The use of dynamic templates enables marketers to completely rethink marketing concepts and measures, but it goes deeper than that. Let's take a look into what dynamic templates are all about, how they work and what new possibilities they open up.

In classical database publishing, a vast array of templates are often used to represent all of the necessary graphic and design applications. Once the templates have been created, the user typically makes the decision on which template to use.

Dynamic templates are completely different. With the implementation of dynamic templates, the task to comprehensively automate the entire creation process can become a reality. Precisely formulated, the goal is to work with a single dynamic template that decides on the basis of criteria, data, statistical methods, and more, how information is placed (for example, on a catalog page).

In practice, various templates are used. For example, a sample page created in Adobe® InDesign® can serve as a template. Various parameters are stored and defined in the sample document, such as a left / right page or how the header and footer should be designed. Supplementary rules define further information relevant to the design.

Page templates specify how elements are placed within the defined parameters or within a certain area of the page. Its elements are overall contents or product contents. A page template specifies, for example, whether the page is structured in one or more columns and defines spacing and other relevant parameters. Although a page template is usually defined with the priint:comet plug-in in InDesign, it can also be created dynamically via an API.

Content templates simply define how product information is displayed on a page.

An example that shows how our customer, Winkhaus, arranges the product information

In order to work with dynamic templates and prepare the product data for the print process, it is typically necessary to re-evaluate and make revisions to existing data structures and processes. As in other areas, such as AI-driven content generation, high-quality data is the crucial key to success.

The data preparation using the example of Winkhaus illustrates this:

We distinguish four types of data: navigation elements, information, identifying information and price information in the broadest sense.

• Navigation
  Navigation elements are a type of grouping under which further and more in-depth information can be found. Navigation elements are typically chapters, subchapters, subheadings or similar.
  
  
• Information
  Additional information provides more details or further orientation to the superordinate navigation element or to products.
  
  
• Identification
  Identifying information gives the reader deeper, more detailed information.
  
  The difference between navigation and identification is as follows: Navigation guides the reader through the publication so that they reach the desired location. Identification means that the reader then decides whether they they are satisfied with the information or want to dive into that detail. Similar to navigation, there is also extended information in identification. This can be certain textual attributes, explanatory images, etc.
  
  This concept of distinguishing between information and identifying information is also shown in the table above: Classically, the item number and the item description are identifying information. The other contents are supplementary information and sometimes price information.
  
  
• Price information
  Price information can be a single price, tiered prices, a discount code, a discount group, quantity information, packaging units, etc.

To dynamically generate catalogs and other publications at this extremely high level, the data quality we need for automation is crucial. One of the essential questions here is to clarify whether content represents information, identifying information or price information. Furthermore, the content is classified according to its media type, i.e. whether it is text, an image, an audio file and beyond.

Data workflow

The data stream we need for dynamic generation is simple and also relatively easy to identify in preparing the PIM or ERP system. There are different phases of the data stream:

• Scheduling
  The upstream decision-making process defines which content will be published in the first place. This planning can be done in the PIM system or in the priint:planner of the priint:suite.
  
  
• Data stream (optional hierarchy)
  At this stage, the question is whether the data stream already has a hierarchy. This can come from the PIM system or the hierarchy is defined via rules. This rule-based approach, i.e. that only one article stream comes from the PIM, is called faceting and is part of the dynamic templates.
  
  
• Creating navigation hierarchies, e.g. faceting (optional)
  Further navigation hierarchies, such as subgroups etc., can also come from the PIM or be mapped via faceting.
  
  
• Use of a dynamic template
  A single dynamic template can be used to display completely different pages or tables. The system decides independently how the presentation should look and, in the case of tables, is able to process any number of attributes per article (see the following paragraph).

Tables often play a decisive role in communication. In terms of design, the issues of clarity and space optimization are crucial. The idea behind dynamically generated tables is that the data or the classification of the data is used to automatically decide how information is to be presented.

There are countless possibilities to display articles and price information in a table. Typically, these two table forms are used:

Regular item table

Regular tables have a certain number of columns in which attributes are displayed with their different values. However, if ten or more attributes have to be displayed, this often leads to problems in practice. In this example, it is no longer possible to display this large number of columns at all. The solution lies in the concept of grouped rows. In intermediate rows, corresponding attribute values are displayed in summary form- usually at the end of the table are the columns with the price and other identifying information. By doing this, a large number of attributes can be displayed. The more attributes that have to be displayed, the more intermediate lines there are and the longer the table becomes. The presentation is neither space-optimized or clear to the user. If there are too many attributes, it is advisable to create pivot tables.

Pivot tables

To solve the problems described above, many designers use pivot tables, also called matrix tables. Pivot tables make it possible to summarize data and present the most important properties of the data in a pointed way. Pivot tables provide a tremendous gain in clarity.

In the actual customer project, the data is only fed into the process, so to speak. The tables are generated automatically with one of the two table types.

The approach we implement in today's projects is for algorithms decide how a table is displayed- whether a classic or a pivot table is constructed. Algorithms also decide how the individual attributes or information are displayed based on predefined rules. Furthermore, they decide how tables are designed to optimize space. Controlled parameters allow the user to influence how the table is displayed. This means that in the ideal situation you only have one dynamic template for pivot tables and everything else is determined by the algorithms.

What is very exciting about the topics of automatic table display is the topic of space compression: concrete customer examples have shown us that the algorithm achieves significantly better results than when a human regulates and controls it via the PIM system.

Product pages are generated fully automatically. For example, perhaps the user also saves 10% of the internal effort, 30% external effort and the production time is reduced by 40%. But is that all? Of course not!

The use of dynamic templates enables companies to take into account the requirements that have long been commonplace in the digital world. Namely, to publish contextually, at any time and on demand.

• Contextual
  With dynamic templates, our customers generate publications fully automatically in any context. Context means in every language, for all countries, target groups, areas of application, etc. For example, if a company wants to create a special catalog for just one single application, it is generated at the push of a button. Preparation times are no longer necessary.
  
  
• Anytime
  The publication date of catalogs is often linked to events such as trade fairs or other important publication dates, with editorial processes typically geared toward these dates. Fully automated generation breaks up typical production cycles and creates production times according to demand.
  
  
• On Demand
  Generation takes place on demand, at the moment it is needed. Whether a catalog is actually printed out or generated via the website is irrelevant.