Native advertising as a future-proof solution

Less invasive yet effective advertising methods that respect users’ privacy and still deliver relevant results: Over 10 years of experience in content marketing make us certain that native advertising can do just that. It therefore offers a robust solution for the challenges of the post-cookie era. By integrating advertising into editorial content that fits seamlessly into the user experience, brands can communicate their messages in a natural and less intrusive way. This type of advertising benefits enormously from high-quality content that addresses the interests and needs of the target group without having to rely on conventional tracking methods.

Adapting performance strategies in the cookieless age

Our CCO Rainer Willisits is aware of the need for an adapted marketing strategy. “The future may be challenging without cookies, but despair is inappropriate. Within the industry, we have extensive know-how – on the part of technology, media companies, marketers, but also on the agency and customer side – to reach target groups effectively,” he explains and emphasizes that this certainly does not mean a one-fits-all solution, but rather underlines the importance of a detailed understanding of the marketing funnel: ”It will be relevant to have a clear strategy and objective. Performance starts with branding and all funnel stages contribute to a conversion or a lead actually happening. By analyzing and defining the set KPIs, you will be able to recognize the added value of measures – and not just pay attention to the last touchpoint.”

The role of AI and content in the future of marketing

Jürgen Schmidt, co-founder and partner of Content Garden, emphasizes the long-term relevance of AI in the advertising industry. “The topic of AI is currently experiencing extreme popularization through tools such as ChatGPT. In research, of course, we have been working on this for much longer and not everything that is now reaching the masses is really new. But the central point always remains the same: It is above all the quality of the data that determines success, and of course that you know what to do with it,” says Schmidt. He emphasizes that real added value is not created by technology alone, but by understanding and using the data generated. High-quality content provides the basis for generating and interpreting this data and making intelligent marketing decisions based on it. “Especially in view of the shifts in the advertising industry in the so-called cookieless future, the power duo of AI and content is becoming even more important. With few other measures, you can make good statements about user (behavior) via preferences and interaction rates.”

Conclusion

The most interesting opportunities open up for advertisers primarily through creativity. Native advertising is therefore a form of advertising that companies can use to face the cookie-free times. With native formats, they rely on high-quality content that integrates authentically and seamlessly into selected media and conveys brand messages unobtrusively.

Think out of the box!

Of course, as an advertiser you carry around a whole backpack full of brand identity, corporate wording and cemented slogans; sometimes there’s not too much room to manoeuvre. And sure, even we in content management often have an idea of what content might go down particularly well with the target group. However, neither the established brand messages nor our gut feeling are always the last word. So the question must be allowed: Why not try out several different – and sometimes completely new – possibilities? Perhaps the golden egg will be found. What we always find, at least, are insights and learnings.

What is testing actually? A definition.

To put it very simply: testing takes place when at least two equally valid variants of an asset are tested for their suitability in parallel in a comparable setting. Simple example: We show 100 people poster A and poster B and ask them to say which one they like better. Of course, Native Advertising is not about posters, but for example about different advertorials, teasers, advertising material or even specific wordings and images. And we don’t even have to ask people for their opinion here, but get the feedback directly through their online click behaviour. This also allows us to test many more options and parameters simultaneously than just A or B. Potentially, the possibilities are limitless. Nevertheless – or precisely because of this – you have to approach it carefully. Because testing does not always really make sense.

When and where does testing make sense?

Brand messages have to be unchangeable to a certain extent, so that they ensure identification. That’s why you shouldn’t constantly send out new, changing messages. But there are certain (advertising) settings in which you can constantly test opportunities and limits. Native Advertising offers such a setting on a silver platter. Targeted testing takes hold where the ready-made slogan is of no use: in the preferences of the potential target group. Testing these preferences is hardly possible anywhere as efficiently as in online content marketing. Through clearly measurable metrics such as clickrate, dwelltime or bouncerate, we can evaluate which approaches elicit the most response. This is how we receive learnings – for ourselves and for our customers, the advertisers.

Testing in practice at Content Garden

The extent of our testing possibilities depends, of course, on the specific topic, because some products or services simply have more facets than others. On the other hand, it also depends on how broad the target group is, how established the brand message is or how strict any legal requirements are. Overall, however, our testing palette is huge: it ranges from different content approaches to variations in addressing the target group to a targeted check of how we achieve the most click-outs. A few examples?

Testing level #1: Exploring content approaches

• Promotional or editorial? In the course of advertorial testing, we can write several articles on the same product or topic in different formats. The classic way is to test a text with a strong promotional focus against a text with an editorial touch. A very promotional text focuses primarily on brand messages, deals aggressively with the product and tends to use inviting language. In contrast, an editorial text is linguistically more neutral, it focuses on information, entertainment or inspiration, revolves around a (brand- or product-related) meta-theme and sometimes approaches the product “via the back door”.
• Storytelling or fact-heavy? These approaches can also lead to very different content – regardless of whether they are promotional or editorial. The test here is whether the readership is primarily interested in factual information or whether an emotional presentation, for example by means of a realistic story, perhaps with a fictitious buyer persona in the leading role, can win the hearts of the readers.
• Which type of text? Each topic can be presented in several very different formats: from the sober information text to the helpful guide and the popular listicle (“6 things you should know about the testing approach”) to the interview and the narrated product test. What do you think attracts the readership the most?

Testing level #2: Addressing target group(s)

• In linguistic nuances, entire articles can be conceived in completely different ways and thus reach different target groups. For example, it can make a big difference whether we address our readers directly or in a neutral way, or – in German – if we use “Sie” or “du”. Tonality is also an important factor. It can be worthwhile to write one article in a provocative and humorous way and another – on the same topic – in a sober and respectful way. Who knows what insights will come out of it?
• In the course of our teaser testing, as standard we send several different headlines with different lead texts into the race. Together with a header image, the headline and lead text make up a “teaser” – and we test these teasers against each other. Here, the tendencies of the readers can often be measured very clearly by means of click behaviour. We learn: Which headlines are most popular? Which wordings are not accepted at all? Do questions, calls-to-action or factual statements work better? Does a teaser have to be particularly “clicky” or is it better for the specific product to radiate factual restraint? Questions upon questions – teaser testing answers them.
• The image level also plays a decisive role in addressing the target group. To name just a few examples: Do colourful or sober images work better in combination with the headline? Do the high-contrast or the homogeneously coloured images work better? With or without people? Close-up or panorama? Product photos or those that have an editorial feel? We will be happy to find out!

Testing level #3: Triggering engagement

Of course, we also want to get our readers to look further into a topic. In other words, we want to generate clicks and clickouts. There is no patent remedy for how we trigger this engagement – and therefore it is always advisable to test different possibilities here as well. These are, for example:

• Ad split tests: We have various ads in our portfolio. Knowing which one works best provides important insights and helps in subsequent marketing measures, among other things.
• With regard to engagement, we take another look at the image level under different circumstances. Here we can sometimes go completely different ways than with header images. A small example: product release plates (which are not suitable as header images on daily media) can have great relevance here and are even often our “test winners” in certain sectors – which is why we naturally use them more and with success in these sectors. Thank you, testing!
• In ads, too, the testing approach is often a gamechanger on the linguistic level. There are always interesting learnings here. Questions could be: Should the tonality of the ads rather adapt to the article or stand out from it? Is it better to use strongly promotional text or to remain neutral and informative? Do readers prefer a strong call-to-action or do they prefer factuality or even mysterious wording?
• Of course, we also have to consider the content level in terms of engagement. Should we tease out more details about the product or should we insist once again on the known values? Is it worth triggering the “fear of missing out” by talking about limited availability or limited editions? And which product USPs are the most likely to encourage clicks? We want to know. That’s why we test!

Non-stop insights: Our technology makes it possible

The beauty is that all these efforts do not come to nothing with us. Because we can not only creatively implement testing approaches, but also distribute the content via our publisher network, administer it via our internal system and analyse it with data support via our in-house technology. The analysis already takes place during the runtime of a campaign: we optimise the content in real time, discard elements that are not performing well and replace them with new ones if necessary. In short, testing provides us with continuous insights that help us to become even better every day.

Have the courage to test! (Your brand, too, can be different.)

A little euphoria at the end can’t hurt. That’s why we say: Let’s test for all we’re worth! Because gut feeling and established brand messages are all well and good; but with approaches that go outside the box, we can all only win: the advertisers, the creative minds in content creation and also the readers. After all, testing is about finding out what the target group is really interested in – so we can create advertising content that people enjoy consuming. If that isn’t by chance the declared goal of Content Garden!

By the way, you can find out more about how this goal also binds us together as a team following this conservative link. Or, if you prefer, here: You can’t miss this! Because testing is simply our thing.

Pointless debates on principles

Of course, when we say “best case scenario,” it’s a dangerous word in the current discussion around AI texting tools. Are these programs good or bad for our society? Are they a risk or a long-awaited salvation? Will they put copywriters and translators out of work in droves? Or -? Stop. We don’t even want to get into pointless debates about principles. The fact is that AI text tools are here to stay. And they can already do a lot.

We should therefore come to terms with their existence and take a close look at what we can use them for – and rather not for. In this magazine article, we show how we should view these tools critically, but why the apocalypse of human language is not imminent. Because AI text tools will also become immensely valuable for us in the marketing industry. Even – read this and be amazed – for copywriters.

This program “can do something with text”: A differentiation

AI text tools are springing up like mushrooms and it’s easy to lose track of them all. We won’t try to discuss individual programs here, as the proliferation is now too great and our magazine is not the right place for that. What is worthwhile, however, is to take a look at what types of tools there are. After all, two people (or machines) are not always talking about the same thing when it comes to AI text tools. Very roughly, we should at least distinguish these AI text competencies:

• Text comprehension
• Text translation
• Text editing
• Text production

We will not look at translation in more detail here. Although it is causing major upheavals in the world of communication, it is only indirectly relevant for us at Content Garden. In addition, it functions in part according to different technical laws than the other three areas: While in translation the basic command to the AI is always to convert language A into language B, far more complex mechanisms often take effect in understanding, editing and production. The principle of “I tell the AI what I want and it spits it out” only sounds simple at first glance, but the cheese is not eaten that quickly. Reassuring, in a way. Because this is where we as human users come into play.

Prompting: the new key skill?

The so-called prompting (actually a term from behavioral therapy, which is now applied to AI) refers – in very simplified terms – to the process of the AI receiving a linguistic instruction from the user as to what it should generate and output. So a prompt is basically a command in text form. It can be a single sentence, but it can also be multiple sentences, complex prompts with lots of details, or even just a few keywords. The AI’s “associative memory” links the contents of the prompt with the existing training data – and generates corresponding output.

Sounds simple? Think again. In practice, this is where the big challenge lies when dealing with AI tools, but at the same time, the beautiful necessity to bring in our human creativity. This is not only true for copywriters, but for everyone who wants to use AI for creative purposes. However, especially for people who work with text, a great potential reveals itself here: after all, the prompt must also be in text form; and the more accurate, targeted or even innovative this command is, the better the result will correspond to what was hoped for.

Thus, the question of how to “compose” (good) prompts is certainly the most exciting and ultimately decisive thing for the text industry when dealing with AI tools. Only those who can prompt well will be able to work well with an AI and use it meaningfully. Developing skills in this regard, gaining experience, and merging the possibilities of AI with one’s own talents will be inevitable for copywriters in the future. In other words: prompting could develop into a new key qualification in the creative industry.

AI text tools at Content Garden: To what extent is this imaginable?

How relevant and accurate the capabilities of an AI program are always depends on the area of application. If we look at the four areas of expertise mentioned above, it quickly becomes clear that all of them have relevance in marketing. As far as we at Content Garden are concerned, AI penetrates directly into our core activities with its capabilities in text comprehension, text editing and text production. But where is the practical application now? And where are the limitations (at present)?

Accurate: Why we let the centaurs into the garden

The centaur concept, i.e. the targeted collaboration of humans and AI, is by no means new. The basic idea is that a fusion of human and artificial intelligence should be able to deliver the best possible result, in other words: outperform both the human working for itself and the AI working for itself. In some restricted and completely programmable areas, this idea is now obsolete (for example, “centaur teams” of humans and computers are no longer necessarily better than the AI operating alone).

However, wherever highly dynamic circumstances prevail, where genuine originality is required and where unconventional thinking is needed, the human+AI team is currently still ahead. And that’s ahead of AI alone, but also – watch out – ahead of humans doing without AI. And that’s why we let the centaurs into our (Content) Garden.

“AI will speed up text production and identify gaps”.

It’s clear that we need to use modern technologies to stay competitive. And hand on heart: many functions of AI text tools genuinely sound like they make work easier. They can support us in the creative process and, in the best case, efficiently relieve us of a tedious part of the work. “We can assume that AI text tools will help us in many situations,” our COO Stefan Huber also predicts.

“They will speed up text production, point out gaps in research, sometimes provide new input. AI will also be helpful for more mechanical tasks like testing different headlines. On the other hand, we can rather not expect completely new ideas from it, because the AI feeds its productivity only from existing data. And as we know, there is already a huge amount of content on almost every topic due to the many content marketing efforts of companies. When AI makes use of it, it’s not innovative or fresh. For innovation and also for quality control, it certainly still needs the human behind and with the AI.”

What does this mean in practice?

Under the basic assumption that a “centaur model” is the future, we took a look at where and how AI text tools could already be useful for us now or in the near future. And we found plenty, from briefing to final review:

• #1 Help with research: Here we can let the AI support us so that we don’t miss any important points. The AI could also research relevant keywords or create a summary from the researched data.
• #2 Capturing briefings and customer material: Especially when a lot of material has been delivered, the AI could elaborate and summarize the key messages so that we can recognize the red thread more easily and quickly. The material can be prepared in a structured and reduced way.
• #3 Create outlines: We can give the AI an idea for a text and then receive an outline from them (for example, a table of contents or a specific suggestion for the text structure).
• #4 Provide inspiration: The AI can enrich our drafts or thought shards with further suggestions that we use as inspiration. This improves our drafts and helps us discover approaches we didn’t know or see before. Writing blocks may also be solved by such inputs.
• #5 Production of certain text parts and text types: For text components such as headlines, subtitles, calls-to-action or “foil-like” text types such as press releases, suggestions could be created by the AI. SEO-optimized suggestions or variations of best performers could also come from the AI. In addition, the AI could create derivations from a more extensive text type to a smaller one – a classic example is the generation of a social media post from a blog post. Retexting into Simple Language is also efficiently possible using AI. We could then continue to work from the AI suggestions and build on them to create the final versions.
• #6 Optimize texts: Texts produced by us could be improved by optimization suggestions from the AI. For example, the AI can explore alternative formulations, suggest text shortening, emphasize aspects more strongly or close thematic gaps.
• #7 Error checking: AI text tools will also help us in a purely formal sense. Error detection and related correction suggestions, also stylistic corrections and other aspects of proofreading can be partially covered by AI.

These points are just a few examples of how artificial intelligence could be useful for us in text production or even crucial in terms of competition. But what about the flip side? What can AI text tools not (yet) do, in other words: Why is the centaur team human/machine probably still better than the AI producing alone?

Human – All-too-human – Inhuman

When we talk about the limitations of AI text tools, we should keep in mind that even today’s possibilities were perceived like science fiction some time ago. Even now, defensive stand-up phrases like “After all, an AI can’t feel like a human…” still circulate in many places. That may be true, but in certain areas of application, the result is still amazingly human. All too human, perhaps – because this is precisely one of the biggest limitations of these tools: they need human data from which to draw.

Their algorithms rely exclusively on existing knowledge; new knowledge and new skills must always come from humans. In addition, timeliness is never completely achievable (for example, ChatGPT’s training data only extends to the year 2021), critical thinking cannot be programmed, and existing (linguistic) structures are simply adopted by the AI. As a result, an AI ultimately remains inhuman. At Content Garden, we therefore believe that AI text tools are not (yet) relevant in some things, or do not have the sufficient capabilities to help us here for the foreseeable future. A few examples:

• #1 Strategic planning: AI text tools don’t have our concrete experience values from daily business. Therefore, they will tend to be of less help in the strategic component – such as designing campaigns, editorial plans, or applying in-house experience data.
• #2 Original approaches: When unconventional thinking, new methods, and fresh ideas are needed, we’d better not rely solely on AI. An AI text tool can be surprisingly creative, but not original in the true sense. It can provide inspiration, but the truly new must inevitably come from humans.
• #3 Read between the lines: Want an example of where AI is limited? For example, a briefing must be extremely good and precise for the AI to implement it on its own or even summarize it in a meaningful way. The AI text tool cannot read between the lines, nor does it know the history between us and the client.
• #4 Critical text types and topics: Some things may be too difficult for an AI text tool to understand and produce at the moment. The more complex, timely, or specific a topic, the riskier it is to rely on AI. Training data and contextual knowledge are still lacking for certain circumstances, other topics can only be tapped based on personal experience, still others require in-house knowledge of a text type, mechanics, or environment that is simply unknown to AI.
• #5 Refinement and control: we believe that human control and editing of all texts will still be necessary. There are several reasons for this assessment. Just a few examples: Any AI is automatically subject to the bias that it sometimes adopts errors or discriminatory structures from its training data. It can only react inflexibly to (linguistic) currents. It only knows the logic of the training data, not the erratic of the real world. She lacks mental concepts like space and time. Moreover, it can never make personal assessments and process its own empirical values. Therefore, a text produced by an AI text tool should always be critically read and improved by us. Not least because of the human touch that should be noticeable in our texts, it is currently hardly conceivable for us to have entire texts created only by an AI.

As a reminder, it is literally a tool

Hopefully, one thing has become clear by this point: We at Content Garden are convinced that we need to make use of artificial intelligence in our work with texts. The partial unease with which AI programs are currently eyed may have its justification in some respects. Nevertheless, artificial intelligence will not displace all text creators. But text creators who use AI will displace text creators who do not use AI. Why? Because a tool is a tool. People have been inventing tools for thousands of years. And as with any tool, the personal result depends on how well you can use it.

Conclusion: The machine learns. Humans have to learn along with it.

The bottom line is that what AI text tools do doesn’t even cross the line of what has long been conceivable. After all, a chatbot that writes a love letter in my place is no different than Cyrano de Bergerac or any other ghostwriter. Authentic? A matter of opinion. Honest? It just depends on whether you play with open cards. Qualitative? Obviously. Stopping? No. So let’s do the only sensible thing: pick up the tool, learn its use, and use it.

Finally, we must honestly concede: at this point, no one can seriously predict how the development of AI text tools will continue. The ideal case would be that new inspiration, more efficiency and better quality emerge from the centaurian interplay between man and machine. At any rate, we at Content Garden will be eagerly contributing our creative share.