Success Story: E-Guide for ENYAQ and ELROQ

Performance data after six months

The evaluation of all eight parts over the first six months shows a clear pattern: the e-guide is one of the most stable, highest quality and best performing formats in the channel.

Like ratio (quality indicator)

• No part below 99%

• Majority of parts: 99.5%

• Individual parts reach 99.8%

• Benchmark for ‘very high approval’ on YouTube is approx. 96%

The e-guide is 3–3.5 percentage points above the upper quality band, which is unusually high for a long-form format.

Views and usage

• Cumulative > 650’000 views

• All eight parts show relatively even distribution of views (no typical series decline)

• Parts 1–8 range between 35’000 and 65’000 views per part, depending on relevance and seasonality

• Constant views over months, no short-term ‘peak and decay’

Usage corresponds to the behaviour of ‘permanently relevant’ knowledge products, not seasonal videos.

Playback duration and retention

• Average playback duration between 8 and 15 minutes, depending on the part.

• Percentage view duration consistently ~35–40%, despite some long running times.

• Several parts achieve well over 40% view time for videos of 20 minutes.

For long-form explanatory videos, this is above the platform average.

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 searches, recommendations by the algorithm and high relevance.

YouTube Search: High percentages per part → Users actively search for answers that the format offers.

YouTube Suggestions: Stable average percentages → Algorithm recognises content connectivity.

Browse Features (home page, subscription feed): Regular double-digit percentages → YouTube recommends the E-Guide as ‘reliable content’ on the home page.

External: between 10–20% depending on the part → The e-guide is embedded, linked and shared externally.

Google Search: up to 82.4% of external traffic → Google identifies the videos as the leading thematic answer.

This distribution corresponds exactly 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

In the first six months, the e-guide generated:

• 1300 comments,

• 400 emails with specific questions or feedback,

• several dozen 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:

• Numerous purchasing decisions have been made or confirmed based on the information provided.

• Vehicle owner satisfaction is increasing because they are able to correctly understand functions and system limitations.

• 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)

• Systematic orientation instead of fragmented individual information

• Significant reduction in uncertainty when using the vehicle

• Reproducible classification of displays and errors

• Improved use in winter, when travelling and when charging

• Less frustration thanks to transparent expectation management

Prospective buyers

• Realistic assessment of vehicles before purchase

• Comparison of marketing claims with real behaviour patterns

• Structured preparation for configuration and use

• Reduction of cognitive load in the decision-making process

Manufacturers and retailers (indirect)

• Easier vehicle handover

• Side effect: more stable customer expectations

• Better classification of known error patterns

• 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:

• very high approval rating

• above-average retention

• strong search and recommendation structure

• long-term relevance

• 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.

You can view all parts on YouTube here.