1.
Bubble

1.
Bubble

Profit (Sum) Product Category 400k 800k 1.2m 0 20k 40k 60k 80k 100k 120k 140k 160k Sales (Sum) What is the relationship between Profit and Sales by Product Category? Clothing Electronics Fitness & Outdoors Food Crafts

A bubble visualization lets you add a third or fourth
column of data to a typical x-y graph. Scaled circles
(bubbles) are used to indicate other layers of data.

Use cases:

  • Segment your products on a sales and profit chart
  • Create perceptual maps to position products
    (such as good/bad, high/low)


P r o fit ( S um) P r odu c t C a t ego r y 400k 800k 1.2m 0 2 0k 40k 60k 80k 100k 1 2 0k 140k 160k S a les ( S um) W h a t is t h e r el a tionship b e t w e en P r o fit a n d S a les b y P r odu c t C a t ego r y ? Cl o thi n g E le c t r onics F it n e s s & Ou t doors F ood C r a f ts
What drives Satisfaction? Satisfaction Predictive Strength Type of Travel is a predictor of Satisfaction 34% Drivers Strength 15% 14% 14% Age Range Age 34% Type of Travel Airline Status

2.
Spiral

2.
Spiral

What drives Satisfaction? Satisfaction Predictive Strength Type of Travel is a predictor of Satisfaction 34% Drivers Strength 15% 14% 14% Age Range Age 34% Type of Travel Airline Status

2.
Spiral


The spiral visualization, which is unique to Watson Analytics, reveals what is driving data outcomes. It can be used for predictive analytics. The nearer the driver to the center, the stronger its influence.

Use cases:

  • Find out what drives sales (such as price, advertising spend, location)
  • In a customer satisfaction analysis, discover what issues are negatively affecting retention

3.
Network

3.
Network

What are the connections between Department and Position? Warehouse Manager Warehouse AssistantManager RegionalManager Warehouse Worker Franchise Distribution Manager Production and Distribution Customer Service Representative Customer ServiceManager Customer ServiceCoordinator Customer Service

A network visualization shows connections within your data. It is built on nodes (column items) from which lines project to reveal connections.
The weight (shade or thickness) of a line suggests the strength of a connection.

Use cases:

  • Map organizational infrastructure and understand
    how personnel and departments are connected
  • In marketing, use to identify similar buyers—or
    buyer interests—among selected target groups
What are the connections between Department and Position? Warehouse Manager Warehouse AssistantManager RegionalManager Warehouse Worker Franchise Distribution Manager Production and Distribution Customer Service Representative Customer ServiceManager Customer ServiceCoordinator Customer Service
Quarter Navigation Packs Tents First aid Lanterns Binoculars Watches Woods Tools Q1 2012 Q1 2013 Q2 2014 Q2 2012 Q3 2013 Q3 2014 Q4 2012 Q4 2013 Product type What are the values of Revenue for Product type and Quarter? 161,84 K 97,72 K Revenue

4.
Heatmap

4.
Heatmap

Quarter Navigation Packs Tents First aid Lanterns Binoculars Watches Woods Tools Q1 2012 Q1 2013 Q2 2014 Q2 2012 Q3 2013 Q3 2014 Q4 2012 Q4 2013 Product type What are the values of Revenue for Product type and Quarter? 161,84 K 97,72 K Revenue

4.
Heatmap


A heatmap is like a shaded spreadsheet. Instead of numbers appearing in cells, each cell is shaded to represent a value or a value range. The lower the value, the lighter the shade. The higher the value, the darker the shade.

Use cases:

  • Highlight main revenue-generating products contained
    in a large volume of data
  • Insert into presentations to allow easy comprehension
    of data without reference to numbers

5.
Map

5.
Map


A map visualization is like a heatmap, except it attaches intensities to geographic regions—for example, continents or countries. Different regions are shaded from dark to light to indicate values such as population densities.

Use cases:

  • Create data maps that reveal activity only relevant to the areas of your business
  • Spot under-performing sales territories and understand local conditions that might be affecting results
https://www.youtube.com/watch?v=0DSNdLDOZ5w&feature=youtu.be Tijuana Munich Sao Luis San Francisco Sofia Miami Dallas Abu Dhabi Tokyo Toronto Paris Budapest Bucharest Phoenix Kiev Nashville Athens Cairo Perth Atlanta Vienna Chicago Kansas City Madrid Minneapolis Montreal Oxford Amman Hong Kong Hamburg Calgary Istanbul Seattle Helsinki Rome Stockholm Johannesburg Cape Town Jerusalem Las Vegas Berlin Hamburg Winnipeg Nagoya Sao Luis Philadelphia Beijing Zurich Manchester Venice Oslo New York Valencia Fukuoka Casablanca Halifax Seoul Amsterdam New York Bogota London Lyon Barcelona Sydney Caracas

6.
Word Cloud

6.
Word Cloud

https://www.youtube.com/watch?v=0DSNdLDOZ5w&feature=youtu.be Tijuana Munich Sao Luis San Francisco Sofia Miami Dallas Abu Dhabi Tokyo Toronto Paris Budapest Bucharest Phoenix Kiev Nashville Athens Cairo Perth Atlanta Vienna Chicago Kansas City Madrid Minneapolis Montreal Oxford Amman Hong Kong Hamburg Calgary Istanbul Seattle Helsinki Rome Stockholm Johannesburg Cape Town Jerusalem Las Vegas Berlin Hamburg Winnipeg Nagoya Sao Luis Philadelphia Beijing Zurich Manchester Venice Oslo New York Valencia Fukuoka Casablanca Halifax Seoul Amsterdam New York Bogota London Lyon Barcelona Sydney Caracas

6.
Word Cloud


A word cloud is a visualization of words sized according to a value in your data. The greater the value associated with the word, the larger it appears in a word cloud.

Use cases:

  • For digital marketing, discover the best performing keywords used across online campaigns
  • In a customer satisfaction analysis, collate online comments to find recurring words that reveal how the public responds to your products or services

7.
Decision
Tree

7.
Decision
Tree

What is a predictive model for Case Call Duration (mins)? (Predictive strength: 62,7%) CaseArea Access/Login Hardware Software System CaseType Issue Request CasePriority Unassigned High Agent Training Level Agent Training Level NoTraining NoTraining MinimalTraining SufficientTraining SufficientTraining All data

A decision tree reveals the influencers on particular outcomes. Identify existing patterns to help predict future outcomes.

Use cases:

  • Identify the drivers that lead to a successful outcome—for example, a sale
  • Explore customer satisfaction patterns and predict likely outcomes based on key decision points on the decision tree
What is a predictive model for Case Call Duration (mins)? (Predictive strength: 62,7%) CaseArea Access/Login Hardware Software System CaseType Issue Request CasePriority Unassigned High Agent Training Level Agent Training Level NoTraining NoTraining MinimalTraining SufficientTraining SufficientTraining All data

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