When the same market produces different channelisation figures

Channelisation has become one of the most important measurements in modern gambling regulation. Across Europe, regulators, operators, researchers and policymakers frequently rely on channelisation figures when assessing whether a regulated market is functioning as intended. The concept appears deceptively simple. If most gambling activity takes place with licensed operators, regulation is often viewed as successful. If large numbers of consumers continue to use unlicensed providers, questions naturally arise regarding the effectiveness of the regulatory framework. Yet behind the headline percentages lies a far more complicated reality that receives considerably less attention than the figures themselves.

Germany provides perhaps one of the most interesting examples of this challenge. Since the introduction of the Glücksspielstaatsvertrag 2021, channelisation has become a central reference point in discussions about consumer protection, enforcement effectiveness and market performance. Industry participants frequently cite channelisation estimates when arguing that regulation is working well or when suggesting that significant portions of the market remain outside the regulated sector. What is often overlooked, however, is that different studies frequently arrive at very different conclusions regarding the actual size of the regulated market.

This raises an important question that sits at the centre of the wider debate. Before discussing whether Germany’s channelisation rate is high or low, can the figure itself be measured accurately? Unlike tax revenue, licensing numbers or enforcement actions, channelisation is not a directly observable statistic. It is an estimate that must be constructed using data, assumptions and methodological choices. Those choices can significantly influence the final outcome, meaning two researchers examining the same market may arrive at markedly different conclusions while both following legitimate analytical approaches.

The issue matters because channelisation is increasingly being used as a proxy for regulatory success. The Gemeinsame Glücksspielbehörde der Länder (GGL), operators, trade associations and policymakers all have an interest in understanding whether consumers are being directed towards licensed products where regulatory safeguards can be applied. If the underlying measurement is subject to substantial uncertainty, confidence in the resulting policy conclusions may also be affected. The debate therefore extends beyond gambling and enters the broader field of regulatory measurement, where complex markets are often reduced to a small number of headline indicators.

This article does not attempt to determine Germany’s actual channelisation rate. Instead, it examines the methodologies used to calculate channelisation, the assumptions that underpin those methodologies and the reasons why different approaches can produce dramatically different results. In doing so, it explores whether the debate surrounding channelisation is sometimes less about the final percentage and more about the process used to generate it. A figure that appears precise on paper may in reality contain a significant degree of uncertainty that is rarely communicated to the public.

The question behind the percentage

At first glance, channelisation appears to be a straightforward concept. Most definitions focus on the proportion of gambling activity that occurs within the regulated market compared with activity taking place outside it. The principle is widely used throughout Europe because it provides a simple framework for evaluating whether regulatory objectives are being achieved. If consumers choose licensed operators, regulators can generally enforce responsible gambling requirements, anti-money laundering controls and consumer protection measures. If consumers increasingly migrate towards unlicensed alternatives, those safeguards become more difficult to apply.

The challenge begins when analysts attempt to determine the size of both sides of the equation. Measuring activity within the regulated market is often relatively straightforward because licensed operators submit information through reporting frameworks established by regulators. The position becomes considerably more complicated when attempting to estimate activity occurring outside those frameworks. By definition, unlicensed operators generally do not provide comprehensive reporting data to national authorities. Researchers therefore have to rely on indirect indicators, assumptions and estimation techniques to determine the likely size of the offshore market.

This distinction is critical because channelisation is not measured directly. Nobody can observe the entire German gambling market in real time. Instead, analysts construct models designed to estimate activity that cannot be fully observed. The final percentage therefore reflects not only underlying consumer behaviour but also the assumptions built into the model itself. Two studies may examine exactly the same market conditions while producing different results simply because they rely on different methods of estimation.

The result is that discussions about channelisation often become discussions about methodology. One estimate may be based primarily on consumer surveys. Another may rely heavily on website traffic analysis. A third may incorporate payment data, search behaviour or market intelligence. Each approach captures a different aspect of gambling activity and each comes with its own strengths and limitations. Understanding those differences is essential before drawing conclusions from the final figures.

Why channelisation is harder to measure than many assume

One of the most common misconceptions in the channelisation debate is that the underlying data is readily available. In reality, analysts face a significant visibility problem from the outset. While regulated operators report extensive information regarding player activity, deposits, revenues and compliance metrics, no equivalent reporting framework exists for operators that sit outside the licensed market. This means researchers are attempting to measure a market where a substantial portion of the activity may not be directly observable.

The difficulty becomes easier to understand when compared with other economic measurements. Governments can calculate tax receipts because taxes are reported and collected. Regulators can count licences because licences are issued through formal processes. Channelisation is fundamentally different because one side of the equation often consists of estimated activity rather than recorded activity. The resulting figure therefore depends heavily on the assumptions used to construct those estimates.

This challenge is not unique to Germany. Similar debates have emerged in jurisdictions such as Sweden, the Netherlands and Denmark, where policymakers have sought to understand the extent to which consumers remain active outside regulated markets. In each case, researchers have faced the same fundamental problem. The legal market can often be measured with a reasonable degree of confidence, while the offshore market must largely be inferred from indirect indicators. The uncertainty surrounding those indicators inevitably influences the final result.

A further complication arises from the fact that channelisation itself can be defined in different ways. Some studies focus on revenue, others focus on player numbers and others attempt to estimate overall gambling activity. These distinctions are not always immediately visible when headline percentages are published. Yet they can have a substantial impact on the final figure and the conclusions that observers draw from it.

When different methodologies produce different results

The most significant reason channelisation estimates can vary dramatically is that researchers often use different methodologies to answer slightly different questions. While all studies may seek to estimate the size of the regulated market, the path they take to reach that conclusion can differ considerably. Each methodology highlights certain aspects of consumer behaviour while potentially overlooking others. As a result, two well-constructed studies may arrive at different outcomes without either necessarily being flawed.

One commonly used approach relies on consumer surveys. Researchers ask participants about their gambling behaviour, which operators they use and how frequently they engage with particular products. Surveys can provide valuable insights because they capture information that may not be visible through transactional data alone. They can also help identify consumer awareness of licensed and unlicensed operators. However, survey-based methodologies inevitably depend on the accuracy of participant responses, which introduces a number of potential limitations.

Consumers may not always remember their activity accurately. Some may misunderstand which operators are licensed and which are not. Others may underreport or overreport their gambling behaviour for a variety of reasons. Even when survey design is robust, the results remain dependent on sample selection and participant honesty. These factors do not invalidate survey-based approaches, but they do create margins of uncertainty that must be acknowledged when interpreting the results.

Another frequently used methodology focuses on website traffic. Researchers analyse visitor numbers, user engagement patterns and market share estimates across gambling websites in an attempt to determine where consumers are spending their time online. Traffic data can offer useful insights into consumer interest and brand visibility. It can also provide information regarding the relative popularity of licensed and unlicensed operators. Nevertheless, traffic data alone does not reveal how much gambling activity actually occurs after a user visits a website.

A website visitor may register and deposit funds. Equally, that visitor may leave within seconds without placing a single bet. Some users may visit multiple operators before making a decision, which can lead to duplicated observations across datasets. The relationship between traffic and revenue is therefore not always straightforward. Converting website visits into estimates of gambling activity inevitably requires assumptions, and those assumptions can materially affect the final channelisation figure.

The limits of revenue estimates and market modelling

Many analysts regard revenue-based estimates as one of the more meaningful approaches because they focus on actual gambling expenditure rather than consumer interest alone. In theory, measuring where gambling revenue is generated should provide a clearer picture of market activity than measuring website visits or survey responses. The challenge, however, is that comprehensive revenue data is generally only available for the regulated market. Estimating offshore revenue remains considerably more difficult.

To overcome this problem, researchers often construct models that combine multiple sources of information. Traffic data may be combined with survey findings, payment indicators and publicly available market intelligence. These inputs are then used to estimate likely spending patterns across different categories of operators. While such models can provide useful insights, their outputs remain heavily dependent on the assumptions embedded within them.

Consider two analysts examining identical traffic data. One may assume that a relatively small proportion of visitors ultimately deposit funds. Another may assume a significantly higher conversion rate. Both assumptions may appear reasonable depending on the context and supporting evidence. Yet the resulting estimates of offshore revenue could differ substantially, producing noticeably different channelisation figures despite starting from the same underlying dataset.

This highlights an important reality that is often overlooked during public discussions. Channelisation estimates are not simply measurements. They are frequently modelled outputs generated through a combination of observed data and informed assumptions. The precision of the final percentage can therefore create an impression of certainty that may not fully reflect the complexity of the underlying calculation.

Why assumptions matter more than most readers realise

If methodologies form the foundation of channelisation estimates, assumptions are often the factors that determine the final outcome. Every measurement model requires researchers to make decisions about consumer behaviour, spending patterns and market activity that cannot be observed directly. These decisions are often entirely reasonable and necessary. However, they can also have a significant influence on the conclusions ultimately produced by a study.

For example, a model attempting to estimate offshore gambling activity may begin with website traffic information. The next step is considerably more difficult because traffic must somehow be converted into gambling expenditure. Researchers must decide how many visitors become active customers, how frequently they gamble and how much they typically spend. Small adjustments to any of these assumptions can generate large differences in the final estimate.

The same issue applies to survey-based methodologies. Researchers may receive responses indicating that a certain percentage of consumers have used offshore operators during a given period. The challenge then becomes determining how representative those consumers are of the wider market. Are they occasional users or highly active customers? Do they generate a proportionate share of gambling expenditure or a disproportionately large share? The answers can materially affect the final channelisation calculation.

This does not mean that assumptions are inherently problematic. Every economic model relies on assumptions to some extent. Inflation forecasts, unemployment projections and GDP estimates all involve elements of estimation and judgement. The key issue is not whether assumptions exist but whether they are clearly disclosed, justified and understood by those relying on the resulting figures.

Revenue-based and player-based channelisation are not the same thing

One of the most overlooked distinctions in the channelisation debate concerns the difference between player-based and revenue-based measurements. These approaches are often discussed as though they describe the same phenomenon, yet they answer fundamentally different questions. Failing to distinguish between them can create considerable confusion when comparing studies.

A player-based approach attempts to estimate how many consumers use licensed operators. Under this methodology, the focus is placed on participation rather than expenditure. If a large majority of consumers maintain accounts with regulated providers, the resulting channelisation figure may appear relatively high. Such an outcome could suggest that the regulated market has achieved broad consumer reach.

A revenue-based approach focuses on where gambling expenditure actually occurs. Under this model, a smaller group of highly active offshore customers may account for a significant proportion of overall spending. As a result, revenue-based channelisation can sometimes appear materially lower than player-based channelisation even when both measurements are analysing the same market. Neither approach is necessarily incorrect because each examines a different dimension of consumer behaviour.

This distinction becomes particularly important when channelisation figures enter public debate. A study reporting high channelisation may be measuring consumer participation, while another reporting lower channelisation may be measuring expenditure. Readers unfamiliar with the methodological differences may assume the studies contradict one another. In reality, both may be accurate within the context of the specific question being asked.

What international experience tells us

Germany is not the first jurisdiction to encounter disagreements regarding channelisation measurement. Across Europe, regulators and industry stakeholders have repeatedly debated the true size of regulated and unregulated gambling markets. These discussions provide useful insights because they demonstrate that methodological disputes are often a feature of market measurement rather than evidence of analytical failure.

In Sweden, for example, different estimates of channelisation have periodically generated debate regarding the effectiveness of the country’s regulatory framework. Stakeholders have sometimes relied on differing methodologies to support differing conclusions about consumer behaviour. Similar discussions have emerged in the Netherlands, where policymakers have sought to understand the extent to which offshore operators continue to attract Dutch consumers following market liberalisation. The recurring theme is that measuring activity beyond the reach of formal reporting systems remains inherently challenging.

The international experience also highlights another important point. Channelisation figures are often treated as precise indicators despite being derived from methodologies that involve varying degrees of estimation. This does not mean the figures lack value. On the contrary, they can provide important insights into market trends and regulatory outcomes. However, international experience suggests that they may be most useful when viewed as indicators rather than exact measurements.

Perhaps the most consistent lesson from other jurisdictions is that confidence in channelisation figures tends to increase when methodologies are openly explained. Transparent assumptions, clearly defined definitions and detailed methodological disclosures allow independent observers to evaluate findings more effectively. The quality of the discussion often improves when attention shifts from headline percentages to the process used to generate them.

Transparency alone cannot solve the problem

Calls for greater transparency frequently arise whenever channelisation figures become controversial. There is certainly merit in encouraging greater methodological disclosure. Researchers should explain how estimates are constructed, what assumptions have been applied and which limitations may influence the results. Such disclosures help readers understand the strengths and weaknesses of a particular study.

Transparency, however, does not eliminate the underlying measurement challenge. Even if every assumption were disclosed and every calculation fully explained, analysts would still face the fundamental problem of estimating activity that cannot be directly observed. The offshore market would remain partially hidden from view. Researchers would still need to rely on proxies, indicators and modelling techniques to construct estimates of its size.

This is an important distinction because it shifts the discussion away from individual studies and towards the nature of the problem itself. Channelisation is difficult to measure not because researchers are necessarily making mistakes but because the market being measured is only partially visible. Greater transparency can improve confidence in estimates, but it cannot transform an inherently uncertain exercise into a perfectly precise one.

For policymakers, this reality may have important implications. Decisions regarding regulation, enforcement priorities and market structure are often influenced by channelisation estimates. If those estimates contain unavoidable uncertainty, policymakers may need to focus not only on the numbers themselves but also on the range of possible outcomes suggested by different methodologies. A single figure may tell only part of the story.

Our Conclusion

The debate surrounding channelisation is often presented as a debate about percentages. Discussions frequently focus on whether the regulated market captures a particular share of gambling activity and whether that share is increasing or declining over time. Yet a closer examination suggests that the more important discussion may concern methodology rather than the final number itself.

Channelisation is not a directly observable statistic. It is an estimate constructed through a combination of data, assumptions and analytical choices. Different researchers can analyse the same market using different methodologies and arrive at materially different conclusions without either necessarily being wrong. The differences often reflect the complexity of the measurement challenge rather than flaws in the underlying research.

For Germany, the central question may therefore be broader than whether channelisation is high or low. A more useful question could be how confidently any observer can measure a market that is only partially visible. As long as offshore activity remains outside formal reporting frameworks, uncertainty is likely to remain part of the calculation. Understanding that uncertainty may ultimately be just as important as understanding the percentage itself.

The broader lesson extends beyond gambling regulation. Policymakers increasingly rely on complex measurements to evaluate the success of regulatory frameworks. Organisations such as the European Gaming and Betting Association (EGBA) regularly reference channelisation and related market indicators when discussing the effectiveness of gambling regulation across Europe. Those measurements can provide valuable insights, but they should not be mistaken for absolute certainty. In the case of channelisation, the most important figure may not be the percentage that appears in a headline. It may be the margin of uncertainty that sits behind it.

FAQs

What is the Germany Channelisation Rate?
The Germany Channelisation Rate refers to the estimated proportion of gambling activity that takes place with licensed operators compared to unlicensed providers operating outside the regulated market.

Why is channelisation important in gambling regulation?
Channelisation helps regulators assess whether players are using licensed gambling services where consumer protection, responsible gambling measures and compliance requirements can be enforced.

Can the Germany Channelisation Rate be measured precisely?
No. Channelisation is not directly observable and must be estimated using various data sources, assumptions and analytical models.

Why do different studies report different channelisation figures?
Different studies often use different methodologies, such as consumer surveys, website traffic analysis or revenue modelling, which can produce varying results.

What role does the Glücksspielstaatsvertrag 2021 play in channelisation?
The Glücksspielstaatsvertrag 2021 established Germany’s modern gambling framework and made channelisation a key indicator for evaluating regulatory effectiveness.

How do researchers estimate offshore gambling activity?
Researchers typically rely on indirect indicators such as website traffic, consumer surveys, payment data and market intelligence because offshore operators do not usually report data to German authorities.

What is the difference between player-based and revenue-based channelisation?
Player-based channelisation measures how many consumers use licensed operators, while revenue-based channelisation measures where gambling expenditure actually occurs.

Why are assumptions important in channelisation studies?
Assumptions influence how researchers convert limited data into market estimates. Small changes in assumptions can significantly affect final channelisation figures.

Do other European countries face similar channelisation challenges?
Yes. Countries such as Sweden, the Netherlands and Denmark have also experienced debates regarding how channelisation should be measured and interpreted.

What is the main takeaway from the channelisation debate?
The key issue is often not the final percentage itself but the methodology used to calculate it and the uncertainty that exists behind the published figures.