In a recent press release, Anthony Chavez, VP of Privacy Sandbox, announced that they will be expanding their testing of the Privacy Sandbox. This means that the development of the Privacy Sandbox will take longer than expected, until Q3 2023. By that time, Chavez expects the Privacy Sandbox APIs to be launched and widely available in Chrome.

As developers begin to adopt the Privacy Sandbox API, Google will start phasing out third-party cookies in the second half of 2024. This delay doesn't come as a surprise, as the original plan was to phase out third-party cookies this year.

The Privacy Sandbox is designed to protect user privacy while supporting the growth of businesses on the open web. One of the most well-known outputs of the Sandbox is the Topics API, which replaces FLoC (Federate Learning of Cohorts) and serves as a replacement for interest targeting.

But what are third-party cookies and why should you be aware of them when browsing the web?

What are cookies?

A cookie is a small piece of data (under 4 kb) stored in the browser for a website in the form of a key-value pair. It’s basically text (string) in a certain format understandable by the browser: "User_id": "12345". It is temporary info that a browser stores for further requests to that website server. Concretely: It’s a web-based technology (mobile applications are not depending on cookies) mainly meant to identify a user, or more specifically a browser.

On login, the server stores the string and everytime the browser makes a request to the server it sends the cookie back, so the server knows who that user is. For example, when you login into a website, the cookie is used to re-identify when that user comes back in a new session, set the correct language for the identified user and pre-load some default information.

Cookies can be divided into two categories: first-party cookies, which are only available to the website owner, and third-party cookies, which originate from other websites and are typically used for advertising. First-party cookies are used to improve the user experience on a website, while third-party cookies are used for a variety of purposes, including advertising.

In general, third-party cookies are the issue for the privacy-concerned. The reason is that these are stored under a different domain than you are currently visiting. Cross-domain tracking is the reason you’re seeing targeted ads of the website you visited, or the plane you haven’t booked yet. Third-party cookies are like a trail of crumbs. People generally don’t realize they are leaving information behind for other websites to pick up.

Third-party cookies are one of the reasons why General Data Protection Regulation (GDPR) was introduced, the main reason why Safari released Intelligent Tracking Prevention, and why Google started building their Privacy Sandbox API.

Is there any use to third-party cookies?

To a lot of web users, third-party cookies are nothing more than a liability. For them, these cookies are nothing more than a revenue stream for advertisers and a way for advertising technology to gather data for intrusive purposes.

But is there any use to third-party cookies besides advertising? Yes, in fact there is. Many plugins, like live chat services or social media plugins, are in fact using cookies. These cookies are used to activate the application by the former and to allow users to sign in or share the website content by the latter.

However, the real use of these cookies is personalized advertising. Because they can keep track of your individual interests, you’re way more likely to see an advertisement that resonates to your behaviour. The expected outcome is that users are more likely to engage with these ads. After all, if you are forced to see the ads, it's better if they are related to your interests.

It is a fact that third-party cookies are most useful for marketing purposes, and give little to no added value to the end-user. Only a very little amount of websites (or part of those websites) will break by disabling third-party cookies.

Third-party cookies’ days are numbered. It is only a matter of time until regulators and consumers force the industry to remove them, and many advertisers will have to shift to alternatives. This is not necessarily a bad thing, but today the industry is far off from being ready for making the switch to alternative advertising, like cohorts and contextual advertising. The decision of Google to postpone the release of the Privacy Sandbox API is one of the many signals we’re getting that underlines that.

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For most of us, LinkedIn is seen as the most powerful network in terms of recruitment. With nearly 774 million users in more than 200 countries in 2021, LinkedIn has indeed become the reference for professional social networks.

But the platform is now also proving to be a powerful tool for online advertising. Like Facebook, Instagram and Google, one of LinkedIn's main revenue sources is advertising. Whether you're a marketer looking to increase a company's online visibility, or a recruiter looking for new profiles, the possibilities for advertising on LinkedIn are many.

Indeed, the platform is improving day by day by offering new ad formats, new features but also personalized targeting. Like Facebook and Google, audience targeting is indeed one of the keys to successful advertising campaigns. It is indeed necessary to broadcast the right message, at the right time and especially to the right people! So let's review the different targeting options available.

As on other platforms, LinkedIn offers basic targeting options, i.e. create an audience based on :

• Geographic location (show ads to people in a certain area)
• Demographics such as age and gender
• Specific audience attributes related to company size, industry, education, degree, current position, skills, etc.
• Customized segments (Matched Audiences) such as people who have already interacted on your site, visited your LinkedIn page, etc. (remarketing audiences)

These different attributes already allow you to refine your audience to keep only the people most likely to interact with your ads. But there are still other features on LinkedIn to optimize your targeting as much as possible, and thus generate better performance for your campaign:

• LinkedIn Audience Network: which allows ads to be served on an additional network to LinkedIn to increase the reach of the campaign
• Interest Audience: it is possible to target people who have shown an interest in a certain topic, based on their searches on LinkedIn, their engagement with a page, etc.
• Group targeting: it is also possible to target all people who are part of the same group on LinkedIn (sports, finance, movement, etc)
• Creation of Lookalike audiences to further expand the target while remaining relevant

As with any platform, remember that even when you have found the best targeting for your LinkedIn campaigns, it is important to regularly optimize these audiences! Here are some best practices: 

• Perform A/B tests with different targeting criteria to compare audiences
• Adapt the content of the ads (texts, images, CTAs, etc.) according to the best performing audiences
• Compare your audiences to LinkedIn audience models and adapt if necessary (many insights available on pre-designed models)
• Test the Audience Expansion mode, which allows you to reach an additional target similar to the original audience

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Introduction

Nowadays in digital marketing we are used to advertise for an existing market. Our tactics are easier with all the features flourishing on the advertising platform.

But what about launching an advertising campaign about a product which is new and doesn't have a market yet ? For the ones that read the international bestseller “ Blue Ocean Strategy“ from Chan Kim & Renée Mauborgne, you understood I am referring to the “blue ocean”. They created the terms red and blue oceans to differentiate the market universe.

In this article, we will first summarise what’s the difference between red and blue oceans. Then, we will discover how we can advertise digitally for those non-existent markets. 

Red Ocean

Red Ocean represents the majority of advertisers currently who are competing in existing and known markets. There is a high competition and advertisers try to outperform their rivals to claim a bigger market share. Usually focussing on the same advantages and claiming to be better than the competition. This intense competition gave the bloody colour to this concept of red ocean.

Given this saturation, growing is a synonym of stealing market share from competitors. The profits are usually lower due to this tension.

Blue Ocean

Blue ocean is referring to the unknown & unexplored market space.

In this context, the competition is irrelevant because the market rules still need to be set.

This lets a huge playground in terms of potential for growth. Exactly like an ocean which is vast. Don’t fall directly into this desire to plunge into this untapped market: you need to be aware that demand fully needs to be created. There comes the real challenge to create innovative value.

An example is Apple who created their blue ocean with a high quality product clearly differentiated from competition. The desire was for the brand itself and not the product anymore: a need was created.

I believe now you wonder how to create innovative value and reach this blue ocean. We will unfortunately let you without an answer on this question. But we highly recommend you to consult the six paths framework created by Chan Kim and Renee Mauborgne, authors of the bestselling 2005 book Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant.

Now let’s rather focus on our real expertise …

What about digital advertising?

While for existing markets (red oceans) you have various advertising solutions totally mature like the in-market audience for instance. Here to advertise in a blue ocean, we are obliged to be inventive with our approach and tactics.

The first step is to get immersed in the product's understanding. The USP and needs it is replying to has to be crystal clear within the briefing.

Then you can move to the audience and more specifically on how you can reach it. While a socio demo audience approach is now more than easy, for the blue ocean you need to identify the mindset of the potential customer. But yes a mindset is way more subjective and not always tangible to reach digitally.

To implement this strategy, we advise to use a tactic that is luckily working in all mindsets identified. The tactic is pretty simple: determine apps or research that this mindset implies.

Let’s take an example to better understand. Our mindset is “flexibility, autonomy & young” (with “young” I am not referring to a demographic element but a mindset). Those characteristics are not that tangible to target online via an audience or specific inventory. But we could easily link to specific apps:

• Flexibility: Frichti, Too Good To go, Gorillas, UberEats to order easily and fastly your meal at home
• Autonomy: Tricount ( help you to split the shared spend among several people ); EasyPark
• Young: Tinder, Yuka ( to be aware of real ingredients within your product), Netflix

So in this example you would be interested by the audience that are looking for those apps or searching for search terms similar to the needs resolved by these apps.

Once you identified the app & researches, we need to concretely target this audience.

We are continuing the example with Google ads where you can create a custom intent audience. This audience is based on keywords researched the last 7 days on Google. There is an equivalent to capture your research of Apps within Google Play. By creating this audience you succeed in reaching this mindset. This audience can be used in the full Google stack advertising so also into Google Display Network and YouTube for instance.

Yes you could have also bought a keyword such as “Netflix” within your Google ads account. But what’s the interest ? You would pay an extremely high CPC because your ads are not relevant for this research. Moreover you know that someone searching for “Netflix” is determined to go on Netflix at this instant. While with our solution you are capturing the mindset.
See below how this is concretely working:

As explained in the scheme,

1. Customers make a research on Google ( google search or Google play) within this last 7 days
2. You target this audience based on this mindset within YouTube. So you are not directly replying with ads into Google Play or Google search results. This makes this approach even stronger.
3. Thanks to your YouTube presence, you are in line with your audience mindset and have a higher chance to spark the curiosity to visit your website.
4. Once this potential audience is coming on your website, they can directly interact with your brand and are captured into your remarketing audience.

Conclusion

Our digital advertising possibilities are extremely mature. To target an unknown market you can easily tweak those initial options in an inventive way to reach the right audience.
In a nutshell; continue to use the current targeting options but adapt it to your real context.

Finally for a blue ocean strategy, focus rather on the mindset than our classical socio demo targeting.

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In your Google ads SEA campaigns, you have all your keywords that you are bidding on. Depending on the match type you give them, your ad may appear when there is a match with the user’s search terms he is typing. In Google Ads you have 3 types of keywords: “exact match”, “phrase match” & “broad match”. In this article we will focus on the latter as its way of functioning has been improved by Google algorithms. You will get the practical guidelines to be able to implement your own A/B test on Broad Match.

Before starting, you must note that Broad match is the default match type that is assigned to your keywords. On top of that, the following definition only applies to positive keyword targeting (Negative broad match works in a different way):

Broad Match: “Ads may show on searches that are related to your keyword, which can include searches that don’t contain the keyword terms.” (Google Definition).

In other words, your ad will appear on the searches that are relevant to your product/service you are selling. In order to do so, Google has improved the broad match type by taking into account other signals such as the landing page, the user location and the other keywords you are bidding on in the same ad group.

You might be more used to the “phrase match” type as you feel more in control on which searches your ad will appear. However, you might lose opportunities for some searches. Indeed, searches on the search engine are always unique as the signals sent by the users differ from each other. On top of that, new queries are popping up every day.

By using broad match in your account, you will benefit from:

• Efficiency
• Time saving
• Reach & coverage expanding
• Relevance
• Increase your campaign optimisation score

If you are still reluctant to convert your keywords in broad match, you have the right to be as this might not be convenient for all sectors and campaign types. Indeed, we would not recommend it for your brand campaigns as you want your search terms to always include your brand name. Nevertheless, it might be interesting to test it for your generic campaigns.

Upfront launching your experiment, it is fundamental to align on how you are going to assess the success of it. What are the KPI’s you are going to select and as of which percentage uplift you consider the test a victory.

In order to make an A/B test, you can follow these steps:

1. Make sure you don’t have any broad match type keywords in your original campaign.
2. Remove your campaign from the shared budget if you are using one.
3. Go in the recommendation tab of your campaign and look for the “Upgrade your existing keywords to broad match” card.

       4. Click on “View recommendation” then on the three vertical dots in this section:

       5. Click on “Apply as an experiment”: 

You will now arrive in the classic setting of an experiment where you can put your start and if needed, end date. For an ideal A/B test, we recommend having it running for 8 weeks, especially if your conversions have an important delay. In that way, you make sure the algorithms gathers enough data to be able to measure a significant uplift.

Meanwhile your experiment is running, it is important to continuously optimise your campaign by looking at the search terms triggered by your broad keywords. This will help the algorithm understand your business better.

After a couple of days, you can go to the experiment section of Google ads to have a look at the experiment summary.

Depending on your KPI’s, evaluate if the testing campaign is having better performances. When you estimate the experiment has enough data to measure an uplift, you can decide to “apply” or “end” the experiment. When applying, the changes will automatically be done to the original where all keywords will be converted into broad match.

For some clients, we had the opportunity to make the A/B test on few campaigns. One of them ran for 6 weeks and as the trial generated a higher ROAS (+15%) and higher conversion value (+17%) with still relevant search terms, we decided to apply it to the base campaign.

However, for another campaign where we did the same test, results were not convincing. Hence, we ended the experiment.

You now have the steps & tools to set up your own A/B test on broad match keywords. As you can see, the test is easy to implement and it is up to you to put it in place for each of your campaign’s type to determine whether broad match brings better performances or not.

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