reporting kpi's with chernoff faces by super analytics

Chernoff’s Faces Generator

How to create a simplistic yet powerful data visualization engine that generates complex data in

Chernoff’s Faces style.

Purpose of this Document

This document is a series of studies, whitepapers, presentations, instagrams, and other forms of publications in which the Super Analytics team seeks to find new / old smart and even crazy as yet innovative ways to report performance or to visualize data. Prior to making this document we wrote a blog post at www.superanalytics.fi/blog about the Chernoff’s faces We hope you’ll enjoy! J Best, Kalle Heinonen Super CEO @super_analytics

What is Chernoff’s faces (wikipedia)

Chernoff faces, invented by Herman Chernoff, display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation. The idea behind using faces is that humans easily recognize faces and notice small changes without difficulty. Chernoff faces handle each variable differently. Because the features of the faces vary in perceived importance, the way in which variables are mapped to the features should be carefully chosen (e.g. eye size and eyebrow-slant have been found to carry significant weight)

Example of Chernoff’s Faces

Getting started

The ideology behind the simplistic Chernoff’s Faces Engine (CFE) is quite straight forward. Each of the ‘facial indicators’ are generated by their predefined min-max locations on x-y graph. At first we will need to define the variables or the facial indicators.

Facial indicators (Variables)

Eyes = Sales increase /decrease in % Gaze = EBITDA increase /decrease in % Eye-brows = Customer Retention % Ears = Customer Acquisition Cost Nose = Market Share Mouth = Brand Awareness increase /decrease Head = Employees

Variable Values = Eyes

Eyes = Sales increase /decrease in % Sales  

Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Variable Values = Gaze

Gaze = EBITDA increase /decrease in % Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Variable Values = Eye-brows

Eye-brows = Customer Retention % Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Variable Values = Nose

Nose = Market Share % Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Variable Values = Ears

Ears = CAC (Customer Acquisition Cost) Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Variable Values = Mouth

Mouth = Brand Awareness increase Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Variable Values = Head

Head = Employees Excellent  

Good  

Sa/sfactory  

Poor  

Bad  

2  

1  

0  

-­‐1  

-­‐2  

Sample Chernoff-KPI

The sample Chernoff-KPI on the right is based on the 5-level threshold-driven character engine. Each set of 5 characters is defined for each KPI individually. As a combination these form holistic performance report, with a grin.

Pretty much everything is down. The market share and amount of employees remains high, yet all other fronts are performing poor. Sales & EBITDA is going down.

Super Analytics CFE applied into realistic perspective.

We will create a simplistic comparison of Apple Inc and Nokia Corporation to form an Analysis of their Competitive Positioning via Market Share, Growth and Turnover in FY2006 and FY2012.

NOKIA CORPORATION APPLE INC.

Competitor Analysis (Apple vs. Nokia 2006)

Gro

wth

Market Share

HI

LO HI

HI

Nokia  =  $41bn  

Apple  =  $19bn  

Competitor Analysis (Apple vs. Nokia 2012-2013)

Gro

wth

Market Share

HI

LO HI

HI

Apple  =  $156bn  

Nokia  =  $39bn  

What else could be done with the Super Analytics CFE?

Customer Experience

Measurement

Marketing Campaign Happiness

Customer Satisfaction

Analysis

Employee Satisfaction

Analysis

Marketing Channel Analysis

Production Quality

Analysis

Marketing Channel Analysis (Bought vs. Earned Media)

Ret

urn

on In

vest

men

t

Cost Per Acquisition

HI

LO HI

HI

Earned  media  

Bought  media