This week I want to show you exactly how BlueConic can interact with your existing data layers, or create new data layers based on the data collected/integrated with our (CDP). To summarize the previous two posts: Data layers create a common language between marketing and IT, and enable consistency in data availability across digital marketing platforms. As a connector and data distributor for digital platforms, BlueConic can store information from data layers in user profiles, and make user data available for other platforms via data layers.
Reading and writing
First, reading! Users configure our CDP to listen to the highly relevant information stored in existing data layers (e.g. user/content attributes or campaign lifecycle data). Once stored, the data persists in user profiles indefinitely and can be utilized for real-time interactions and/or shared out to other marketing and data platforms via BlueConic .
When BlueConic listens, you have many options available to you with regard to how to store data. The thought process to work through the options goes something like this:
Do you want a single or multi-value profile property?
When populating a profile property, consider whether the user attribute should contain one value or multiple values. Examples: If tracking products purchased, you’re dealing with multiple values. If tracking demographics, such as gender or profession, you’re dealing with one value. Even when listening for one value, you can accumulate them in a property that contains multiple values. For example, you are likely to want to track all sections from which articles are read, though only one article is read at a time.
Do you want the first occurrence, most recent occurrence, or all occurrences stored in the profile property?
When you’re overwriting (setting) a profile property each time a value is encountered, you’re storing the most recent occurrence. When you use the same setting, with an option to only store if the property is empty, you’re storing the first occurrence. If you’re accumulating (adding) values, you’re getting all of them. Accumulation is a valuable and often overlooked function of storing data in a big data system – you’re not restricted to first or last-touch data, you can store and use it all. You’re really getting all of them. Not 50 of them, not 1024 characters’ worth: all of them.
Do you want to rank all occurrences by allocating points to values based on user behaviors? (e.g. 1 point for a click, 5 points for social sharing, 3 points for cart adds/favorites, etc.)
Interest ranking listeners do just that. It’s typical to keep track of user preferences with regard to topic/section interests, or product/category interests, for example. Armed with this information for known and anonymous users, you can be sure to present offers, messaging, and content that is highly relevant based on long-term and real-time interests.
Do you want to create numeric engagement scores based on values, rather than (or in addition to) storing the values themselves?
That’s what scoring listeners take care of for you! Form targeted engagement scores around these topic/section or product/category examples that are likely to appear in a data layer.
Doing all of this listening really is a matter of pointing and clicking. Here’s an example of selecting a data point using the “visual picker” which also allows you to scrape information from pages and cookies:
1) Profile property values (attributes of your users) that have been collected via BlueConic using the methods described above, or that have been integrated into BlueConic via connections with other platforms (CRM and ESP), or
2) The names of dynamic segments users fall into as they interact with your Web properties (based on customer lifecycle stages, content preference, demographics/technographics, etc.
As an example, I’ve configured our Ensighten connection to write to a BlueConic-specific data layer named bcDataLayer. Here we write key user segments along with a couple of profile properties into this dynamically created data layer:
For your business to have owned, quality first-party data is pretty terrific, but it’s outstanding to be able to make great use of it across all marketing, communication and analytics technologies in an automated fashion! I continue to be amazed by the sheer number of possibilities this capability unleashes for our customers. Countless applications can either read directly from a data layer, or can communicate with the data via tag management solutions. As a former digital analytics professional, I think back to the days of having to export data from multiple systems just for segmentation analyses – never mind moving more data around if you actually wanted to act on it.
Happy Thanksgiving to our US readers; don’t forget to include data layers in the “things you’re grateful for” list!