How to Make Your MarTech Database Work for You

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How to Make Your MarTech Database Work for You

As a marketer, you understand the value of leveraging marketing technology (MarTech) to boost the efficacy of your processes, provide more consistent and personalized customer experiences, and drive conversions. You probably stay attuned to the trends surrounding marketing automation and other business imperatives in this digital age. Maybe you’ve even got a marketing technologist in-house (or are one yourself!) Everything I mentioned above can boost your business, and they all rely on one key driver: quality data. For your MarTech solutions to be useful, your database must be on-point. How can you build, clean, and segment your MarTech database to get the best results for your business? Let’s explore.

Eight Steps to Building a MarTech Database

Before we dive into the steps of building a MarTech database, we should first define what constitutes the data itself. Lead data? Absolutely—but that’s not all. To reap all the benefits from your MarTech investment, you should incorporate data from every step in the sales funnel. How did your last campaign perform? What are your key accounts, and what are they most responding to? What accounts are lagging, and how might you be able to turn the tables? Actionable answers to critical questions like these can only be found when you’ve got a variety of data to pull from.

Now, onto the steps:

  1. Scour a variety of sources to gather the data you’ll need to build your database. First, start with your internal data. What information has been shared by your sales and customer support? What information do you have that came from lead digital capture forms, social media, or trade shows? Include vendor and partner information, too. Then, move on to third party sources like government websites or other open data sources.
  2. Because much of your data probably came from a third party, you’ll need to transfer it into your system. Note that more data equals more challenges. If you have a huge number of data sources (from step one) or collect data often, I strongly recommend you consider data automation. You can develop your own code in-house or choose a commercial tool, many of which are more affordable than you think.
  3. Ensure your data is formatted in a consistent manner that is tailored to your destination system. For example, if your destination system only accepts a particular field length and you’ve got data that exceeds it, you need to pare that entry down or risk it becoming unusable. Also, check field types (numeric v. text) and field parts (first name/last name vs. full name).
  4. Cleaning data is straightforward: just fix the errors. Are there typos? Is anything misspelled? Are there factual errors? Do you have duplicate data? Is the data up to date? This step is critical.
  5. There are a few ways you can validate and enrich your freshly cleaned database. First, you can search manually—a costly and time-consuming option, but one that does deliver great results. Second, you can source a commercial data service that has done the legwork for you, checking for things like company size, email, or job title. Third, you can fill in the blanks yourself by looking at incomplete fields and using data automation and solid reference data to infer what’s missing.
  6. Even if your data is clean and enriched, you can still go wrong if you skip the standardization step. For optimal reporting, analytics, and process automation, you need to set rules that ensure your data sets are in alignment. You should be consistent when it comes to things like phone number formats, hyphenations, part number configurations, abbreviations, and more.
  7. Segmenting is the crux of delivering personalized customer experiences, so it’s important to do it well and do it often. Depending on your goals, segment your lists based on industry, geography (firmographic points), buyer persona, job function (demographic points), competitor products, MarTech usage (technographic points), and more. To do this, you can use filters—but there is a caveat. Filters alone can yield false positives (easy to spot) and false negatives (hard to spot) that can be costly, and making this effort each time you have a campaign can lead to inconsistencies and lost productivity. A better way is to practice permanent segmentation—a process by which a data automation tool performs the segmentation and updates all records, saving you time and delivering superior results.
  8. Add Context. No two buyers are the same, and no two companies are the same. Your data needs and MarTech goals will not be identical to every other business in your industry. Yes, there will be some overlap, but don’t hesitate to add context to your MarTech database and segmentation efforts to get the outcome you want. For example, if segmenting by firmographic, demographic, and technographic doesn’t make sense for you, replace them with rules that do.

What’s Next?

Your MarTech is only as good as the data that fuels it. How would you rate the quality and efficacy of your current database? What are your key challenges when it comes to staying on top of your data? I’d love to hear your thoughts.

This article was first published on The Marketing Scope.

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