Several industry studies have estimated the cost and business impact of low data quality. A 2016 IBM study found that poor data cost the U.S. economy $3.1 trillion that year, which is more than the entire GDP of most countries. Poor-quality and inconsistent data continues to be a major problem for marketers.
So, where do we go from here? In my work, I’ve enjoyed helping clients and partners from Fortune 1000 companies who are at the top of their game; and I’ve also enjoyed working with the most disorganized data “closets” you can imagine. And believe me, most organizations—like people—are quite messy (as far as their data goes).
Every organization, including yours, is on a journey toward excellent data management to fuel superior customer experiences. Forrester Research estimates that less than 0.5% of all data is analyzed and used. According to Richard Joyce, Forrester senior analyst, “just a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company.”
How can you make your data more accessible? More organized? I’d like to help you drag those items out of the back of your data “closet,” and put them to good use and monetize them. Will you join the savvy organizations that have become more data-driven and are reaping the rewards? Before you start to organize that “closet” you first need to keep your eye on data quality.
So, what does data quality really mean? And how can you move your organization toward achieving it? According to Forrester Research, data quality exhibits the following three attributes:
- Accurate: Is my data correct?
- Complete: Does my data provide a 360-degree customer view?
- Consistent: Is my data consistent across platforms?
Keep that definition of data quality in mind. Then, just as you would organize your closet by color, season or occasion, begin to organize data residing in your marketing applications and systems (e.g., marketing clouds, DMPs, CRM system, and other places where customer data is stored):
Categorize your data according to: Origin, Use, and Performance (think of the acronym OUP if it helps you remember).
STEP 1: NOTE THE ORIGIN OF YOUR DATA
What is the source of the data? Internal (CRM system, digital analytics tool, etc.) or external (partner organization, data vendor, etc.)? Based on the origin, the method of leveraging the various data assets can change.
Adding a date or an indicator for timing of the data like a “first seen date” or “date of entry” is especially helpful when the data represents a marketing signal. Marketing signals are typically time sensitive and only relevant for a short time. Since origin also relates to use, adding tags indicating origin are especially helpful for complying with people’s expectations and contractual provisions.
Where should you make these notes? Ideally, where the data resides. However, I’ve worked with marketers and been a data geek long enough to know that marketers are not known for their documentation skills and may store information in a variety of spreadsheets that may (or may not) be kept for the long haul. Don’t do this.
Develop a process where you make systematic (consistent, rules-based, clearly defined) notes as close to the data source as possible. As a marketer, you need to become a data geek, too. The game has changed, and you need to change with it.
Using your first-party data is always the preference as history, compliance, and costs are internally controlled with this type of data. Additionally, you are perhaps beginning to discover that there are marketplaces for your data. In other words: there are a myriad of ways for you to monetize your data, but if you don’t know how you sourced the data internally, this will limit what you can do with it next.
“FIRST-PARTY DATA HAS ALWAYS BEEN THE MOST POWERFUL AND PREDICTIVE FOR A BRAND, AND MANY COMPANIES ARE NOW ESTABLISHING PARTNERSHIPS TO COMBINE THEIR UNIQUE DATA ASSETS.” – ACXIOM’S TOP MEGA TRENDS IN DATA-DRIVEN MARKETING, 2018
SECOND- AND THIRD-PARTY DATA
External data can originate from two sources:
- A second-party relationship with a partner organization, or
- A third-party data vendor where you license data about people, including demographics, lifestyle and behavioral information.
Noting the origin of your external data should be done with as much detail as possible.
STEP 2: NOTE THE USES OF YOUR DATA
With GDPR standards going into effect in May 2018, organizations had to take even better care of their data practices and procedures. Acxiom has created, among other strategies, a short guide for data governance and so should you:
“WE ARE USING GDPR AS AN OPPORTUNITY TO STRENGTHEN OUR DATA GOVERNANCE PROGRAM, AND WE RECOMMEND THAT OUR CLIENTS AND PARTNERS DO THE SAME.” – SHEILA COLCLASURE, ACXIOM
For example, you may be wondering: Can we combine data sets? This gets tricky as you weave in the legal guidelines, regulatory requirements, etc., and even more complicated if your data origins are unclear or messy.
SECOND- AND THIRD-PARTY DATA AND MARKETING OPPORTUNITIES
When utilizing second-party data for marketing purposes, the permissions and disclosures should be negotiated up front and can be key to making the arrangement worthwhile. When applying third-party data (demographics, lifestyle and behaviors) those uses are typically broad but can have licensing limits related to the licensing period or sharing limitations. Third party data is especially helpful for filling persona gaps and for expanding reach and descriptive targets for messaging, modeling, segmentation, and measurement.
With both second-party and third-party data origins, ownership is rarely extended, but the data use terms can be broad. Knowing those limitations and applying the relevant flags can be helpful to ensure proper use.
STEP 3: NOTE THE PERFORMANCE OF YOUR DATA
Successful marketers are able to tie performance to the data that is driving the performance. Often marketers want to scrutinize the individual elements (e.g., their construction, accuracy, etc.), but the most important point is “how well it works.” For example, measure the quality of the data based on how well it works, not on how closely it resembles the actual demographics of an individual.
Be honest. Note the good, the bad, and the ugly. That way, ugly won’t be repeated next time.
To determine the return on your data investment, you should understand the specific data components that are driving successful marketing outcomes (e.g., conversions, leads, sales). Tying measurement back to the data origin completes the information cycle.
The most critical point: Make sure the underlying data in a model—or a segmentation system—that may be the key driver receives a proper valuation assignment. I’d suggest tying value to the data that forms a successful model and not just the algorithmic or analytic intelligence used to create it. In other words, the analyst who created a great model may have done exceptional work, but you need to tie the underlying data that enabled the model, as well as the data science that created it, to your notes on data performance.
STEP 4: EXPERIMENT WITH NEW DATA
New devices and emerging technologies are creating more data. Consequently, you should consider new and unique data elements that may be available to your organization (e.g., wearable device data, mobile device location, connected vehicle data, chatbot data, etc.). These elements could represent new categories of data you haven’t yet tried; or they could represent components needed, but not readily available in the data marketplace.
In addition, modeling is one technique to expand targeting opportunities for the newest trends in digital, mobile or social. However, you must find a partner that is modeling to fulfill niche targets or find a provider that can link you to a wide range of data targets for your own in-house analysts to model.
STEP 5: CHOOSE THE RIGHT DATA PROVIDER
Choose the right relationship. Pick a data provider that fits your needs both today and tomorrow. You will want a partner that is advancing technologically, always innovating and moving forward to enable data that is tried and true yet creating data for the latest marketing trends. When you take a partnership approach, you gain a listener and a consultant that provides insight as it works to better understand your marketing plans. The partner approach opens the door for joint initiatives and constructs a pathway for data into your organization as well as a pathway to monetize your data externally. Pick a trusted, valued partner that balances technology and innovative thinking.
STEP 6: MONETIZE YOUR DATA
And finally, explore opportunities to monetize your data. Every company is now a data company. First-party data owners are starting to monetize their data, making it available in new ways.
However, before you can really monetize data, you must organize what you have and prepare for new data assets that emerge continuously. Will your organization’s data “closet” be organized and prepared enough to reap the benefits of data decisioning? When a brand needs niche data to fuel marketing, will it be searchable and discoverable? Will your organization become a data provider as well as a data buyer? Are there opportunities to share data with a partner in a privacy-compliant environment to benefit both companies as well as people? Begin preparing now for a world where data becomes the fuel for increasing your organization’s bottom line.
To learn more about Acxiom’s consumer data, please click here.