Matthias SpeicherPerformance data after one year
The evaluation of all eight parts over the first year shows a clear pattern: the e-guide is one of the most stable, highest quality and best performing formats in the channel. Figures as of July 2026.
Like ratio (quality indicator)
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99.6% positive ratings across all parts
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Benchmark for ‘very high approval’ on YouTube is approx. 96%
The e-guide sits around 3.6 percentage points above the upper quality band, which is unusually high for a long-form format.
Views and usage
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Cumulative 983’000 views, of which 729’000 on the German and 254’000 on the English channel
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All eight parts show an even distribution of views, with no typical series decline
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Per part between 73’000 and 160’000 views across both languages, depending on relevance and seasonality
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Plus 54’000 views of the German and 22’000 of the English playlist
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Constant views over months, no short-term ‘peak and decay’
Usage corresponds to the behaviour of ‘permanently relevant’ knowledge products, not seasonal videos.
Watch time
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Around 112’000 hours of total watch time, of which around 88’000 hours on the German channel
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An average of just under seven minutes of watch time per view
Longevity
A year after release the series still draws around 1’370 views a day, roughly 1’090 of them on the German channel. The e-guide sustains itself without new promotion.
Traffic sources: depth rather than surface reach
The distribution of traffic sources shows that the e-guide does not rely on random traffic, but on active searching.
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On the German channel, depending on the part, 67 to 77% of external traffic comes from Google search, averaging around 73%.
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On the English channel it is 35 to 63%, averaging around 51%.
Google identifies the videos as the leading thematic answer. This distribution corresponds to the pattern of a high-quality, functional knowledge product that solves specific problems.
Direct engagement and behavioural impact
In addition to quantitative performance data, the e-guide shows another level of impact that cannot be derived from platform metrics. It arises from direct user interaction and demonstrable changes in decision-making and usage behaviour.
Direct engagement
The e-guide continuously generates comments, emails with specific questions and individual conversations in private chats. This form of user interaction is a strong indicator of relevance. Users invest time, describe their situations precisely and actively seek personal exchange. In analysis, this is referred to as intent-driven, high-intensity engagement, which only arises when content addresses real problems and is perceived as trustworthy.
Behavioural Impact
The e-guide directly influences user behaviour:
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Numerous purchasing decisions have been made or confirmed based on the information provided.
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Vehicle owner satisfaction is increasing because they are able to correctly understand functions and system limitations.
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Product retention is increasing because misinterpretations and frustrations are reduced.
This level of impact is only evident in formats that go beyond providing information and actually offer guidance. The e-guide fulfils this role: it makes complex systems usable and measurably changes behaviour in everyday life and during the decision-making phase.
Impact on users, interested parties and the market environment
The figures clearly show that the e-guide addresses three levels of impact.
Users (everyday use)
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Systematic orientation instead of fragmented individual information
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Significant reduction in uncertainty when using the vehicle
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Reproducible classification of displays and errors
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Improved use in winter, when travelling and when charging
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Less frustration thanks to transparent expectation management
Prospective buyers
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Realistic assessment of vehicles before purchase
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Comparison of marketing claims with real behaviour patterns
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Structured preparation for configuration and use
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Reduction of cognitive load in the decision-making process
Manufacturers and retailers (indirect)
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Easier vehicle handover
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Side effect: more stable customer expectations
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Better classification of known error patterns
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Systematic explanation of complex dependencies
The E-Guide structurally closes a gap that neither manufacturer communication nor classic reviews cover.
Conclusion
The E-Guide shows how a methodically developed format works in a complex technological environment:
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very high approval rating
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high total watch time
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strong search and recommendation structure
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long-term relevance, a year after release
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evident benefits for users, buyers and, indirectly, OEMs
The data proves that the e-guide not only works, but is also one of the strongest content products within the entire project.