All marketers appreciate that the contacts in their database cannot all be treated the same, and one of the significant boons of marketing automation has been the ability to ‘segment’ prospects and customers that are held in a business’ contact database.
The criteria that B2B marketers may use to segment is dependent upon the data that is collected. Typically, this includes:
- Contact details
- Purchase history
- Marketing automation score or sales stage
Marketing automation segmentation using these data points typically comes in three forms – one-off segmentation, semi-dynamic and dynamic segmentation.
One-off segmentation involve lists that are populated with names only once. For example, if you set up a static segmentation to find all leads who are “SVP Digital” in “Financial Services”, your marketing automation system will find that list. But after a static list is generated, people will never be added to the list again. This is typically the only type of list that people are familiar with before using marketing automation, and is usually used for one-off campaigns – campaigns you don’t run on a regular basis.
Semi-dynamic segmentation is are lists that can add more people to, but not subtract people from, the list. For example, if you set up a semi-dynamic segmentation of “SVP Digital” in “Financial Services”, your marketing automation system will find all the people who meet the criteria and add the new people who meet the same criteria every day. Because semi-dynamic segmentation does not allow for subtractions from the list, if someone changes a job title from SVP to CMO in your database, it will not remove him or her from the list by the same automation that put that person on the list. Removing the person would require another semi-dynamic segmentation.
Dynamic segmentation means that a person can be added and removed from a list based on the same data point changing. For example, a fully dynamic list of prospects who have visited your website in the past 30 days is a list that will grow and shrink every day, based on visits to your website.
Rules-based dynamic segmentation is terrible
Sophisticated marketing teams are able to use marketing automation tools for semi-dynamic and dynamic segmentation, however this hasn’t necessarily led to the results one would expect.
The Annuitas 2017 B2B Enterprise survey of over 100 B2B enterprise marketers from organizations with annual revenues that exceed $250M revealed that only 2.8% of respondents believed demand generation campaigns achieve their goals. Undergirding all of these responses was frustration in the marketing automation technology powering campaigns.
The problems in this process are threefold:
- How segmentation is orchestrated
- The data points used for segmentation
- Understanding what content to send to a segment
The first problem is that no matter how dynamic your list segmentation, they require the use of marketing automation rules or drip logic (“If this X happens then do Y”, “if X does not happen, then do Z”) to orchestrate both the selection of contacts and the message (be it a piece of content, a product, or an email communication) that is sent to them.
The second problem is that the usual data points used by marketing automation to segment contacts (such as a ‘job title’ or marketing automation score) ignore that contacts in your database evolving in their interests, needs and motives. No two “CMO” OR “SVP Digital” + “Financial Services” + “United States” contacts are the same, although they may share the same firmographic profile. Even if you do have the personnel and time to create all the rules necessary for better segmentation, you’re still working from an incomplete view of each contact.
Thirdly, accurate dynamic segmentation doesn’t just rely on accurate rules and contact data, but also an understanding of the right message to send to each contact. Again, this requires marketing teams to go through an tag each piece of content with all of the relevant metadata to describe the content and make it ‘readable’ by your marketing automation rules. Again, this is a Herculean effort on the part of most marketing teams.
So, the reason why segmentation isn’t working for those 97.2% of respondents in Annuitas’ study? Inaccurate segmentation, that isn’t really ‘dynamic’, powered by inefficient rules that cannot predict or adapt to the evolving contexts and interests of contacts in their database.
Dynamic segmentation for omni-channel engagement
Content Intelligence refers to a type of machine learning technology whereby algorithms ‘learn’ from new information and quickly decide what the next best action is for an optimal outcome. Machine learning is well-suited to an environment where CMOs face complex buyer journeys, constantly evolving user profiles and myriad pieces of content that need to be categorized and structured before being served to segments – or even individuals (“segments of one”).
Rather than relying on restrictive rules-based logic, Content Intelligence adapts to the unique signals and interactions of each buyer and automatically decides the next-best-content to send to them. Similarly, an algorithm can learn each individuals’ interests by analyzing their unique content consumption. This is truly dynamic segmentation for across all your digital channels, and something that Idio can power for you and your marketing team.