When it comes to customer analysis, most people will talk about how analytics tools can be employed to gain better insights or how to get more tests in a specified timeframe from a MVT tool. At the end of the day, what you need is sufficient data about your targets that can help you identify universal customer behaviors across various channels or personalize their user experiences across several channels.
Today, marketing professionals use a variety of tools to capture data about their customers. All of these tools have different structures and terminologies. In other words, each tool has its own method of managing customer data. Thus, the customer data captured is usually fragmented, unorganized, and redundant. Marketing professionals are unable to perform an accurate analysis due to this data inconsistency.
Marketing professionals now realize that they need to organize their data in an efficient manner if they want to guarantee its accuracy and integrity. This is where the data layer comes into prominence. If you want to achieve your objectives of accurate analytics across channels or in-depth user personalization, creating the right data layer is a necessity. The release of the Customer Experience Digital Data Layer (CEDDL) specification is perhaps the best indicator of the rising criticalness of the data layer.
To put it in very simple words, the data layer is a universal method to organize and structure data according to a standard format regardless of the source of the data. It can contain vast and varied data such as mobile application usage, online behavioral data, and e-commerce transactions. It is called a layer as it is a logical component in the technology stack that provides the interactive experience on websites and mobile apps.
The data layer has a technical and non-technical aspect. While the layer represents business objectives and requirements, its existence is influenced by web technologies. Therefore, it is critical to have both developers and marketers on board when defining the data layer. With inputs from all parties, business requirements can then be translated into functional actions. Here are some of the things you could define in your data layer:
Page attributes Capture data about the web pages frequented by your visitors such as Page Title, Page URL, and Page Category.
Visitor Information: Capture anonymous data about the visitor such as demographic data, social information, historical information, etc. This is ideal for websites that have a logged in experience.
Conversion Pages: Capture conversion statistics such as Conversion Value, Product SKUs, Product Categories, Total Purchase Value, Payment Type, etc.
The amount of data that will be used on each page depends on your tracking requirement. Once you have defined your data layer, you will need to implement it on your web page in a specific format, which is documented by Google. You can do this by creating a Universal Data Object.
Consider a situation where a customer visits and browses your site. They see offers that complement a purchase they made recently. This is an excellent example of analytics dictating the flow of customer personalization and data relevancy.
If you have a structured dataset that can identify and link multiple identities of the same customer across different devices and channels, then you can be certain of delighting your customer. If you do not have this well-defined dataset, then you would be incapable of providing your customer an integrated holistic experience. As a result, your customer would have an isolated experience that does not make optimum use of the data they have already provided to you.
The benefits of having a data layer should be, thus, quite obvious:
In addition to the above, a well-defined data layer will enable you to experience the following benefits:
Shared standard — As there now exists a shared standard, marketing professionals do not have to worry about using the right technology.
Increase efficiency — The data layer helps you to organize your dataset and eliminate redundancy. This will have a positive impact on your website performance metrics like page load, etc.
The Universal Data Layer
For those familiar with a tag management system like Tealium or Google Tag Manager (GTM), you may be quite aware of the data layer and its importance. However, a drawback of these tag management systems is that each one has its own data layer convention. This brings along a different set of challenges such as design complexity and maintenance issues. The existence of a universal standard would eliminate all of these issues.
The first effort to create a standard data layer came from the Customer Experience Digital Data Layer (CEDDL) community. The advantages of a standard data layer are:
Savings in terms of time and money
No dependence on vendor for data layer implementation
While the CEDDL is still in its early stages, it promises to revolutionize the world of data layers.
Marketing professionals use a variety of digital channels to track customer actions, identify customer behavior and measure customer value. Naturally, they expect a single platform that can amass their data in a standard structure. The data layer is the logical solution that frees you from depending upon silos of customer data. It represents the ideal of omnichannel marketing that every digital marketer worth his/her salt strives to achieve. A well-constructed data layer will take you a step closer to in-depth user personalization and analytics.