Last September we published an article that discussed why, over the last few years, marketers and the MarTech industry have gradually come to favor customizable marketing technology stacks.
The article explains how marketers are no longer seeking all-inclusive, out-of-box marketing systems to simplify their jobs; instead, they’re looking for innovative ways to leverage multiple systems to their advantage.
Unsurprisingly, this emerging mindset of customization is also having great impact on the ways in which marketers identify, coordinate and use the data that fuels these finely tuned tech stacks.
Rather than simply pumping prospect data in at the top of the funnel and then shedding leads through the subsequent waterfall stages, demand marketers are creating new data-injection points throughout the various funnel stages to append information, enhance target profiles and continually optimize the ways they interact with specific accounts and individual prospects.
At the same time, media companies, whose core business used to be slanging leads, have adjusted their models accordingly. They’re pivoting to full-funnel data providers, reimagining the ways they can utilize and monetize their substantial data stores to accommodate growing marketer demand for various types of info.
All of this means the market for B2B marketing data has become just as dynamic and specialized as the MarTech landscape.
Demand marketing’s growing complexity means more specific data requirements
Demand marketing is evolving quite rapidly. As businesses grow, pipeline requirements do as well, and B2B marketers must continually create and refine new engagement strategies (and tactics) to generate the business opportunities sales teams depend on and the C-suite expects. And of course, the development of sophisticated engagement strategies depends on specialized data.
Take account-based marketing (ABM) for example; no longer as simple as creating a list of named accounts for sales reps to target, ABM strategies rely on a complex ecosystem of data providers:
- Predictive data (itself aggregated from numerous data sources) – allows you to create models and initial account lists
- IP-tracking data – highlights which accounts are visiting your website and showing buy signals so you can prioritize lists accordingly
- Contact data – intelligence on and contact info for individual decision-makers at targeted accounts (often acquired via numerous media partners)
- Surging interest data – further highlights which accounts and contacts are showing buy signals by downloading relevant content
- Appended data – provides further intelligence on accounts and contacts
- Correction data – validates and updates account and contact data to ensure database integrity
Some of these types of data can be obtained from a single vendor, such as predictive analytics providers. Others, such as account decision-maker data, often depends on relationships with more than 20 different media partners, each with their own specialized audiences.
Every marketing org has a unique and very dynamic audience. The needs of these audiences are constantly changing, and marketing teams require customizable data ecosystems (that they can quickly adjust) to provide the specialized information needed to continually engage the right targets with the right messages. This is the only way marketers can match the dynamism of their audiences.
The transformation of B2B media companies to full-funnel data providers
An indication of this growing marketer need for customizable data ecosystems is the way in which media companies have been evolving their own offerings and business models. In fact, Integrate’s own Scott Vaughan recently wrote a Marketing Land article on this topic. In the article, he states:
“A larger B2B-focused media company may generate millions of engagements and leads annually, all of which remain in the company’s database long after being sent to the marketer. Now on top of the static lead/contact info itself, think of all the added insights aggregated through the analysis of this data:-surging contextual content that illustrates interest in specific topics among individual leads (e.g., the number of white paper downloads in a set period of time);
-actions that signal info consumption preferences;
-industry trends (e.g., sudden bump in, say, cloud computing interest among enterprise companies); and
-changes in account info (e.g., human capital jumps between companies).
All metadata such as this has value… a great deal of value. And the smart media companies are rightly set on monetizing it.”
Scott goes on to argue that this media company transformation to full-funnel data providers will become pivotal in B2B marketing’s ascent toward demand orchestration.
Steps to develop your customizable data source ecosystem
- Take inventory of the data sources you’re currently using and data vendors you’re working with. Creating a table to organize these relationships often helps.
- Talk to your current media partners. Ask them what kinds of data solutions are in the works (or are already available). As mentioned above, there’s a lot going on in the B2B media industry right now, and you’ll likely be surprised by what you find.
- Reach out to fellow marketers at other organizations and inquire about what types of data they’re using, how they’re using it and how the results have been so far. Conferences are obviously a great place to have these discussions, but don’t hesitate to reach out on social media or ask questions in the comments sections of blogs and articles – marketers love to share their learnings and only rarely treat them like secrets.
- List your marketing processes to identify places where your recent data learnings may be of use.
- Aggregate all the info you acquired during the previous four steps and create a visual roadmap of which types of data you need, where you plan to use it and how you might procure it (i.e., specific vendors, various Integrations, etc.).
- Once you’ve identified the data types and sources that make sense for the development of your unique ecosystem, start testing. It’s usually a good idea to test incrementally, but some types of data go hand-in-hand, especially when developing an ABM program. Use your best judgement, but also feel free to reach out to us if you have any questions.