Attribution marketing in 2025: beyond the first-click and last-click models
Marketing attribution in 2025: beyond first-click and last-click models
Buying journeys have become more complex, especially in Switzerland, where consumers interact across multiple languages and devices. Traditional attribution models, such as first-click or last-click, are no longer sufficient to analyze these multilingual and multi-step journeys. Here's why:
- Limited insight: These models ignore intermediate interactions, essential for understanding purchase decisions.
- Poor budget allocation: Focusing on a single touchpoint biases marketing resource allocation.
- Compliance and data protection: New Swiss laws (LPD) impose restrictions on user tracking, making these models even less reliable.
Modern alternatives: multi-touch models
To better reflect the reality of customer journeys, advanced attribution models, such as data-driven or U-shaped models, distribute credit among multiple touchpoints. They are more accurate, especially in multilingual markets like Switzerland.
Why choose these models?
- Comprehensive analysis of interactions.
- Optimized budget allocation.
- Compliance with data protection laws.
AI and marketing attribution
Artificial intelligence allows real-time data analysis, cross-device tracking, and automatic strategy adjustments, even in a multilingual environment. It is an essential solution to maximize campaign efficiency while respecting user privacy.
Comparative table: Attribution models
| Model | Strengths | Limitations |
|---|---|---|
| First-click | Simple, low cost | Partial view, ineffective for complex journeys |
| Last-click | Simple, low cost | Ignore intermediate steps |
| Multi-touch | Comprehensive analysis, better accuracy | Higher complexity and cost |
| AI-driven | Real-time, automatic adjustment | Requires advanced infrastructure |
In 2025, Swiss companies must move away from simplistic models to adopt more suitable solutions, such as multi-touch models and AI, to remain competitive while adhering to local regulations.
Issues with first-click and last-click models
How first-click and last-click attribution models work
The first-click model attributes all conversion credit to the very first point of contact with a potential customer. Conversely, the last-click model gives all credit to the last point of contact before the purchase.
Let's take an example: a prospect discovers your brand through a Facebook ad. Later, they purchase a product after clicking on a Google Ads ad. With the first-click model, Facebook gets all the credit. With the last-click model, Google Ads is valued.
Although these approaches may seem logical – one emphasizing the importance of discovery and the other of final action – they oversimplify often complex buying journeys. And that's precisely where their main problem lies.
Main issues with first-click and last-click models
One of the major flaws of these models is their inability to reflect the complexity of modern buying journeys. In Switzerland, for example, where consumers frequently switch between national languages (French, German, Italian) throughout their decision-making process, these models quickly show their limitations.
They ignore intermediate interactions, yet essential in the customer's journey. Focusing solely on the first or last touchpoint gives a partial and biased view of the customer journey.
This simplified view can lead to poor marketing budget allocation. Some crucial channels may risk being underfunded, reducing the overall campaign effectiveness. This becomes even more problematic in a context where privacy regulations, such as the GDPR or the LPD, impose restrictions on user tracking. These constraints can make certain touchpoints invisible, further limiting the relevance of these models.
Comparative table: First-click, Last-click, and advanced attribution models
| Criterion | First-Click | Last-Click | Advanced Models |
|---|---|---|---|
| Implementation complexity | Very simple | Very simple | More complex |
| Deployment cost | Low | Low | Higher initial investment |
| Precision for short journeys | Correct | Correct | Very high |
| Precision for long journeys | Limited | Limited | Optimal |
| Management of multilingualism | Inadequate | Inadequate | Optimized |
| Partial | Partial | Integrated | |
| Budget optimization | Risk of poor allocation | Risk of poor allocation | Optimized allocation |
| Team training time | Short | Short | More substantial |
| Cross-channel visibility | Low | Low | Complete |
| Real-time adaptation | No | No | Yes |
This table highlights the limitations of first-click and last-click models while emphasizing the advantages of advanced attribution models. These allow for a more comprehensive analysis and more efficient management of marketing campaigns, especially in complex environments like Switzerland.
How AI Is Making Marketing Attribution (Finally) Easy | The Ecommerce Coffee Break Podcast
Models that work better
Multi-touch attribution models have become a relevant response to the limitations of traditional approaches. By attributing credit to multiple touchpoints, these models better reflect the complexity of modern buying journeys. Today, 75% of companies adopt this method, surpassing first-click and last-click models [1][2][3].
The idea is simple: distribute conversion credit among different touchpoints to better understand the contribution of each channel. Let's take an example in Switzerland: a customer may discover a brand through a French ad, visit a German website for more information, and then finalize their purchase after receiving an email in Italian. This multi-touch approach is particularly useful in a multilingual environment.
According to a study, 60% of marketers consider data-driven attribution essential for analyzing high-potential customer journeys [3]. Moreover, the global market for marketing attribution software, estimated at $3.1 billion in 2021, is expected to reach $12.9 billion by 2031 [3]. These figures show how crucial these tools are becoming. Let's now look at the different types of multi-touch models and their applications.
Different types of multi-touch attribution models
There are several variants of these models, each tailored to specific goals.
- Linear model: This model assigns equal credit to each touchpoint in the customer journey. For example, if a prospect interacts with four channels before converting, each channel will receive 25% of the credit. This method is ideal for campaigns focused on awareness or products with long sales cycles, as it recognizes all marketing efforts.
- Time decay attribution: Here, the most recent interactions receive more credit. For example, the first interaction may be assigned 5% of the credit, while the last one, close to conversion, can receive up to 50%. This model reflects consumers' tendency to give more importance to recent information.
- U-shaped model: Also known as position-based, it assigns 40% of the credit to the first and last touchpoints, and distributes the remaining 20% among intermediate interactions. This model highlights the importance of initial discovery and final conversion, while acknowledging intermediate steps.
- Data-driven attribution: This approach uses machine learning algorithms to analyze historical data and determine the actual impact of each touchpoint. It adapts to the company's specifics and consumer behaviors, offering unparalleled accuracy.
Comparative table: Multi-touch attribution models
| Model | Credit distribution | Strengths | Limitations | Ideal use case |
|---|---|---|---|---|
| Linear | Equal credit for each interaction | Simple to use, recognizes all channels | Does not reflect the actual impact of each touchpoint | Awareness campaigns, long sales cycles |
| Time decay | More credit to recent interactions | Takes into account the recency of interactions | Undervalues early touchpoints | Short campaigns, quick conversions |
| U-shaped | 40% to first and last points, 20% in the middle | Balance between discovery and conversion | Sometimes neglects intermediate interactions | Journeys with well-defined steps |
| Data-driven | Customized distribution via algorithms | Precise analysis tailored to the company | Complex, requires a lot of data | Companies with multiple interactions |
For Swiss companies, the choice of model largely depends on the complexity of their customer journey and the available resources. For example, a luxury watch SME in Geneva might opt for the U-shaped model to value discovery and conversion. On the other hand, a tech company in Zurich, facing complex sales cycles, might prefer data-driven attribution for more precise analysis.
Implementing these models requires a robust infrastructure. This includes data collection through UTM parameters, JavaScript tracking codes, and suitable software, all centralized in a CRM for effective interaction analysis.
Using AI-driven attribution for real-time data
With the multi-touch approach, AI now provides instant and accurate analysis of the customer journey.
Artificial intelligence transforms marketing attribution by processing data in real-time and adjusting strategies based on observed behaviors. Unlike traditional models based on historical data, AI offers immediate responsiveness, crucial in a dynamic market like Switzerland, where consumers interact across multiple languages and digital channels. With its capabilities, AI identifies complex patterns in these multilingual interactions, laying the groundwork for continuous optimization.
What AI-driven attribution tools enable
AI-driven attribution tools push the boundaries of traditional methods with advanced analysis capabilities. With machine learning, they detect subtle correlations between different touchpoints, even when data is fragmented.
They also excel in cross-device tracking, linking interactions across various devices. For example, AI can recognize that a user browsing a site in German from their phone in Zurich is the same person making a purchase in French on a computer in Geneva a few days later.
Predictive optimization is another key asset. By analyzing historical data, algorithms predict which channels or sequences generate the best conversions. For example, if a prospect views technical content in German before moving to pricing pages in French, AI can adjust attribution to reflect this specific sequence.
Dynamic segmentation allows adapting attribution to different customer profiles. A B2B buyer in Zurich will not behave the same as a consumer in Geneva, and AI creates specific models for each segment.
Finally, AI excels in analyzing micro-conversions. These small actions, like downloading a brochure or subscribing to a newsletter, often overlooked by traditional models, offer valuable insights into campaign effectiveness.
Benefits for Swiss companies
Swiss companies derive tangible benefits from AI-driven attribution, especially in a multilingual and multicultural context.
With real-time analysis, they can adjust their campaigns immediately. A company based in Lausanne, for example, might discover that a French LinkedIn campaign performs better in the morning, while its German version performs better in the afternoon. AI detects these trends and automatically adjusts content delivery.
The multilingual capabilities of AI help unify customer journeys, even when navigating between content in French, German, or Italian, providing a clear overview despite linguistic diversity.
Data protection compliance is also a strong point. Modern tools meet the requirements of the Federal Data Protection Act (LPD) by using anonymization techniques and encrypted identifiers to analyze journeys without compromising user privacy.
Finally, automated budget optimization maximizes return on investment. AI reallocates budgets to the most performing channels, taking into account local specificities, such as consumption differences between urban and rural areas.
EWM SA applies these solutions to offer Swiss clients tailored marketing optimization while ensuring strict compliance with local regulations. By fully leveraging AI capabilities, these tools help achieve specific goals while adapting to the Swiss market's particularities.
Attribution compliant with data protection in Switzerland
Managing personal data has become a major issue for marketing attribution, especially in Switzerland, where rules are tightening.
With Swiss consumers increasingly sensitive to privacy, companies must adjust their practices to comply with laws while ensuring the effectiveness of their campaigns. Adopting a privacy-respectful approach is no longer just a legal constraint: it is also a way to strengthen customer trust. Here are the key principles to consider.
Swiss regulations to know
The Federal Data Protection Act (LPD), revised and enforced since September 1, 2023, imposes strict standards for processing personal data. Inspired by the European GDPR, it retains specificities unique to Switzerland.
- Explicit consent: Companies must obtain clear and detailed agreement before collecting data. Generic cookie banners are no longer sufficient; the exact use of data for analyzing customer journeys must be precisely explained.
- Data anonymization: Personal information such as email addresses or phone numbers must be encrypted or replaced with untraceable technical identifiers.
- Right to erasure: Users can demand complete deletion of their data. This implies that companies must have systems capable of locating and removing all data associated with a person.
- Data minimization: Only strictly necessary information should be collected. For example, to measure the impact of a campaign, knowing an age range may be sufficient rather than the exact age.
- Transparency: Users must be clearly informed about tracking and attribution practices through accessible and understandable privacy policies.