Visitor Profiler

The Visitor Profiler analyses your e-shop’s visitors and categorizes them into a set of predefined groups in order to inform about the composition and behaviour of their visitors and customers. This module is based on the web tracking data that have been previously collected by the Data Collector and Consolidator module. These data is mainly based on the Google Analytics (and also Piwik) digital footprint of the eshop. The digital footprint connection is carried out by means of the ID View (Tracking) code, previously included in the registrarion form.

A registered E-COMPASS cockpit user can navigate through the visitor profiler features by clicking on the “Visitor Profiler” link on the top navigation bar.

Overview

The system classifies visitors into four different types:

• Loyal: Visit more pages than average, they make purchases four times more often than average.
• Need-Based: Low page visits; they are new 75% of the time and often come to the site from Google. Low purchase and stay time ratio.
• Wandering: Stays for quite a long period of time (7 minutes), and visits an amount of pages close to the “Loyal” visitor. However, is usually new (70%) and makes fewer purchases than the “Loyal” visitor.
• Misplaced: Stays in the site for a very short period of time (8 sec) and visits a small amount of pages. They make no transactions at all.

Once you are in the Visitor Profiler page, the first step is to select the time period to analyze, that is, the Start Date and End Date to filter the analyzed data, as shown in the figure at right.

After that, you can select among the five facilities of this module and press the button "submit" to start a specific analysis. These facilities are described below:

• Typologies by origin (option by default)
• Typologies over time
• Typologies over time %
• Focus by origin
• Focus by typology

Typologies by origin

In this analysis, the distributions of visitor's categories (Loyal, Misplaced, Need-Based, and Wandering) are plotted by means of bar graphs with regards to their origins. The origin can be selected by: Continent, Country or Region, although a filter by region per country/continent is also possible to filter only regions of a given country of interest. Each group of bars represents a given country/continent/region for which, the number of visits (concerning categories) can be observed by moving the mouse on. As shown in the two following figures, the complete bar chart can be plotted "Grouped" or "Staked" (respectively) to see a different perspective of visit distributions.

For instance, it can be observed that a higest percentage of visitors from Oceania are Misplaced, whereas Loyal visitors in this continent are around 10%.

Typologies over time

If you select the option “Typologies over time”, a line graph will appear plotting the typologies of visitors (categories) and their absolute numbers over time, through the time period set.

In this sense, this facility allows focusing on a specific point of chronology and better and more personalized way limiting the analysis. The graph below enables to focus on a time subperiod zomming the region of interest. It also offers the ability to view the complete historic of the different profiles. Thus we can observe the evolution of them over time. Therefore, it can be contrasted with the data if the customer loyalty strategies are bearing fruit over time. For example, the time period between dates 2015-10-28 and 2015-11-02 shows a peak in visits with an increasing number of Misplaced visitors. Ideally, the number of Loyal visitors is also high this date, with regards to the remaining days.

Typologies over time %

In case of selecting the option “Typologies over time %”, a similar graph to the previous one will appear although focused on percentage values of the four categories of visitors. The exact percentage for each day will emerge when moving the mouse on the graph, as shown in the figure below.

This graph also allows three different visualizations: Stacked, Stream and Expanded, which can be selected by clicking on labels at top-left to show three different perspective of calculated data.

Focus by origin

This option will generate a sector chart of visitor’s categories with regards to their geographical location. The region/country of origin is selected from those available for the e-shop in the time period previously selected. For instance, you can see the proportion of Loyal visitors with regards to Misplaced ones in Spain.

Focus by typology

Finally, the option “Focus by typology” generates a sector chart of origins: region, country and continent, indicating the proportion of visits for each tracked location. The focus in this case is the visitor’s category. In the figure example, Loyal visitors are organized in sectors by country, where we can see how Greece register the highest amount of this kind of customers.