Just like a hiker drinking untreated and unfiltered water from a dirty creek, putting raw, untreated and unfiltered data into your marketing automation and CRM will make these systems perform at a suboptimal level. And that’s putting it politely!
Unfortunately, many marketers see dirty data as too costly, too time consuming or, and let’s be honest, too unexciting of a problem to solve. However, we at LeanData, LOVE addressing our customers’ dirty data issues, as we believe that the convergence of cloud technology, readily accessible social channels, and smart process can deliver highly impactful results from data quality investments.
Regardless of the vendors or systems you use to address your dirty data, here are some best practices that every company can do to get started:
Change your Company’s Mentality
Your marketing automation and CRM systems shouldn’t give the people featured on Hoarders a run for their money. The first step is easier than you think: search for “test”, find a completely useless test lead… then press DELETE. Congratulations, you’ve taken the first step in your journey, but more importantly you have now changed the mentality that data should never be deleted or merged. In data quality, the adage “less is more” definitely applies as marketers need to focus on quality over quantity.
A bloated database full of test leads, bots, orphaned contacts, or bounced emails, is not just an annoyance, it is harmful and gives the sales team an excuse to declare that “marketing just sends us junk” (we are sure you have heard that many times). The willingness to delete junk or merge duplicate accounts is a very important shift in mentality. As you find more junk data or duplicates, take the extra couple of seconds to get rid of them – you will be surprised how good it actually feels!
Focus Your Collection/Sanitation Efforts on Key Data Points
Sure, it would be great to know every lead’s height, weight and astrological sign, but chances are there are only a couple of key data points that will actually allow your company to market and sell to each prospect more effectively via better lead management, scoring, segmentation and messaging. Common highly actionable data points include: location, vertical, annual revenue, and title. Once you have picked the 1-2 impactful fields for your company, be fanatical about collecting those data points on every form, list upload and sales calls.
If it’s Important, Standardize
Ever notice how many different ways there are to greet someone: “hi, hello, hey, yo, what’s up, how’s it going?” People have just as many ways to abbreviate their title or describe their industry, or even share the size of their company. Because of this, it really pays to bucket and summarize the myriad of possibilities for important data points. Marketing automation is a great tool to use to this end, as it makes it easy to match your CRM revenue bands with those displayed on your forms, or to automatically bucket titles by common keywords or abbreviations. The big benefit here is that bucketed values allow for easy reporting and segmentation across the fields that matter to you.
Low Hanging Fruit is Fast and Just as Delicious
Even if you are in the unfortunate position of having complex account ownership rules or back pointing integrations that make data cleansing daunting, there are easy wins available. Start with the safest and easiest – 2 records belonging to the same rep, for example. Get those done first before trying to tackle harder merges or issues. Even if these only cover 10% of the dirty data problem…you just fixed 10% of your data quality problem!! You now deserve a glass of ice cold refreshing lemonade (or maybe even a margarita if it’s a Friday afternoon). Not only does this type of approach allow for bite size improvements, but it builds momentum for the harder, trickier merge sets.
Sure, dirty data can seem like a tough issue to address, but with the trend towards data driven marketing, data quality is a critical factor that can make or break your ability to achieve your sales and marketing objectives. So try out these best practices that will make your data healthier or share your own in the comments below.
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