What’s in a name? How to improve marketing data management

If you haven’t been in the trenches of digital campaign execution lately, you may not realize the many interdependent variables and values required to execute a synchronized omnichannel campaign.

However, as a marketing leader, you are clearly aware of all the channels and devices your current and future customers are on. You’re also aware of the importance of having a first-party data strategy to best inform your decisions.

Taken together, the need to get all the necessary data right for your campaigns, content, products, and offers is paramount.

But there’s a surprisingly common oversight within marketing organizations: the creation and adherence to naming standards within campaigns, content, product listings, digital coupons, and more. Some of the challenges inconsistency and lack of data standards create include:

• Unclassified or misclassified data, resulting in wasted time fixing the data and slowing down the time to insights.
• The inability to create thorough and accurate audience segments for dynamic personalization and improved targeting.
• Missing metadata that causes campaign misfires, wrong offers, bad experiences, and poor results.

This is why prioritizing marketing data management matters. A marketing data management program employs a blend of automated and manual data creation rules and processes for marketing teams to provide the data needed to execute and analyze marketing strategies effectively.

The marketing data I’m referring to is different than customer data, which has associated personal identities (sometimes with personally identifiable information, sometimes by device). I’m discussing the type of marketing data that does not directly associate with a customer record but rather all of the assets that marketing creates: campaigns, content, product listings, digital coupons, and so on.

Best practices

As you go about creating or reassessing your own marketing data management program, attention to a few key areas can transform the quality of your marketing data and your marketing performance. These recommended best practices include:

1. Creating a unified and shared marketing taxonomy:

Developing a unified taxonomy (think: naming convention) across your marketing ecosystem is the first key step when creating a collaborative program for your marketing data. Bringing together and consolidating independent taxonomies will ensure marketing data creation is unified. A more detailed explanation of this process is shared here (especially pertinent for Adobe Experience Platform customers).

2. Aligning all teams on the use and governance of the marketing taxonomy:

When you are a large organization spending hundreds of millions of dollars on digital marketing, many teams, both internal and external, are developing campaigns and respective content. Once a unified marketing taxonomy is created, it’s essential that everyone creating marketing assets adheres to it and the corresponding data values within. This requires leadership and change management from the top-down. You may get resistance, especially from outside agencies, from some who don’t want to adapt their current processes, but the need for trustworthy marketing data requires they do so.

3. Implementing a system that manages and automates the standardization of data:

There are several ways to build a system for collaborative marketing data management. Traditional taxonomy or master data management tools don’t necessarily address the specific needs of marketing teams. Alternatives include purpose-built technologies, in-house systems, or no code or general database management. A deeper discussion of these different options can be found here.

Getting marketing data management right

Once organizations gain control over their marketing data management, making use of the marketing taxonomy and naming conventions they’ve developed, the results are significant.

A Fortune 50 manufacturer we worked with, which spends over $2 billion a year in marketing, estimated a savings of $10 million per quarter due to improvements in its naming standard and governance process. The savings comes from both a reduced time in data clean-up and increased speed in delivering business insights. The process started with the company taking disparate taxonomies and merging them into a singular taxonomy (best practice 1).

Another customer, a Forbes Global 2000 company that spends $1.8 billion annually in marketing, showed a 24% reduction of “Unspecified” campaign tracking by aligning and standardizing nomenclature for all the metadata its digital campaigns require. More than 20 teams aligned within a shared system (best practices 2 and 3) to create standardized data for more than 70 digital campaign fields based on their defined naming conventions.

Conclusion

Mandating the creation of quality marketing data consistently has never been more important. With tightened and more scrutinized digital marketing and advertising budgets, along with advances in measurement and attribution, there’s no way around it. Dig into these best practices to continue the process.