Is there such a thing as too much data in marketing and sales? Yes, if that data is not in a format you can easily use, but a data management platform can help.
A data management platform (DMP) sorts raw, non-uniform data and converts it into a consistent, predictable, and unified format, a process known as standardization. This standard format enables “collaborative research, large-scale analytics, and sharing of sophisticated tools and methodologies” (OHDSI). With more consistent data, it becomes easier, faster, and more cost-efficient to analyze customer behavior, find trends, target, nurture, and convert. It also helps deliver more personalized customer experiences. In fact, 68% of consumers say personalized experiences are important to them, yet only 15% expect companies to actually deliver on them (Oracle). Here are four steps marketers can take to standardize data.
Start by pinpointing all the sources of data in your business. A data source is a data supply point from which information flows into your database. At most companies, the sales and marketing teams build, mine, and maintain multiple sources, pulling a range of data types to drive leads through the funnel. But other teams may also own data sources, so find as many data stakeholders as possible. Communicate the goals and benefits of data standards to your colleagues.
Learn everything you can about your company’s data sources, because standards are only effective if they account for all data realities. To build valid, enforceable standards (step 2), understand: each kind of data source, how often each source supplies data, which teams own each source, which teams use each source (or want to), and if the data source is first-, second-, or third-party. Keep in mind that it may not be possible to standardize or otherwise alter data from a third-party source.
All companies have specific needs, goals, and sources, so it’s hard to give universal advice. However, there is one fact we should stress to data-driven marketers – try to strike a balance between your need for precise standards and the unavoidable messiness of “big data”.
Big data has three Vs: volume, velocity, and variety. In other words, there's a lot of it, it comes in fast, and in a variety of formats or types. Data standards should be specific and all-encompassing so you leave no data behind in your business systems; they should hold up even if there's a flood of rapid, disparate information. Standards must also be forward-thinking. A startup might not have big data now, but what about 10 years from now when it becomes a Fortune 1000 brand? It shouldn’t have to reinvent the data wheel. Standards should work for the data you have today and your data in the future.
Time to start standardizing. Start externally with the sources that feed your database.
Customers’ online behavior generates data. Three of the most popular forms of engagement are email opens, ad clicks, and form fills. Actions like email opens and ad clicks are likely to enter your database with some level of standardization already applied, courtesy of your marketing automation software. But form fills can be trickier. While forms provide vital details on sales leads, they can wreak havoc on a database with blank text fields.
For example, suppose an academic research brand wants to know the highest level of education of users who download reports. They build a landing page for a new whitepaper and gate the asset with a form, asking visitors for just three pieces of information in free-form text fields: “name”, “email address”, and “education level”. This follows best practices because the form is short, the offer is valuable, and it populates the “education level” data field. Also, free-form text fields are great for qualitative research.
But imagine that 400 people with a master’s degree visit the landing page. How do these users input that fact? Unlike data formats for common details like someone’s name (FirstName LastName) and email address (email@example.com), there's no such standard format to express education level. Users could input the data in many ways. For example:
Master’s with an apostrophe
Masters without an apostrophe
M.A. with two periods
MA with no periods
Master’s in [Specific Field]
Master’s from [Specific School]
These 400 replies were entered in different ways even though they fundamentally mean the same thing. It’s hard for analysts to spot trends if essentially identical data is formatted inconsistently. In this example, the only solution is to manually inspect all responses, which likely number in the thousands, given the problem almost certainly occurred at all other educational levels, too.
This is why marketers often standardize data by using dropdown menus that force format consistency on the way in. Our example business could use a dropdown menu with an option such as “Master’s Degree” or “Graduate School” if it wanted to keep things high-level. Or it could make separate options for “M.A.” and “M.S.” if it wanted more details. It could ask the data team to build a filter on the back end that sorts responses into “Arts” and “Science”—which brings us to the final step.
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What use are great data standards that only apply to new, incoming data? That’s only half the equation. It’s best practice to also apply the same standards to data already collected. Filters are invaluable in this process. Data filtering lets users refine data sets to include only the data they need for a specific task or campaign, and to exclude “data that can be repetitive, irrelevant, or even sensitive” (Techopedia). When standardizing data, don’t delete any data or fields in the existing database but rather keep the information as is and apply filters.
Returning to our previous example, people entered a variety of detail about their degrees. Of the 400 respondents with a master’s degree, some also provided the type—Arts or Science—others included the school, and still others mentioned the specific field of study. The company could ask its data team to build filters that make it possible to pull that data apart as needed.
Standardizing your existing data is a big investment, but the payoff can be huge. Across the company, different teams can interpret data in the same way and everyone will have access to same depth and quality of data for their projects.
Data management platforms are crucial to achieve uniform data. Better input means better output; the more consistent and uniform the information you receive, the faster you can glean insights that lead to conversions and a better bottom line.
Remember: Account for all your data sources, involve a range of stakeholders, standardize external sources (with a keen eye for open-ended text fields), and use filters to organize the data you already own. There’s a wide world of data out there, but with good data standards, you can come out on top.
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Beth Perry is a Senior Content Marketing Manager for Oracle Advertising and CX.