Visitor Profiler - Revision history

GarciaNieto: /* Focus by typology */

2016-01-07T16:38:50Z

‎Focus by typology

GarciaNieto: /* Focus by origin */

2016-01-07T16:27:06Z

‎Focus by origin

GarciaNieto: /* Typologies over time % */

2016-01-07T16:18:18Z

‎Typologies over time %

GarciaNieto: /* Typologies over time % */

2016-01-07T15:58:32Z

‎Typologies over time %

GarciaNieto: /* Typologies over time */

2016-01-07T15:58:04Z

‎Typologies over time

GarciaNieto: /* Typologies over time */

2016-01-07T15:56:51Z

‎Typologies over time

GarciaNieto: /* Typologies over time */

2016-01-07T15:49:45Z

‎Typologies over time

GarciaNieto: /* Focus by origin */

2016-01-07T15:10:43Z

‎Focus by origin

GarciaNieto at 15:10, 7 January 2016

2016-01-07T15:10:08Z

GarciaNieto at 15:05, 7 January 2016

2016-01-07T15:05:17Z

← Older revision		Revision as of 16:38, 7 January 2016
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−	~~If selected~~, ~~you can choose one~~ of ~~the visitor categories~~ and ~~a geographical accuracy to display~~ the ~~distribution~~ of ~~visitor origin within~~ the ~~selected group~~	+	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.

	[[File:focus-by-typology.png\|800px]]		[[File:focus-by-typology.png\|800px]]

@@ Line 56: / Line 56: @@
 == Focus by origin ==
-With this diagram you can additionally filter the customers by a location in addition to the visitor clusters
+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.
 [[File:focus-by-origin.png|800px]]

← Older revision		Revision as of 16:18, 7 January 2016
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	== Typologies over time % ==		== Typologies over time % ==
−	~~This view shows~~ the ~~visitor typologies in~~ percentage values. The graph allows ~~for~~ three different ~~visualisations. It seems that more or less either loyal or other visitor types’ numbers keep constant 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.

@@ Line 47: / Line 47: @@
 == Typologies over time % ==
-This view shows the visitor typologies in percentage values. The graph allows for three different visualisations.
+This view shows the visitor typologies in percentage values. The graph allows for three different visualisations. It seems that more or less either loyal or other visitor types’ numbers keep constant over time.
 [[File:typologies-over-time-percent.png|800px]]

@@ Line 44: / Line 44: @@
 [[File:typologies-over.time.png|800px]]
-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. Nevertheless, it seems that more or less either loyal or other visitor types’ numbers keep constant over time.
+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 % ==

@@ Line 40: / Line 40: @@
 == Typologies over time ==
-This graph shows the different typologies of visitors and their absolute numbers 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.
 [[File:typologies-over.time.png|800px]]
-In this sense, the tool allows focusing on a specific point of chronology and better and more personalized way limiting the analysis. 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 eshop's Loyal style visitor is predominant followed by Misplaced style visitors. In addition, it seems that more or less either loyal or other visitor types’ percentage keeps constant over time
+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 example eshop's Loyal style visitor is predominant followed by Misplaced style visitors. In addition, it seems that more or less either loyal or other visitor types’ percentage keeps constant over time
 == Typologies over time % ==

← Older revision		Revision as of 15:10, 7 January 2016
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	With this diagram you can additionally filter the customers by a location in addition to the visitor clusters		With this diagram you can additionally filter the customers by a location in addition to the visitor clusters

−	~~With this diagram you can additionally filter the customers by a location in addition to the visitor clusters~~

	[[File:focus-by-origin.png\|800px]]		[[File:focus-by-origin.png\|800px]]

← Older revision		Revision as of 15:10, 7 January 2016
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	[[File:typologies-over.time.png\|800px]]		[[File:typologies-over.time.png\|800px]]
		+
		+	In this sense, the tool allows focusing on a specific point of chronology and better and more personalized way limiting the analysis. 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 eshop's Loyal style visitor is predominant followed by Misplaced style visitors. In addition, it seems that more or less either loyal or other visitor types’ percentage keeps constant over time

	== Typologies over time % ==		== Typologies over time % ==