In the dynamic landscape of digital marketing and advertising, the continuous evolution of technologies and strategies is the only certainty and, thus, models and techniques that were established in the past are now making a comeback.
This is precisely the case with contextual targeting, the targeting model at the heart of contextual advertising, which for decades has been a fundamental element of the marketing mix and which, as we have already discussed in detail in a previous article, is now experiencing a ‘second youth’ thanks to the most recent developments in the technological sphere and, above all, following the growing interest of brands and operators in privacy-compliant solutions.
Contextual targeting has, for many years, been considered as an alternative to behavioural targeting, but is it really necessary to consider these two targeting strategies as alternatives to each other?
Before answering this question, let us try to analyse the main characteristics of each model in order to fully understand the advantage of adopting a hybrid approach.
Contextual targeting: what it is and how it works
Contextual targeting is a targeting strategy that allows you to create vertical target audiences by virtue of a specific content. In contrast to traditional methods that rely on user data and cookies, contextual targeting allows the context and topic of a web page to be assessed in order to determine the most relevant ads to display.
Thus, if a user, for example, views an article on a cooking blog, contextual targeting will allow the latter to display ads related to related products or services, let’s imagine utensils or appliances, rather than cooking classes.
Contextual targeting has several methods and can be easily adapted to different aspects of content, however, three main models are identified for convenience.
- Contextual targeting by keywords: this involves targeting ads on the basis of particular keywords in the content or search queries.
- Contextual category targeting: In this case, the display of ads is based on the category of reference of the content itself. Advertisers define subject categories relevant to their products and services and the ads are then displayed on pages that fall within the chosen categories.
- Semantic contextual targeting: this is the most sophisticated contextual targeting model that uses natural language processing (NLP) algorithms to understand the context and meaning of words and phrases.
To simplify, a contextual targeting process thus involves several steps. The first of these is, without a doubt, the scanning of the page contents, which allows the system to analyse in detail the texts and images that make up the so-called context. The main keywords are thus identified, enabling the content to be identified and subsequently associated with categories and topics.
Once the content has been associated with the categories, the ads are matched to the categories themselves and then the former are placed within the most relevant pages.
Behavioral targeting: the importance of knowing your audience in depth
Collecting data and information on users and customers in company properties is the first step for an effective behavioral targeting strategy, which, as the name itself reminds us, involves profiling and segmenting its audience starting from data regarding browsing and purchasing behavior.
Unlike demographic targeting, which is based on static user characteristics such as age or gender, behavioral targeting focuses on users’ dynamic actions and behaviors, allowing brands and businesses to tailor messages and offers based on individual behavior.
From the pages visited, to the items added to the cart or purchased, from the banners or links clicked to the searches performed: every user activity can be tracked and every information collected can be considered relevant in order to create complete customer views for each user and to propose sponsored content and ads relevant to the interests and needs shown. If you then add the data collected in the physical store to the data collected online, it is possible to have an even more complete picture.
From the personalization of offers and content to the increase in engagement and loyalty: the advantages of behavioral targeting activities concern not only the advertising area but, more generally, allow you to optimize the personalization strategies of the customer experience tout court.
Contextual and behavioral targeting: why choose a hybrid approach
Faced with the question of whether to implement behavioral rather than contextual targeting activities, the best way to go is to try both strategies and evaluate the results for a more prudent choice.
Contextual and behavioral targeting are not two sides of the same coin, but rather outline two targeting approaches that can coexist: on the one hand, contextual targeting allows you to reach the user with ads relevant to the current browsing context, while behavioral targeting allows you to leverage “more historicized” information such as page, visit, searches performed, purchased products.
For an effective strategy that can actually allow the creation of high-performance audiences, the advice is to adopt both approaches.
Using contextual targeting and behavioral targeting at the same time can help create a more comprehensive approach, reaching consumers in different ways and at different points in their buying journey.
Blendee allows you to put in place effective behavioral and contextual targeting strategies: in addition to the collection and normalization of data and information at the level of single customer view and the consequent profiling and advanced segmentation activities, Blendee’s Marketing Operating System allows the creation of vertical audiences starting from the definition of a set of contexts and keywords to describe the content and interests of users.
Creation of vertical audiences, but not only: the “AD Server” engine allows you to manage and optimize ADS campaigns of all kinds, delivering engaging and personalized cross-platform content. All this is possible thanks to the use of artificial intelligence and machine learning algorithms that make it possible to enhance content, classifying it even more effectively in order to deliver increasingly relevant advertising messages.
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