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    Interaction With Big Data In a World Without Cookies

    JimmieBy JimmieOctober 31, 2023
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    Interaction With Big Data In a World Without Cookies
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    The usual way of targeting advertising and analyzing audiences based on cookies is gradually dying out: Apple, Safari, and Firefox are already blocking 3rd-party cookies, and since 2023, the world’s most popular browser, Google Chrome, has been doing this. How does this affect the advertising market and what alternatives exist to cookieless advertising? You will learn about this in this article.

    Table of Contents

    • What Is The Problem With Cookies?
    • What Is The Alternative to Cookies?
    • What to do to work effectively with cookieless advertising?
    • Wrapping It Up

    What Is The Problem With Cookies?

    3rd-party cookies are a source of audience behavior data for most companies. Audience segmentation takes place in three stages: each profile is assigned an anonymized ID, which is compared with cookie and IDFA data; IDs with similar characteristics are combined into segments that are targeted by advertising. About 75% of Internet users are tagged this way. After all the bans come into force, advertisers and agencies will have 5% of this data at their disposal.

    The restrictions also apply to those cookies that the site itself collects: they will be stored for no more than a day. The problem is that we rarely make a purchasing decision instantly: the modern buyer first reads reviews, compares prices, and consults with friends. When a site stores cookies and identifies a customer over a long period, a company can track the customer’s journey: for example, estimate how many contacts it took for them to make a purchase decision. Now such analytics will not be available.

    What Does This Mean For The Market?

    Interaction with the company will become less transparent. Now, no matter how many times the user visits the site after clicking on the ad, you can see the starting point. The data will be reset once a day, and the client will receive a new ID each time. As a result, the share of direct traffic to the site will increase, and it will be more difficult to identify in this group those who were influenced by the advertising campaign. The cost of attracting a client will increase.

    Most advertising services will have less information about the audience, and targeting accuracy will decrease. Accordingly, conversion will decrease and CPA will increase.

    What Is The Alternative to Cookies?

    The industry does not have a single answer to this question. There are four main alternatives to cookies that will allow you to work with data analysis.

    1. Browsers are developing new techniques for segmenting users

    Internet giants, for which advertising is the main source of income, are interested in developing effective and safe ways to segment audiences, even without third-party cookies. Google is developing a Privacy Sandbox program, which is based on the FLoC – Federated Learning of Cohorts system. FLoC directly in the browser analyzes which sites a person visits and, based on interests, assigns him to one of the cohorts – groups of users with the same ID. Advertising will target these cohorts.

    There are three key problems with this system. The first is that per unit of time, a user can only get into one cohort, which will constantly change. This limits targeting options and makes it difficult to create long-running advertising campaigns when we expect the user to encounter advertising multiple times across different channels. The second problem is security and anonymity. The third problem is that the Chrome concept is not supported by any other browser: each market player has their own vision of a way out of the situation. This means that the market will not have a single targeting system for different browsers. It is worth noting that FLoC, which has not proven effective, was replaced by the Topics API, which also did not receive support.

    1. Using your own data

    When registering, clients leave their data and consent to their processing, including for advertising purposes. Information about registered users accumulated in CRM systems becomes the main source of analysis for large advertisers. However, working with really large volumes of data and building look-alike models requires technical capacity and specific expertise.

    1. Contextual targeting

    Contextual targeting ties advertising not to the user profile, but to the content on the site. Its advantage is 100% confidentiality and the ability to show advertising to the user at the moment when he shows interest in a certain topic. Its disadvantage is that it is not suitable for all channels and audiences. For example, if the task is to show advertising based on a home or work location, contextual targeting, like FLoС, does not work. Among the solutions that work on this principle are Apple’s SkadNetwork, Sandbox, etc.

    1. Stable ID

    A stable ID is an identifier linked in an impersonal form to some stable parameter, such as mail, device number, or mobile phone. The latter is considered the most reliable method of identification: one user can have several email accounts for different tasks, consumers often change devices or give them to relatives, and subscribers tend to keep their phone numbers. In addition, linking an ID to a number allows you to collect anonymized information about offline indicators: for example, migrations, home and work locations, and travel habits.

    What to do to work effectively with cookieless advertising?

    1. Develop CRM. Collect as much primary data about clients as possible, and motivate them to register on the site. This way you will know the client better and get a basis for your own analysis or building look-alike models.
    2. Conduct test advertising campaigns. Invest small budgets and experiment with format, message, delivery, and channels, and evaluate their effectiveness. This allows you to reduce CPA due to the fact that you direct the main investments to the most effective channel.
    3. When choosing a contractor, find out what data the service is based on. Give preference to those who build models based on a stable ID and can do data matching with your system – check how well the platform data matches your already verified data. The higher the correlation, the more reliable the platform.
    4. In a number of segments, it is necessary to take into account not only the interests of the audience but also other methods of segmentation: by work and home location or income level. This is most relevant for the real estate, beauty, and HoReCa segments, as well as for medium and small businesses. Find out if the contractor has such targeting capabilities.

    Wrapping It Up

    To work effectively with data without cookies, advertisers will have to rethink their methods and strategies, and find new ways to target and analyze data to achieve their goals. If you are looking for a white-label solution for launching your own RTB marketplace and developing cookieless advertising, we recommend contacting SmartHub.

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