By Brooke Larson, Strategic Account Manager
Lately, when meeting with new clients, one of the first topics that we cover is a discussion around the data layer. And usually, with less technical stakeholders, this is met with, “What’s a data layer and why should I care?” Most people want to talk about how to get better insights from their analytics tools or how to generate more tests on a quarterly basis from their A/B or MVT tool.
So, why are we spending time discussing the data layer?
If you have any hope of effectively personalizing the user experience across channels (e.g., targeting the right offers, presenting the right messaging) or if you want to understand customer behavior across multiple online channels, then you need to make sure that visitor information is collected consistently.
Today, most companies have unorganized, fragmented and redundant data in tool-specific language; each vendor uses different terminology for the visitor data that they are collecting. Companies now have a massive amount of data, generated from lots of digital touch points, but it sits in tool-specific siloes that can cut off your ability to make effective decisions.
Put another way, each tool tends to have its own way of handling visitor information. This inconsistency can make analysis and optimization difficult beyond what the siloed tools allow. You have all this data about your visitors, but limits on how to effectively use it beyond the siloes.
Hence, the rise of the data layer. In fact, it has become such an important topic that the W3C has published standards on what they call the Customer Experience Digital Data Layer 1.0.
The reason that it is critical now is mostly a function of necessity. Clearly if you want to achieve personalization or more accurate analytics across channels, getting your data layer right is an essential matter for any serious digital marketing professional. With so many data interactions and downstream data needs, companies are realizing that they have to organize their data efficiently and effectively to ensure that they can rely on all subsequent data.
For example, when a customer visits your site and can see offers for products that compliment a recent purchase or feature an offer for something they had looked at previously on their mobile phone, these are examples of your analytics driving personalization and relevancy. If you have a structured, defined data-set that can link that customer identity across devices and interactions, then you can delight your customer. If you didn’t have a unified data set that was consistent and shared across devices and tools, this customer would not have an integrated holistic experience. Instead, your customer would be back to a siloed experience that doesn’t leverage the data that they have shared with you already.
The data layer, at its simplest sense, is a way to standardize the data you collect from your online and mobile channels. This can be done by creating a Universal Data Object (UDO) that can be shared and repurposed over and over again. Even if you don’t plan on undertaking personalization across channels or delving into customer behavior across channels, there are a couple of other benefits to the data layer.
- One hurdle is mitigated by the existence of shared standards. So you don’t have to worry if you are betting on the right technology (think VHS and Betamax or Blue Ray and DVD), there is an agreed upon standard now for the data layer, this is the W3c.– http://www.w3.org/standards/
- Next, by organizing your data set and removing redundancy, you increase efficiency and this should have positive effects on page load and other website performance metrics.
In the end, the data layer is the natural evolution of using digital channels to track and measure customer behavior. It frees you from relying on siloes of visitor data. It’s a major essential step in effectively moving toward personalization, optimization, and deeper analytics.
