Utilizing Tag Management for Personalization

With marketing technologies becoming more advanced, it is now easier than ever for businesses to discover and preserve information about a customer's behavior, interests and needs. The next step is for businesses to use that information to deliver higher quality, personalized experiences. However, many organizations still struggle with gaps in the technology infrastructure. 
 
Although there is no argument that the advancements in MarTech have been profound in recent years, most current-generation publishing and e-commerce platforms still lack personalization features. Even when personalization features are offered, they are often rather limited and do not always integrate well with 3rd-party analytics and marketing platforms, from which customer data are often derived. 
 
In addition, creating and managing a multitude of customer experiences can be a cumbersome task. When the marketing need arises, you may want to experiment with different combinations of content, but building and deploying unique, targeted customer experiences using traditional web development methods can be expensive. But don’t be discouraged, a number of experience optimization and testing tools have arisen in the market to simplify the process. One such tool is our Intelligent Solver ™ framework which was launched to provide further clarity in methodology to help organizations own their data and improve personalization efforts.
 
 
Personalization Tools for the Resourceful Marketing Handyman
Tools, such as Adobe Target, Optimizely, and Maxymiser, utilize client-side technologies to dynamically produce alternate versions of content, resulting in much speedier development cycles and cheaper maintenance. There are still difficulties, however, when attempting to create and manage highly personalized experiences since the tools do not always have access to critical customer data used to drive personalization initiatives.
 
No matter what technologies you choose for personalization, you will still likely face some of these common challenges:
 
  • Data integration: getting the right data at the right moment, in order to produce the right experience for customers.
  • Time to market: allow marketing initiatives to become visible to the customers at the earliest opportunity.
  • Operational cost: adding personalization should not severely increase the management cost of existing business functions.
  • Measurement: the effect of personalization need to be continuously monitored and evaluated, to provide a guide for future iterative improvements.
 
With those considerations in mind, you should look for a personalization solution that, at the very least, provides the following capabilities:
 
  1. Build and store customer profiles, which can be enriched by various sources of data.
  2. Manage business rules for customer segmentation (who to target) and personalization (what to deliver).
  3. Deliver personalized experience at the time of customer engagement.
 
Setting up Tag Management for Personalization
If the business has already adopted vendors in the areas of analytics and advertising, one of the first questions would be how to bring together all those intelligence about customers and act on such data during the engagement. One place to solve such problem that is often overlooked - a place where pieces of customer intelligence naturally come together and could be passed in and out of multiple 3rd-party vendors for further digestion - is the tag management system.
 
Usually, the adoption of a tag management system is primarily to reduce IT effort when dealing with deployments of multiple 3rd-party enhancements onto a website. Many of these 3rd-party vendors provide tools for marketing intelligence and analytics and often require customer profile and activity information to be accessible from within the web pages. Tag management systems typically use a client-side structure called the strategic data layer to store such information, so data is managed consistently and could be accessed from multiple places. Anything that should be analyzed about the visitor for marketing purposes can be found in the data layer.
 
Therefore, naturally over time, the strategic data layer  becomes a very rich source of visitor information from all perspectives, making it a very powerful provider of relevant customer intelligence for personalization. 
To take it one step further, extensions of tag management system such as Ensighten's Activate and Tealium's AudienceStream allow preservation of customer profiles from the strategic data layer, which is then stored securely on a cloud server and can be retrieved very easily for future purposes.
 
 
Building Rules with Tag Management
With all the data available, in a typical personalization implementation, the next step is to build business rules to segment the visitors based on their profile, and then build personalization rules to deliver the right content to the right visitor segments. While various methods can be adopted to develop such business rules, one place that business IT teams often overlooked is again within the tag management system itself. 
 
With its ready access to the strategic data layer capable of containing all the information necessary to make segmentation and personalization decisions, a set of client-side business rules is actually much quicker to develop using custom JavaScript tag inside tag management system, compared to traditional server-side development, which is more infrastructure-dependent. At its core, a business rule engine simply needs to take pieces of visitor data (e.g. age group, visit pattern), match it with pre-defined conditions and thresholds (e.g. over 65, frequently visited site section X), then produce a resulting classification (e.g. potential customer for product A) which can then be directed to a personalization module to generate a tailored experience that drives conversion. 
 
Personalization rules (e.g. customer group A should receive marketing message B and C throughout the current month) can be implemented much the same way and any modern desktop or mobile device could evaluate a large number of conditions client-side under negligible time. While there are certainly shortcomings to this approach, several obvious advantages exist: aggregated data that is readily available, no significant infrastructure investment required, separated development cycle allowing frequent deployments to meet marketing timelines, and a very low barrier to development with clear boundaries for easy maintenance. Chances are, if the business has any existing analytics implementation built on tag management, they are not that far from turning it into a capable personalization platform.
 
Since everything is managed under the tag management system, it takes very little effort to send any data related to personalized content to an analytics platform such as Google Analytics Premium for measurement. This offers a simple answer to the measurement challenge about personalization mentioned above. Such measurement data can be extracted from analytics platforms later, using query tools such as Google BigQuery, to feed right back into customer profiles and enrich the intelligence about the visitors.
 
 
A Sweet Spot for Tag Management
With 3rd-party vendors blossoming in the space of marketing intelligence and analytics, many of which are deployed through client-side tags, tag management is finding itself more and more in a center position for managing visitor data that can be useful to personalization. Some technological restrictions, such as the use of 3rd-party cookies by certain vendors to identify visitors, actually make it more difficult to obtain visitor data from outside of the client-side. 
 
Given its inherent portability due to omnipresent client-side technology and the ability for rapid development that could grow and shrink to adapt to future business changes, it is perhaps a good time now for you to consider leveraging tag management systems to kick-start your personalization implementation.
 
 
 
Are you interested in learning more about Intelligent Solver™?
Comment below or contact us at info@stratigent.com
 
 
 
 
 
 

 

 

 

 

 

 

 

 

 

 

By Yin Yu
About the Author:

Yin Yu is a Senior Analyst at Stratigent.

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