Customer behavior and technology on the market will always change. While data may change forms along the way, it’s usage is the one constant in this ever-evolving customer engagement challenge. Ultimately, to maximize flexibility, organizations should adopt data platforms that separate the execution of data from the management of data itself. This creates two primary outcomes;
Primary Outcome 1 – Robust Dataset
A data platform focused on producing the most valuable dataset possible (not focused on building a one-time email campaign or website personalization action)
Every vendor has limited resources to deploy and build the functionality that serves clients. Naturally, this requires making hard decisions about what functionality to prioritize. When a vendor chooses to focus resources on execution, such as website personalization or sending emails, this takes the focus away from the data itself to work on problems that already have established solutions on the market.
The data landscape is complex, with no shortage of challenges lacking a solution on the market. What are some examples of sophisticated data management made possible by focusing on the data itself?
Here are a few examples:
Example 1: Identity Resolution
To check the “identity resolution” feature box, it doesn’t necessarily take very sophisticated technology. For example, some “identity resolution” features out there may merely join two customer records together when a form is filled out with the same email address. Very useful, but basic, with limited scope and flexibility. This may make sense and represent robust enough functionality for, let’s say, a B2B marketing automation platform.
But— what about when a person has more than one email address? What if you don’t have the user’s email address, but instead have a different unique identifier like a user ID number? And when the records are combined, how does that happen? Is everything shoved into one record regardless of the order of events? Or are the events replayed to form an accurate picture of actions over time?
As you can see, there are a lot of considerations when it comes to combining and deduplicating data for identity resolution. The more resources a vendor focuses on native execution, the less focus there is on the managing the data in this manner for identity resolution.
Example 2: Recency Calculations in Real-time
Another area where a focus on the data itself can lead to a more valuable dataset is the flexibility with which data can be combined, augmented and automatically analyzed as it flows into the system. One example of an advanced calculation is recency. Recency refers to questions like “how much has this customer purchased in the last seven days?” or “how many times has this prospect visited over the last 30 days?”
The strategic value of applying recency calculations can significantly increase if the organization has robust identity resolution capabilities integrated into its data supply chain. Without this integrated capability, there will likely be inaccuracies and inconsistencies in the organization’s view of the customer and thus advanced calculations will simply further those inaccuracies.
Recency can be difficult for some systems because it’s a “rolling” calculation applied at a dynamic point in time on data in motion. Legacy and unfocused data platforms aren’t equipped to deal with uncertain timeframes, dynamic datasets and rolling calculations. With advanced data capabilities like this, the value of identity resolution, and marketing technology as a whole, is scaled dramatically.
Example 3: Customizable Tallies to Define Affinities, Favorites, etc
Another advanced data manipulation capability that helps form insights and drive actions is the ability to “count” special events of some kind in real-time. Having the ability to ad-hoc define what the special event is and what “counts” in the calculation allows creative professionals to leverage data to drive action in ways only limited by imagination. By counting special events across all venues, organizations can define customer affinities, favorites, behavior patterns and virtually any insight imaginable based on counting certain, special actions.
Again, the value of this data handling capability is drastically increased with a robust identity resolution solution integrated. A persistent and unified customer record is the foundation against which automated actions can be built, and further calculations performed over time.
With customizable tallies, organizations can track content affinities, loyal customers, brand and product favorites, help topics consumed, etc. All at a foundational level that extends the insight to every technology in the organization’s stack. These tallies could be combined and used cross-platform and cross-device. While individual systems may have similar functionality (for example, a content management system that determine a visitor’s content preference based on tags), the approach isn’t modular and flexible allowing the organization the ability to define the insight with any accessible data and then extend it across the entire stack.
Primary Outcome 2 – Portable Dataset
A portable data asset (benefitting from the focus on the data) that can move from solution to solution as customer behavior, and current technological trends dictate.
When data and execution are maintained separately, it affords maximum flexibility to use legacy data with the latest and greatest tools. That’s because the data isn’t anchored to a native execution functionality, but instead, is collected and managed from inception specifically to be channel and vendor neutral. It’s a modular approach.
When data and native execution are mixed, that data is naturally managed in a way that gravitates towards the execution functionality, as opposed to maintaining neutrality and portability. This encourages a tool-centric approach where value is produced through the use of a tool (it’s value disappearing with the tool itself), instead of defining a dictionary of data and mapping all data to it to produce a valuable and portable data asset.
This becomes a problem because without legacy data (aka, your view of the customer), new tools are merely a more effective way for organizations to highlight their lack of knowledge about a customer (or inability to apply that knowledge to customer experience). This is no laughing matter in a market where customer expectations are sky high, and literally thousands of MarTech solutions are released annually, highlighting the importance of data flexibility.
“Whatever business you’re in, whether you’re a dentist or a bank, you compete with the likes of Uber, Airbnb, Amazon, et al., in terms of experiential standards”
~ Brian Solis, Author, Analyst @ Prophet
