What is the reason for some data gaps where values of performance measures don’t match when searching criteria is changed from one dimension to another dimension?


Google search console data representation has its own limitations that can be seen here. Some of the highlights are as follows

  • To protect user privacy, Search Analytics doesn't show all data. For example, we might not track some queries that are made a very small number of times or those that contain personal or sensitive information.

  • Some processing of our source data might cause these stats to differ from stats listed in other sources (for example, to eliminate duplicates and visits from robots). However, these changes should not be significant.    

Therefore, the results of brand queries are not equivalent to the overall results. As overall results provide an aggregate of all queries while brand queries (branded and non branded) reflect the aggregate of tracked queries and omit the performance (clicks, impressions, CTR, position) of untracked queries. In the dashboard, the following scheme is used while presenting different visualizations. 

  • Overall Results 

    • All Features + All Queries

  • Specific Feature 

    • Feature +All Queries

  • Specific Feature Brand Query (Branded/NonBranded) 

    • Feature + set of tracked queries

  • Overall Brand Query (Branded/NonBranded)

    • AllFeatures + set of tracked queries

The users of the dashboard don’t need to memorize or handle the above information while extracting and filtering the information. The above information and the following example is provided to clarify the gap between different data segmentations.

Example:

This example illustrates the possible data gaps for filter “Brand Query”  as shown in Figure 1. In this filter, three options can be seen such as  All_Queries, Branded and NonBranded. Apparently, the aggregate of Branded and NonBranded should be equal to All_Queries which is not applicable due to the reason given above. In Figure 2, Figure 3 and Figure 4, performance of Branded, NonBranded and All_Queries is illustrated respectively. 


Figure 1: Brand query filter in the dashboard


Figure 2: Branded query filter in the dashboard




Figure 3: NonBrand query filter in the dashboard



Figure 4: All_Queries query filter in the dashboard


It can be clearly inferred from the above figures that data gaps exist between various filter types as shown in Table 1.


Table 1: Summary of data gaps for brand query filter

Brand Query

Clicks

Total

Difference

Branded

6903

7776

2872

NonBranded

873

All_Queries

10648

10648