New entries 

With the following figure we show the search phrases that have entered the Top-100 during the observation period:


Open example in the Lab

Compared to the Red Bulls, FC Bayern has more than three times as many new entries in total:

But if we look only at the really high entries, the situation is different:

Open example in the Lab

Schalke04 has the most Top-10 entries and the BVB is ahead of FC Bayern:

Exits

We now want to put the exits opposite it. Here it is noticeable that in the identical period more rankings fell out of the top 100 than came in:

Open example in the Lab

The ratio of exits is roughly the same as the ratio of new entries:

You can hold on to that: Domains that have more top 100 placements have a higher throughput than less performant domains.

Finally, let’s have a look at the exits from the Top-10 for this period of time:

Open example in the Lab

It is interesting to note here that the described proportionality no longer applies. Schalke04 and the Red Bulls lose the lowest number of top rankings, BVB loses the most:

Upward Diffusion

But what about the movements within the top 100? In this context, it is interesting to clarify which portions “work their way up” into the top 10 over what period of time. In the following we take a look at all rankings which were found at the beginning of the observation period from the 2nd search result page onwards and which found their way into the top 10 at the end of the observation period:

Open example in the Lab

Here, too, the above proportionality is reflected: FC Bayern has the most intensive upward movements:

Downward Diffussion

Let us now take a look at the opposing movement. What about the rankings that worked their way down slowly but steadily from the first search result page during the observation period without leaving the top 100?

Open example in the Lab

At this point, too, the BVB stands out with a relatively large number of descenders:

Interpretation of movement data

Based on the movements described above, the following conclusions can be drawn: The BVB loses the most rankings from the top 10 in net terms, whereas Schalke04 only tops the BVB in negative terms with the relatively high number of top 100 exits. On the other hand, the FC Schalke has a relatively high proportion of top 10 entries. The movement patterns of the Red Bulls and FC Bayern, on the other hand, are relatively similar and inconspicuous.

Finally, we would like to look at which search phrases have not changed during the observation period:

Open example in the Lab

Altogether we count in the Top-10 the following stable rankings:

Proportionally the red bulls show the least stable rankings:

  • BVB: 7%
  • The Red Bulls: 3%
  • FC Bayern: 6%
  • Schalke04: 5%

Within the scope of an SEO analysis, it would now be necessary to clarify why the movements are as explained. In principle, there are different reasons for this. For example, the content offered can be of varying actuality. Or Google makes algorithmic adjustments in order to classify and sort out offers that do not match the search phrases meaningfully enough. However, it would also be possible that the content offer is simply not optimally tailored to the needs of the target group.

Conclusion

With this case study we want to show which possibilities exist for a transparent and comprehensible evaluation of presence. However, the study also made it clear where the economic focus lies and how other brands were integrated and perceived.

The case study could only consider a fraction of the aspects that the used database would provide. Please contact us for a detailed or topic-focused dossier.