SEO Reporter

This project is an extremely powerful analyzing tool which combines experience with the data gained from promotion systems, such as Google Search Console (GSC), Onpage, Sistrix and AWR (Advancedwebranking), etc. Our system combines their data and calculates a median which is even more accurate than other systems. Once we worked with a client specializing in banking, and when their bank was in the process of launching a large marketing campaign, they added different analyzing tools to their websites. Having analyzed their data, we viewed the data from various analytics tools to find a different perspective. For example, the data from Google Search Console (GSC) was different to the data from Sistrix , therefore our system calculates a median and provides an extremely accurate value indicator, …and we were pleasantly surprised to not have found such an option at Google before we had created it.

The thing is, Google shows data for the most recent 90 days only, while our SEO-reporter allows you to receive and filter data for an indeterminable time period instead. Moreover, we added some extra widgets with which the client is able to assign the data type for both X and Y axes and build a custom graph, as well as being able to select any of the default widgets.
The fact that the system is able to save the settings, makes it extremely user-friendly , and furthermore, there is a small cheat option hidden, which has the ability to adjust data on the graph and constantly maintain your reports.

In addition, we extended the amount of data by merging different promotion systems. For example, Google allows authorized users to define their language, age, gender, employment and income, as well as other options, while unauthorized users may only define their location. However, a user might be logged into another system, e.g. there are more people using MSN instead of Google for their everyday need , so for those users, we collect the data from MSN analytics and combine them with the data received from Google users. Such an approach allows us to obtain more data and apply various filters, e.g. to find out sales correlations which are based on season and gender/age criteria.