For a long time, the click was the most convenient link between interest and action. Anyone looking for a product would click on a result, visit a website, and make a purchase. Reporting painted a picture of a seemingly seamless chain: costs here, sales there. According to the Swiss Advertising Statistics Foundation, search engine advertising in Switzerland has, for the first time, surpassed the combined volume of all domestic electronic media: approximately 1.42 billion Swiss francs compared to approximately 1.38 billion Swiss francs. Search and social media combined total more than two billion Swiss francs. This means that a significant portion of the Swiss advertising market is accounted for by digital environments, whose usage and measurement dynamics are changing.
At the same time, budget pressures are mounting. According to the LSA/SWA Industry Indicator 2026, only 15 percent of the companies surveyed plan to increase their communications budgets, while about one-third expect a decline. On average, 57 percent of media budgets are expected to be allocated to digital channels. Discussions with clients consistently reveal that what can be measured quickly and is easier to justify internally is given greater weight. What appears on the dashboard also shapes the media plan.
When the search ends without a click
Since Google introduced AI Overviews in Switzerland in March 2025, some information searches have ended directly on the results page. The search engine summarizes relevant content and provides links to additional sources. Users who find the answer they’re looking for right there no longer need to visit a website. A Sistrix analysis of more than 100 million Google keywords in Germany illustrates the scale of the impact: For search terms with AI-generated answers, the click-through rate to the first organic result dropped from 27 to 11 percent. Extrapolated, this amounts to approximately 265 million fewer organic clicks per month, though the impact varies significantly depending on the category.
Some Swiss media outlets are also observing this effect. According to their own reports, SRF and Swissinfo are seeing more of their content appear in search results, while click-through rates are declining. According to the IGEM Digimonitor 2025, 60 percent of the internet-using population between the ages of 15 and 75 also use AI tools at least occasionally. AI is therefore no longer a niche phenomenon in this country either. These figures pertain to organic search, not Google Ads conversion tracking. The gap arises before measurement even begins: part of the information-gathering phase ends on the search results page. Fewer organic clicks therefore do not automatically mean less demand or revenue. A familiar part of the customer journey simply becomes visible less frequently than a website visit. Paid search remains important. It capitalizes on existing search intent and can measurably guide users all the way to conversion. Where organic traffic is becoming scarcer, paid visibility may even gain in importance. However, those who focus their budget heavily on existing demand will face fiercer competition for the same search queries. Rising click prices are a possible consequence. And if insufficient interest is generated earlier in the process, brand- or offer-related search demand is also likely to grow more slowly.
The result is not a shift away from search. The key is making a distinction: Which part of the budget captures existing demand, and which part builds future demand? Without this distinction, interactions that occur close to the visible action are given greater weight, while earlier contributions to impact are overlooked in reporting.
Every contact needs a task
People don’t engage with a brand based on the logic of a media plan. Yet campaigns are planned and evaluated exactly that way: along individual channels, with clearly assigned tasks. This provides clarity but only partially reflects the decision-making process. The key is not to use as many channels as possible, but to assign a clear role to each touchpoint. The contribution a touchpoint makes depends on the target audience, the message, the timing, and the usage context. The same channel can fulfill a different role depending on the starting point and the objective. Three questions help determine this role.
What demand already exists, and what demand still needs to be created?
A high number of search queries and conversions can be a sign of a well-managed campaign—or simply indicate that the brand is already well-known and the category is in high demand anyway. When allocating the budget, it should therefore be clear which portion captures existing demand and which builds new interest. A practical starting point: Which measures respond to already visible interest, and which ones create awareness, relevance, or a reason to search in the first place? A touchpoint shortly before a purchase can trigger immediate action. Awareness, recall, and trust usually take more time. If both objectives are evaluated within the same short measurement window, the contact closest to conversion will almost inevitably come out on top. The numbers may add up, yet still provide a distorted picture of the actual impact.
What is the purpose of this contact?
The context in which a message is viewed plays a key role in determining whether people pay attention to it, skim over it, or click away. An offer that requires an explanation takes more time than a short-term price promotion. Some products need to be experienced. A reminder, on the other hand, assumes that interest already exists. Before a campaign is launched, it should therefore be clear what each touchpoint is intended to achieve. Different advertising formats create different conditions for this. In a survey of 2,005 people commissioned by Swiss Post Advertising and conducted by intervista, outdoor advertising on billboards and digital screens, direct mail, and television were among the most frequently noticed forms. When it comes to the statement «I take the time for this,» direct mail performs the best. The survey measures perception, not sales, and does not assign fixed roles to the channels. However, it shows that the medium and the usage situation play a role in determining how intensively people can engage with a message.
Do the metric and measurement period match the task?
A contact intended to trigger immediate action requires different metrics than one designed to prepare a decision. The CMC Print Mailing Study 2026 demonstrates just how critical the time frame is: The Collaborative Marketing Club and Deutsche Post analyzed 858,490 mailings from 35 German online stores. Forty-six percent of the orders attributed to the mailings were not received until the fifth week after mailing. The measurement period belongs in the campaign plan, not in the post-campaign analysis. The study examines a clearly defined use case from a mail-related context and cannot be applied indiscriminately. Nevertheless, the result highlights what short measurement windows overlook: Anyone who had taken stock after four weeks would have missed nearly half of the orders attributed later.
Measurement begins before playback
Effective measurement doesn’t start with reporting—it begins before the campaign launches. A campaign therefore needs not only a goal but also a clear logic of impact. Marketing managers, agencies, and advertising providers must define what contribution individual contacts are expected to make and how this contribution can be realistically assessed later on. Equally important is transparency regarding what can actually be attributed to the campaign and where there is only a plausible correlation. There is no universally applicable budget allocation. However, every budget proposal should separately identify which portion builds demand and which captures existing demand. This distinction alone shows which portion of the investment captures short-term reactions and which creates the conditions for future demand. With the increasing use of AI, this distinction is becoming even more important. Systems consistently optimize for the goals they are given. This makes it all the more important for marketing to carefully decide whether a campaign should build brand awareness, explain an offer, or capture existing demand. Clicks remain an important metric, but they are not the sole proof of advertising effectiveness. With generative search, this metric becomes even less reliable.
Not every impact results in a click. Those who invest too heavily in what gets clicks today are sacrificing tomorrow's interest and demand.
The author
Sabrina Wettstein is the Head of Sales at Swiss Post Advertising. Together with her team, she assists companies with questions regarding targeted, data-driven, and effective marketing communications.
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It’s a scenario that nearly every platform keynote is touting these days: The customer journey has become «predictably unpredictable»—a chaotic web of search queries, videos, shorts, and recommendations that no marketing team can manage manually anymore. So we need artificial intelligence to bring it all back together—automated, data-driven, and scalable. Google’s presentation at OMR is just one variation of a pattern found in nearly identical form at Meta and TikTok. Google aggregates intent. Meta, the social graph. TikTok, the culture. And all three do so within a system that simultaneously controls the process and measures its own success. Even at the 2026 Cannes Lions, «Black Box» suddenly became a buzzword—the first providers there explicitly positioned themselves as a «Glass Box» alternative to the lack of transparency on the major platforms. For the pattern behind this, I propose a term of my own: Black-Box Marketing.
Three «new products,» one sample
With «Performance Max,» the Google algorithm distributes the budget across Search, YouTube, Shopping, and Display without allowing advertisers to understand exactly what the system is optimizing for. At Meta, «Advantage+» has been the default setting for new campaigns since December 2025, rather than an option; Mark Zuckerberg described the vision as early as 2025 as a redefinition of the advertising category: A company simply specifies its goal and budget, links its bank account, and the machine takes care of the rest. On TikTok’s For You page, no one outside the company knows the algorithm that determines—based on dwell time, likes, shares, and even the brief hesitation before swiping—whose message is displayed and how often. New to TikTok is the «Symphony Agent.» This is an intelligent, «agent-based» AI layer that is directly integrated into TikTok’s existing advertising ecosystem and «helps» marketers create campaigns in record time.
From an economic perspective, this is a classic «principal-agent problem» (Jensen/Meckling, 1976), exacerbated by information asymmetry, as described by George Akerlof (1970) for markets with unevenly distributed knowledge: The agent—the platform—has an information advantage that the principal—the advertiser—can neither see nor independently verify. «Black-box marketing» therefore refers to advertising whose management, optimization, and performance measurement take place within a system controlled by a single party, which simultaneously acts as the “judge” of its own success.
Brand Vector vs. Platform Vector
A simple illustration can help explain what this concept means beyond individual campaigns. Every brand sends a message with a specific intent, a specific tone, and a specific target audience—let’s call this the «brand vector.» However, each platform optimizes not for this vector, but for its own: dwell time, engagement, and advertising revenue. That is its «platform vector.» The challenge: These two vectors are aligned only in very few cases. How far they diverge depends on the logic of the respective platform. The distortion is likely to be least pronounced on Google Search: an explicit user intent meets an ad that responds precisely to it, because the business model itself is based on this alignment. However, the «Gemini layer» is leading users further and further away from the actual search results. Searching and finding content are becoming less transparent here as well. And YouTube, in particular, alters the picture even more—a fact that can now be substantiated. A study by Webrepublic and GfK on Swiss YouTube usage (2026) shows that reach alone does not guarantee impact. What matters is whether users are actively and intentionally engaging with a piece of content or simply letting the platform run in the background for casual entertainment.
Here are the facts: On average, Swiss people spend 41 minutes on the platform on weekdays and 50 minutes on weekends, with the quality of their attention varying greatly. «»Not all YouTube is the same,’ conclude the study’s authors—the same ad can be played in either an attentive or a casual setting. Which setting it ends up in is determined not by the brand, but by the algorithm. On Instagram and TikTok, finally, the distortion is amplified: Assets are remixed, re-scored, removed from their original context, and distributed according to cultural rather than communicative logic. A campaign intended for a specific context can end up as an audio snippet alongside completely unrelated content, without the brand ever even knowing it.
"Black-box marketing" therefore doesn't just mean not seeing the mechanism at work. It means not even knowing how much your own message differs from what actually gets through to the other end.
«Vendor Lock-in» as the Price of Passivity
Just enter the goal and budget—the machine does the rest. This convenience has a strategic downside that is rarely discussed: vendor lock-in. The more functions are outsourced to the black box, the more expensive it becomes to switch providers. This is not a side effect, but part of the business logic, and it doesn’t happen overnight—rather, it unfolds across four mutually reinforcing levels: A: At the data level, conversion signals, target audiences, and learning curves are stored within the platform and cannot be exported; what the AI has learned about a company’s own customers remains with the provider—anyone who switches providers has to start from scratch. B: At the competency level, the organization loses the ability to manage the process; as soon as the machine takes over the rest, in-house knowledge of campaign management, media planning, and creative work erodes until the organization simply lacks the ability to do things differently (and perhaps even better), even if it wanted to. This is the most dangerous level because it remains invisible. C: At the level of measurement, success is gauged by metrics that the platform itself defines, collects, and reports—without an independent benchmark and without comparison across providers. D: Ultimately, even at the level of creation, assets are increasingly generated by AI and optimized directly for the algorithm that delivers them. As a result, brand communication becomes platform-driven, tailored to a system over which one has no control.
Together, these four levels result in at least an information asymmetry—if not a power asymmetry—that is structural rather than accidental. Meta itself provides evidence of this. In January 2024, the company removed several thousand detailed targeting options from Ads Manager without warning—categories on which many advertisers had built their audience strategies for years. Those who still wanted to manage their campaigns themselves were effectively pushed toward «Advantage+ Audience»—the automated system that has been the standard since December 2025 anyway.
The same applies to attribution: Meta has repeatedly shortened the standard time window for attributing success, most recently from 28 to 7 days after a click—overnight, identical campaigns began to look less effective, even though their actual impact hadn’t changed. The benchmark has shifted, not reality. Anyone who builds their marketing on someone else’s operating system accepts that its owner also writes the rules by which their own success is measured.
What CMOs Should Do Now
None of this means we should demonize the platform. The efficiency gains from Performance Max and Advantage+ are real, and no marketing strategy today can ignore these reach figures. Something else has shifted: the question of what really matters. The following consequences can be directly derived: Independent measurement: When the algorithm both controls the campaign and measures its success, optimization is based on a reality defined by the platform itself. Understanding impact in one’s own market requires benchmarks that go beyond the platform’s own metrics. Transparency as a value in its own right: The relevant demand on platforms is not «less AI,» but rather transparent rules—and a willingness to evaluate providers based on those rules, not just on reach. Sovereignty instead of dependence: Keep first-party data in-house, retain internal control, and practice multi-homing instead of monogamy. Find the «Big Idea» (again)!: · Develop your own clear idea against which the platforms« »math” must be measured—instead of leaving it up to them from the start.
These reflections are part of a broader examination of what I call the fragmentation paradox: platforms that market as a solution something they themselves have helped create. Black-box marketing is not a marginal phenomenon here, but rather the core of a business model. A black box remains a black box, no matter how efficiently it calculates.
The author
Prof. Dr. Marcus Schögel, Institute for Marketing and Customer Insight at the University of St. Gallen (HSG), President of the Swiss Marketing Association (gfm).
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Good-sounding answers are not yet good plans. This is exactly where Guido Trevisan comes in. He looks at AI not as a promise of salvation, but as a system that comes with its own blind spots. Where data volumes are impressive, knowledge does not automatically begin. And where machines radiate security, they can also conceal a distortion.
What initially sounds like a steep thesis becomes clearer on closer inspection. If AI-based media planning systematically pushes budgets towards those platforms that provide the loudest, densest and most machine-readable information about themselves, then more than just the balance of power between digital and traditional channels will be shaken. This raises the fundamental question of how advertising money can be planned responsibly, effectively and in line with the market in the future.
In this conversation, Trevisan is not sounding the alarm for the sake of it. Rather, he comes across as someone who is following a trail hidden behind plausible results. His suspicions are not aimed at the technology itself, but at its data basis, logic and blind self-evidence. This opens up a larger topic: it's not just about print, TV, audio or DOOH. It's about the architecture of media planning in the age of AI - and the question of whether the industry is currently encountering a new form of bias that sounds too slick to make people immediately suspicious.
Guido Trevisan, you talk about «AI bias» in media planning. Are we talking about a real market problem here - or a rather steep thesis with political benefits for traditional media?
If the thesis is correct, it is not just print, TV or OOH that has a problem, but the entire advertising industry. If you take the thesis further and follow what the current AIs recommend, ineffective media planning would also weaken the advertising impact as a whole. As a result, investments in commercial communication would be reduced and companies would invest elsewhere.
In your view, what exactly is the bias: that AI plans incorrectly - or that it ruthlessly reveals which channels simply dominate in terms of data today?
The bias clearly lies in the incorrect planning of the AI. Assuming that media agency employees and marketers know something about their work, it is very surprising that the planning recommended by AI - especially in the USA, where this bias has already been proven - differs greatly from human media planning. The fact that digital media «produce» more data, even if only partially measured independently, cannot be denied.
You say that large language models (LLMs) and AI systems favor digital platforms. Where does the bias begin here - and where does simple data-driven logic end?
When it comes to media planning, even more reference should be made to existing independent data. Properly prompted, LLMs are a fantastic tool. However, the same applies here: «garbage in, garbage out».
In the end, isn't AI bias simply the new, algorithmic version of the human bias that the industry discussed years ago?
Both are dangerous when it comes to using advertising money in trust. However, I believe that the structural damage potential is much greater with the algorithmic version, because you get a good-sounding answer to almost every question at the touch of a button.
Hand on heart: How much confidence do you have that a machine can actually make media plans worse today than a person with experience, gut feeling and Excel?
As already mentioned, correctly prompted, the machine can certainly produce satisfactory plans. Today, however, most results are inadequate. If an agency briefing is created with an AI and then forwarded directly to the agency one-to-one without experience, gut feeling and Excel - i.e. without any knowledge of its own - the result is rarely any good. I have been told that this is already happening with well-known clients. And then there is the problem that strategic media planning is taught less in further training courses. This is where we as an industry need to start, so that the «human in the loop» generates important added value.
Your argument is based heavily on observations from the USA. What is robust enough about this to shake up Switzerland right now?
It is possible that the deviations from booking behavior in Switzerland will be even greater, as traditional media are more relevant in this country. The data from the USA has at least made it possible to engage in discussions with renowned organizations such as Leading Swiss Agencies and the Swiss Advertisers Association. Our common goal is to implement a similar analysis for Switzerland.
Couldn't your thesis also be read like this? The tech platforms have done their homework when it comes to data - and traditional media have put it off for too long?
Sure. But in my opinion, it's irrelevant. And although the tech platforms have some of the smartest minds, I don't believe that this bias in media planning has been planned this way for a long time. We need to act now and, with relatively little effort, we can provide the Swiss advertising market with a tool to plan reach and impact more effectively based on existing Swiss studies.
If AI recommends traditional media too little: Is this really a bias of the machine - or a visibility problem for traditional providers?
One idea was to simply publish more Swiss data and thereby correct the outputs. The LLMs are based on global information. Our realization: Of course we can and should publish more, and the service providers downstream in the value chains should also make greater use of this. However, believing that we can significantly influence the system with a little additional Swiss information has proven to be a misconception. Quite apart from the fact that the research organizations' data has value and they want to continue to capitalize on it.
The classic genres in particular often insist on their impact, but struggle with comparable, open and machine-readable data. So is the AI bias also homemade?
I have a different view of things. It is the classic genres that can be measured independently wherever possible and financially viable. It's not just in Switzerland that the multinational platforms find this difficult, even though they would have the money and the technical possibilities. Various industry organizations have already tried to involve the platforms in various projects. However, the majority of them have rejected comprehensive cooperation.
You warn against unjustified budget shifts. But who says that these shifts are unfounded as long as the digital side simply provides more reliable signals?
The buddy who chats the most at the regulars' table is not always right. The amount of data alone has no value. The point is to clarify whether it serves the purpose, namely the most effective media planning possible.
In your view, how problematic is it that the large platforms of all things measure their own impact, display it themselves and are also the loudest source of data for many AI systems?
Agency employees and marketers must be aware that the player and referee are the same person in a figurative sense when they are planning the advertising money. Whenever possible, they should consult additional, independent data as a basis for their decisions.
When would your thesis be proven for you? What would a Swiss investigation have to show in concrete terms for the suspicion to become a reliable finding?
In my opinion, it depends on the overall picture. In the US, the model has recommended investing 7 percent in traditional linear TV and 18 percent in CTV. Although the market is of course very different, I would interpret this as a strong distortion of reality in Switzerland. The results must be interpreted by experts. I don't think it makes sense to define a fixed value in advance.
Your approach is called «Swiss Media Agent». Is this a neutral corrective - or ultimately an attempt to give traditional media more weight in the engine room again?
Even if we were to put all the Swiss data available to us online, we would not be able to trim the LLMs to Switzerland. The concept of the «Swiss Media Agent» is that we collect the media data that is freely available today - IGEM Digimonitor management summary, Media Focus advertising compass, WEMF circulation bulletin, data from the Statistical Office, etc. - in one data pool. Anyone using a standard LLM to create a briefing for a campaign or campaign planning can simply integrate the «Swiss Media Agent» into their query in order to enrich the global information with specific Swiss know-how. We create transparency by communicating which data has been taken into account. The data sources are to be defined by a committee - for example from academia, advertising clients, media agencies, marketers and publishers.
And perhaps the decisive question: Is your ultimate aim to make AI better - or to prevent traditional media from losing relevance in the age of AI?
Today, the results of AI simply sound good. We want to refine them with our Swiss data. The efficiency of AI is thus combined with in-depth market knowledge. In this way, we create the basis for campaigns to be played out even more precisely with critical classification and responsible budget management.
What concrete next steps are you planning?
On the one hand, the AI bias in media planning for Switzerland is to be demonstrated. To this end, we are in contact with data scientist and software architecture specialists. Secondly, we would like to bring together interested organizations and individuals who are willing to work on a joint solution or further development of the «Swiss Media Agent» with their ideas, expertise and financial resources.
Anyone who takes a closer look at the terms "effectiveness" and "efficiency", which are quickly thrown around but always cause confusion, quickly realizes that the two terms are close in terms of communication, but can nevertheless be used in opposite or even competing directions.
But what is more important in communication? One thing is certain: effectiveness before efficiency. Too much focus on efficiency can even be disastrous for brands (a tiresome topic - especially in media audits!). Many brands focus on what they can measure quickly and easily and not on what is important for their brand. Research (e.g. the IPA) shows that pure direct response or performance campaigns are a good way to achieve maximum efficiency - with the downside that the impact on sales is very short term, falls away quickly and contributes little to sustainable brand building (and sales).
Dependency for performance campaigns
Performance campaigns are important. But anyone who invests too much of their budget in them runs the risk of becoming highly dependent. And any brand that tries to constantly push its sales figures via performance marketing will find it difficult to let go. This requires the courage to change strategy.
In contrast, longer-term branding campaigns are usually highly effective, but less efficient if they are not supported by activating channels. Ideally, powerful communication strategies should combine effectiveness with efficiency. Here too, it's all about finding the right mix for your brand.
10 steps on how to get a good deal closer to the impact of your campaigns:
- Clear strategy: Do not confuse "strategy" with "goal". Unfortunately, this is a common mistake.
- Clear goals: Define clear communication and media goals and start with a well thought-out briefing. Often underestimated, but essential for strategy development.
- KPIs: Measure and evaluate the success of the campaign with the right key performance indicators. These should not contradict each other. And here too: Less is more!
- Clear target group: These can be identified and defined according to various criteria and should be neither too narrow nor too broad. Be careful with so-called "desired target groups".
- Channel selection: Select the most suitable media channels to reach the target group and objectives. Justify the choice of channel and assign a clear role to each medium.
- Message: The choice of channels should go hand in hand with the creation.
- Creation: 50 -60 percent of the advertising impact comes from the creation! One of the biggest (and most underestimated) levers. Or as advertising guru Dave Trott said: "Creativity is the last unfair competitive advantage".
- Budget distribution: Invest the available media budget in the channels so that they complement and reinforce each other in order to achieve the best possible effect.
- Range: Place significantly more emphasis on reach than on CPM.
- Effectiveness: Make this the primary goal. Efficiency comes second. If efficiency were your sole goal in the long term, the logical consequence would be drastic budget cuts and falling sales figures.
*After holding various positions at media and creative agencies and as marketing manager of an outdoor advertising company, he took over Oliver Schönfeld took up the position of Managing Director at TWmedia in Basel in 2018. He has acquired his knowledge through a great deal of curiosity, many years of experience and by studying marketing and psychology on the windy Baltic coast and in vibrant Manchester.
To the whitepaper series on Werbewoche.ch
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Defining the right and realistic goals in media planning is like a map that shows the captain on the high seas the way to the port of destination. Without a map (and a destination), you will just drift around blindly, end up in uncharted waters or, in the worst case, be shipwrecked.
If the goals are unclear or unrealistic, the result will inevitably be unsatisfactory. It becomes fatal if the strategy is based on castles in the air that are impossible to achieve. Often too many - or, even worse, contradictory - goals are stated in the briefing. With the result that the media money fizzles out ineffectively.
Ask yourself questions
Every brand must define for itself where it positions itself and where it wants to position itself in the future. However, this also includes localizing where deficits exist. For example, in terms of brand awareness, image, likeability, buying interest, recommendation, loyalty, etc.
This is condensed in the question: "What do people think about my brand today and what should they think about my brand tomorrow?" This is formulated and visualized in a strategic brand and media plan that shows which measures can be used to achieve the path from the current situation to the target situation.
This question is essential information for defining communication goals and measures. In reality, however, many people give little or no thought to brands - or much less thought than some marketing managers would like to admit.
However, goals should also be set in such a way that they have a chance of being achieved. The best goals are of no use if I don't have the means and basic strategic considerations to implement them in order to gain a competitive advantage over my competitors.
A good communication plan is effective
Marketing and communication goals should therefore be conclusively argued in the briefing so that the media agency can derive comprehensible - and above all realistic - media goals from this. The clearer and more unambiguous the objectives are defined, the more effective the media strategy developed from them will be.
A good communication plan defines clear goals and places them in a realistic relationship to the competition and the available resources. In my experience, this should not exceed a period of twelve months. Anything else would be nonsense. A marketing strategy, on the other hand, should be much longer-term. Otherwise, you run the risk of so-called "marketing tactification" - also known as "actionism" - which is the opposite of a strategy.
Use SMART goals
Good marketers use so-called SMART goals that are specific, measurable, achievable, realistic and scheduled. Above all, the fewer and clearer the better. Or as marketing guru Michael Porter would say: "A strategy defines what you shouldn't do." Of course, you can come up with a colorful bouquet of goals, but this is not expedient. With too many goals, you run the risk of getting bogged down or contradicting yourself. In my experience, unless you are P&G, Unilever or Amazon, a maximum of two to three goals work best for the overall plan.
The choice of the right channels, combined with the right message (creation), plays an important role here. In a good media strategy, each selected channel is assigned a justified role (task) in the mix - which does not always happen in reality. But the success of your media strategy and the use of your advertising budget depends on this.
More information about the The third white paper from TWMedia deals with the topic of "Goals" - with valuable practical tips for more media power.
After holding various positions at media and creative agencies and as head of marketing at an outdoor advertising company, he took over Oliver Schönfeld took up the position of Managing Director at TWmedia in Basel in 2018. He has acquired his knowledge through a great deal of curiosity, many years of experience and by studying marketing and psychology on the windy Baltic coast and in vibrant Manchester.
To the whitepaper series on Werbewoche.ch
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Webewoche.ch: Artificial intelligence is currently a big topic, especially for text and image creation. Where do you see the potential applications in media planning?
Nico Podgorny: In today's data-driven advertising industry, the use of artificial intelligence (AI) opens up new opportunities to optimize marketing strategies and develop more effective advertising campaigns. As an innovative agency, we are very curious about the benefits of AI. Currently, it is a big playing field for us, where we are trying out many things, looking for practical benefits for our clients or testing the simplification of internal processes. I currently see opportunities in conception and rough planning, and especially in the area of data evaluation. But also in detailed planning and implementation, I can well imagine that there will be solutions in the near future thanks to AI.
Which specific media planning tasks could be supported or even taken over by AI in the future?
One area where the use of AI offers particularly great benefits is target group analysis. By using advanced algorithms, we can analyze extensive amounts of data to gain valuable insights into the behavior and preferences of the target group. AI enables us to identify patterns and trends in order to develop personalized campaigns and thus minimize wastage.
What other tasks do you see?
We are already using AI to create touchpoint maps and detailed descriptions of media consumption. By automatically analyzing user data and behavior patterns, we gain insights into the target group's touchpoints. This helps us identify the right channels and messages to target the audience and maximize the success of campaigns. We may even be able to predict consumption at some point. Furthermore, dynamic and automatically generated message architectures can be created based on onsite and offsite data. Based on these, dynamic AD's can be automatically generated and played out. This makes it possible to have an almost infinite number of different advertising media produced by the AI.
Multi-channeling is another buzzword in media planning. How can AI and multi-channeling be combined?
Indeed, another important step is to use AI in combination with a multi-channel analytics tool like Datorama / Power-BI / Funnel IO. This combination allows us to create customer journey maps and derive conversion funnels. This way, we understand customer interactions across different channels and optimize conversion rates in a targeted way. Keyword: football team and not last-cookie-counts.
Another added value of AI lies in the evaluation of large and complex data sets. Instead of manual analysis, AI algorithms can perform these tasks quickly and accurately. We gain in-depth insights more efficiently and can identify patterns much faster, which we then use as quick wins for dynamic planning. These enable us to respond to opportunities in a timely manner and increase the success of our campaigns. Keyword: dynamic planning.
Where do you see the potential for AI in media planning?
The use of AI also facilitates the development of simple media plans as a basis. Based on the data and analyses obtained, AI provides tailored recommendations for media planning. This can then be used well as a basis for further elaboration. In addition, we use AI to create production plans. The automatic analysis of production data and parameters enables optimized scheduling, which optimizes production processes, reduces costs and increases efficiency.
Do you see any concrete use of AI in any area soon?
We have made first promising attempts to make dynamic planning more precise and efficient with the help of AI and on- and off-site analytics tools. The intelligent analysis of real-time data enables us to continuously optimize our planning processes and achieve better results for our customers. We also need to have an answer to the Cookieless World. As soon as intelligence goes away, it needs more emphasis on conception again and for this we need to rely on other data sources above all. Especially on-site data. This is where AI can help us do some groundwork.
How are you driving the implementation of AI within the agency?
We have currently formed an interdisciplinary working group consisting of Creative, Media, Production, Data, AdOps and Analytics, which is conducting further research under my leadership and pushing targeted projects and gathering experience. Overall, the use of artificial intelligence enables us to make our decisions more free of gut feelings and increase the efficiency of our work. Which then directly benefits our customers. We believe the future is a combination of human expertise and AI technology. Especially with the elimination of the cookie, there needs to be more focus on design again. We are still at the very beginning.
About Mediaplus Suisse
Mediaplus is an innovation agency for the digital age. It offers a broad range of services that go beyond conventional media. With its focus on media, digital, data and content, Mediaplus generates additional added value for its customers. The Mediaplus team supports customers in the development and implementation of media strategies. With creative solutions for offline and online media, including digital and social media, they ensure that advertising messages are effectively communicated to your target groups.
About Nico Podgorny
With over 20 years of agency experience, Nico Podgorny has versatile expertise. Stations include consulting activities and positions as Digital Director at sinka interactive, swissclick and Blue Spirit. He was also a lecturer for online marketing and online advertising at HSLU, MBSZ and Migros Business School. As a founding member and long-time partner of the full service agency The House Agency, he was responsible for consulting and advisory services. In his role as Digital Director and member of the Executive Board at HAVAS, he was able to further develop his skills in the strategic direction of digital campaigns and projects. He is currently Chief Digital and Chief Client Officer at Mediaplus Suisse in the House of Communication Zurich. There he is responsible for the digital strategy and client support. His many years of experience and passion for digital marketing and strategic consulting have shaped his successful career.
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Last September, the market research institute GfK Switzerland conducted the so-called shopping basket study on behalf of Admeira. In order to map the economic relevance of media target groups in German- and French-speaking Switzerland, the study combines two golden standards: the household expenditure of the basket of goods from the Federal Statistical Office (BfS Household Budget Survey) and TV data from Mediapulse. Results of this study, which is unique in Switzerland, are now available for the first time.
"Best Agers" of particular importance
The target group of "best agers" (50-74 years old) is proving to be of particular importance for advertisers. They have high purchasing power and great relevance for consumption. It can therefore be promising to take them into account in media planning, instead of running the risk of unrealized scatter gains with too narrow targeting.
The "Best Agers" influence just under half (46%) of the total shopping basket volume* of the 13 BfS shopping baskets studied in German- and French-speaking Switzerland - they therefore spend above average overall - an enormous potential compared to other target groups. The young target group (15-29 years) performs significantly worse with only 14%, while the target group 20-49 years (household conventional) represents 40% of the population but influences only 18% of the basket spending.
The proportion of "best agers" is growing steadily with demographic change and today already accounts for 31% of the total population (2.7 million inhabitants:in). This group is very open to new developments, lives an individualized lifestyle with high wealth and spends a lot of money.
Highest weekly expenditure per person
In most baskets of goods, "Best Agers" have the highest weekly spending per person: In the total of the 13 baskets of goods examined, they spend 20% more money per person than the representatives of the basic target group 15-74 and more than twice as much as the average representatives of the young target group 15-29. In everyday household spending (food, cosmetics, clothing, but also furniture and package tours) and in the automotive sector, "Best Agers" are clearly ahead in terms of spending volume.
High-quality TV environments are the best way to reach "best agers. In German-speaking Switzerland, the greatest shopping cart potential for this target group - 83% - is reached on SRF 1, which is +41% more than on RTL. In French-speaking Switzerland, 87% of the "Best Ager" shopping cart potential is reached on RTS 1. RTS 2 and TF1 also prove to be the best choice.
Overall, the Admeira shopping basket study shows that it is important for successful media planning to take into account the economic advertising relevance of the target groups, especially for the 50+ media target group newly offered by Admeira with a performance guarantee.
*Cart volume = average weekly reach (number of people) of a media target group x average weekly spend, influenced by one person.
The publication "Purchasing Power-Oriented Media Planning," which provides insights for advertisers, introduces Admeira available for download free of charge.
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