AnomalySonar

AnomalySonar is the martech solution developed by ByTek for the detection of traffic issues due to anomalies on search engines

AnomalySonar is the solution developed by ByTek for the timely detection of traffic anomalies on the website due to search engines issues.

AnomalySonar uses Google Search Console data and processes it to highlight pages, queries and combinations of pages and queries that deviate from their normal behaviour in terms of impressions and clicks.

The solution allows you to quickly identify what is increasing or decreasing in traffic and impressions on your site, thus giving you the opportunity to take early action on your content. The tool also identifies anomalous behaviour that is difficult to track directly from Search Console, such as traffic cannibalization on a query between pages on the same site.

Data sources and solution

AnomalySonar acquires Search Console data via API and analyses it using time series techniques. The analysis is performed separately for pages, queries and combinations of pages and queries, and identifies anomalies in impressions and clicks.

Every time the tool finds a statistically different figure from the one estimated by the forecast models, it highlights it, distinguishing between a negative anomaly (the actual figure is lower than the expected one) and a positive one (the actual figure is higher than the expected one).

The tool is also able to identify keyword cannibalization, i.e. to identify all those situations in which several pages of the same site compete for the same search query and a page suddenly overtakes the page previously visible for that query, thus lowering the site’s performance (e.g. a blog page that ranks better than a product page for a transactional query).

Type of analysis

AnomalySonar reports the results in the form of a dashboard (updated as often as required: weekly, monthly, …) containing the following information:

  1. Plot with number of anomalies detected during the week and divided by:
    1. Type (positive anomalies, negative anomalies);
    2. Type of anomaly (decrease/increase in clicks, decrease in increase in impressions, cannibalization).
  2. Table describing all anomalies detected, in particular:
    1. Date of detection of the anomaly;
    2. Positive or negative;
    3. Type of anomaly (decrease/increase in clicks, decrease in increase in impressions, cannibalization);
    4. Entity on which the anomaly was detected (the page, the query, the combination where the anomaly occurred);
    5. Percentage change in clicks/impressions compared to the previous period;
    6. Average clicks of the entity in the previous period;
    7. Average impressions of the entity in the previous period;
    8. Priority i.e. a weighted index that takes into account all the previous information and indicates which ones are a priority for further checks. The anomalies are ordered by decreasing priority
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