like a virus, dirty data mutates. And propagates. It could start as blank mailing address, perhaps picks up the wrong address through third-party data enrichment, then gets synced to your CRM where more processing happens. Before you know it, you’ve racked up costs not only for obtaining and enriching lead data, but also for sending swag (perhaps as part of a direct mail campaign) to the wrong addresses and directing salespeople to follow-up on leads who never received your packages — leads who never respond. Sounds fun, right?

In the long run, it’ll cost less to slow down and build an operational center of excellence than to hire more manpower and/or regularly perform reconstructive surgery on your MAP of choice. We’re not saying you need to build a gold-medal Ferrari; we’re simply saying that it could be more cost-effective aspiring to it than trying to turn a classic Corolla into a race car. (And if you like the Ferrari idea, you’ll love what we can do for you. Just saying.)

You’ll save time, effort, and money not having to backtrack and troubleshoot. You’ll sidestep the frustration of having to tweak your operational processes repeatedly until you find a formula that sticks. Goldilocks had the luxury of sampling all the chairs, porridge bowls and beds. You, however, as part of a revenue-generating apparatus, have much better things to do.

Data Hygiene is Everyone’s Responsibility

To reduce your margin of error and more easily uncover what’s ailing your operational processes, you should clean your data before import and set up your automation to standardize data regularly. To stave off headaches down the road, you must maintain good data hygiene.

While marketing ops practitioners understand this, many stakeholders in the sales funnel do not. For instance, a field marketer —from whom data can often originate— may submit a tradeshow list for upload without standardizing data fields. IOWA – iowa – Iowa – IA, anyone? A sales rep might begrudgingly enter leads into your CRM with no geographic data…and then asks to invite them to the upcoming roadshow. Do all the leads he input reside in the same metro area, or are they spread across several regions? If your roadshow stops in 17 cities, which location(s) do you invite them to? San Francisco? New York? …Singapore?

When it comes to data cleanliness and completeness, it really does take a village. And while we don’t know the streets and landmarks of your village, we do have some ideas around getting your villagers to work together and keep [sales] traffic moving smoothly.

traffic signals

Educate your stakeholder not only on what to do, but why.

Do you spell out full country names? Use 2-letter abbreviations? Categorize certain countries as regions? Perhaps your persona segmentation requires grouping certain job titles into broad job functions. Whatever your standards are, socialize them so your teams and stakeholders are on the same page.

You can increase the odds of your team developing good data hygiene if you explain its importance. Demonstrate how taking shortcuts and potentially entering dirty data now will have consequences for them down the line. Your stakeholders may grumble about cleaning up their lists for upload, but they’ll be grateful when you can better target and personalize emails for their audiences down the line. Remember, better data = increased opportunity for targeted engagement = more qualified sales pipeline.

An Ounce of Prevention Is Worth a Pound of Cure: Strategies for Improving Inbound Data

From the get-go, throw up guardrails to keep your data imports as clean as can be. This helps prevent sync issues, incomplete records passing between systems, and unnecessary frustration – both on your part and your stakeholders’. This also decreases the risk of dirty data convoluting your automation filters (e.g. pulling financial services executives into a healthcare developer segment) or appearing sloppy in your personalized communications. So, what are some basic guardrails?

Data Hygiene for List Imports

Create templates for data submissions.

You’ve probably been there: you receive a list of leads to import but it only contains email addresses. Your data submitter doesn’t know that you also need first and last name, title, company, and industry. But if you create a spreadsheet template with headers indicating required and optional fields, your submitter isn’t guessing, and you aren’t having to troubleshoot potential errors in lead routing or scoring. You can take that template a step further by adding additional reference tabs or conditional formatting. For example, you can create a picklist of U.S. state values. Then, a data/list submitter can reference if your organization uses full state spellings or two-letter abbreviations, as well as verify if “Puerto Rico” and “Guam” are state values or country values.

checklist

Provide guidelines for data submissions.

Guidelines can be as simple as: Make sure all required fields have a value — no blanks! Or: Check for letter case so you don’t have data values in all caps. Spelling and standardizing case/capitalization are easy ways to appear more professional in communications. Dear roberrrt in a personalized email could just as easily have been Dear Robert if the spreadsheet he was uploaded from was proofread before submission or upload.

Guidelines can also be more involved. For example, take time to verify data integrity after a .txt download is parsed as an .xls, then converted into a .csv for upload. The changing file type can cause a loss or change in special characters. A personalized Hello Göçıž could erroneously transform into Hello Go’i~^. You may not catch all potential errors (hey, nobody’s perfect), but you can certainly minimize the number of errors.

When establishing guidelines, remember localization.

Imagine a regional stakeholder asks you to send a series of invites to German speakers — with their proper honorifics. Without having this data value beforehand, you wouldn’t be able to insert it into your invites, e.g. Sehr geehrte Frau Doktor Doktor Schoff (nope, Doktor Doktor isn’t a typo) or Sehr geehrter Herr Professor Fassbender. These opening salutations are dependent on having two factors in your database records: gender and German honorific.

Although the use of such formality is diminishing (for instance, a social media platform geared towards young users may simply open with Hallo Jana, or Hello Jane), formal opening salutations in letters and emails are still common practice, especially in long-established corporate settings. To better execute this practice, establish what your regional stakeholders’ expectations are and ensure they know what’s expected of them, i.e. providing necessary information that will later be pulled into communications.

Of course, if you’re emailing Germans in general, you have much larger concerns than formal opening (and equally formal closing) salutations. You also need explicit double opt-in, GDPR compliance, easy access to unsubscribe, an Impressum that’s no more than two clicks away, and your company contact information displayed per the legal requirements of your company type. Did your eyes just glaze over? Yeah, those topics each deserve their own blog posts. The thing to note here is that there are lawyers specializing in litigation around these topics. So if you’re not reasonably confident in your data governance, work on that first. An informal or incorrect opening salutation should be lower on your priority list.

reaching a goal, chance, next steps

Data Hygiene for Form Submissions

Collect All Essential Data

Make sure forms collect all your required fields for lead processing and system syncs. Whether you collect all fields in a single form submission or whether you gradually collect information through progressive profiling is up to you, but it’s MOPS 101 to know that you must collect values for the data fields you use in operational processes like lead scoring and routing.

Standardize Incoming Data with Picklists

In form fields with finite answers, like country or state, use picklists rather than open text fields. As with list uploads, the cleaner your data is from the get-go, the less processing your system needs to do, and the less manual troubleshooting you need to do days, weeks, or months from now.

Speaking of state values, you can make your state picklist(s) appear conditionally, depending on the country self-selected by your lead/form-submitter.

Consider Your Approach to Phone Numbers

Phone number is one of those fields with an elusive “ideal solution” for standardization as it comes in countless permutations.

If you only do business in one country or region, you might consider a form that auto-formats phone numbers into the appropriate arrangement. For example, for phone numbers in the United States, that could look like (555) 887-9034 or 555-887-9034.

If your business operates in multiple regions with various formats, you may want to create a field for each phone number arrangement and use advanced visibility rules to display the appropriate format based on country. Alternatively, you might have a separate field for country code, and a field beside it for phone number.

Hint: Telephone numbers can be used for more than calling. Say you need to geo-target an audience for an event, and you want to invite leads in a specific metro area rather than all leads from the state, but you don’t have a city value for many records. You could use phone number as a filter to populate your list. But if you haven’t standardized the formatting, you have to get creative with your filter. (Think: numbers that start with +1 212, +1.212, +1-212, (212), +1 (212). You get it.) A stitch in time saves nine, and good data hygiene at the collection point can save you money in the long run.

A Clean Database is a Happy Database

You might dread going to the dentist every six months for a cleaning, but it prevents you from needing a root canal or tooth replacement down the line. Likewise, your favorite part of your job may not be a regular cadence of data-quality checks…or the work that typically follows your audit. But pair those regular cleanups with a fine-tuned data-collection process, and you’ll be happier having fewer data cavities to address!


1 State of Pipeline Marketing 2018
2 The Ultimate Marketing Automation statistics overview
3 Integrate: The Cost of a Bad Lead

Want even more? Check our on-demand webinar, Avoiding the Impact of Dirty Data.

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At Digital Pi, we use technology to connect revenue to marketing efforts. We fuse marketing strategies, processes, data and applications to make marketing technology solutions work for clients' businesses.

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