Post by account_disabled on Dec 24, 2023 8:49:17 GMT
Abearing in mind that the approach is transposable to sequences of events products etc. The first step is to define the objectives of sequence data processing and then to outline a procedure for the automatic extraction of sequences of interest. Objectives identify and exploit subsequences of interest As Data Scientists at AT Internet our role is to design solutions that automate the extraction of information and assist our users in their analyses.
Since our data model allows us to define objectives financial or not on websites we decided to use it as a variable of interest. In this context we aim to Identify navigation subsequences in relation to Phone Number List conversion rate and highlight these relationships. Annotate the presence of such subsequences in the customer journeys and use them as predictive variables in a machine learning model. For example a website records a conversion rate of but we find that visits to pages A B and then C generate a conversion rate of . The sequence ABC has an associated conversion rate higher than the average conversion rate.
It can therefore be considered as a subsequence of interest. Data sets and modelling Identifying data sets First of all we need to give some context to our navigation sequence data Each sequence is annotated as successful or unsuccessful. We work with sequences of pages visited for example Home Product Conditions of sale the sequences are deliberately reduced when the conversion pages are reached in order not to introduce a bias. In the end you get a dataset like this one user sequence conversion Homepage Product Privacy Contact false Product Product Product Return Policy true Product false Such data sets are often imbalanced. The group that does not convert is often in the majority they frequently represent more than of visitors.
Since our data model allows us to define objectives financial or not on websites we decided to use it as a variable of interest. In this context we aim to Identify navigation subsequences in relation to Phone Number List conversion rate and highlight these relationships. Annotate the presence of such subsequences in the customer journeys and use them as predictive variables in a machine learning model. For example a website records a conversion rate of but we find that visits to pages A B and then C generate a conversion rate of . The sequence ABC has an associated conversion rate higher than the average conversion rate.
It can therefore be considered as a subsequence of interest. Data sets and modelling Identifying data sets First of all we need to give some context to our navigation sequence data Each sequence is annotated as successful or unsuccessful. We work with sequences of pages visited for example Home Product Conditions of sale the sequences are deliberately reduced when the conversion pages are reached in order not to introduce a bias. In the end you get a dataset like this one user sequence conversion Homepage Product Privacy Contact false Product Product Product Return Policy true Product false Such data sets are often imbalanced. The group that does not convert is often in the majority they frequently represent more than of visitors.