Web Analytics Maturity Model

Web Analytics Maturity Models are used as auditing mechanisms, defining strategic pillars of one’s web or digital analytics practice. They are integral for building, benchmarking, and improving digital analytics strategies.

In order to assess Web Analytics performance a benchmark is necessary.  Stéphane Hamel has performed extensive research around this topic and who I believe has one of the best models.  One can perform the analysis and produce the visual results using his model through the Cardinal Path website.

The models help to visualize where an organization is at present and set goals for the future. One can walk through a set of introspective questions whose results can be mapped to create a visual output that helps to easily communicate maturity. Michael Notte, author of Kaizen Analytics and Web analyst at a major European bank, provides examples of how to accomplish this task with Stéphane’s Maturity Model.  

The benefit of this analysis is the creation of standardized levels of maturity. The model itself provides an efficient way to communicate results of the audit.

 

Models:
Stéphane Hamel -             http://immeria.net/oamm/WAMM_ShortPaper_091017.pdf
Cardinal Path (Hamel's) -   http://www.cardinalpath.com/oamm/assessment/
Brent Dikes -  (his L3PS model is not public yet)
Bill Gassman -                  http://www.gartner.com/DisplayDocument?doc_cd=159097
Avinash Kaushik -             http://www.kaushik.net/avinash/web-analytics-maturity-structure-models-process/
Uniltyics -                        http://www.unilytics.com/maturity_model.shtml

Articles:

http://www.cmo.com/web-analytics/web-analytics-maturity-model-success 
http://activemetrics.wordpress.com/tag/web-analytics-maturity-model/
http://www.kaizen-analytics.com/2011/11/web-analytics-in-practice-your-online.html 

Videos:
http://online-behavior.com/emetrics/web-analytics-maturity-model

 

My Sugestions for Model Improvements

 

Full Size Web Analytics Maturity Model Infographic PDF

Web Analytics Maturity Model

 

Type

Level 0

Level 1

Level 2

Level 3

Level 4

Champion

None

Project Manager

Director

Senior management

CXOs

Distribution

None

Beginning

Within team

Across departments

Entirety of organization

Goals

Undefined

Task list

Online channel

Business optimization

Organization optimization

Scope

Ad Hoc

HiPPO

Refined to sector

1 online property

Business ecosystem

Resources

None dedicated

1 part time

1 full time

A team dedicated

Multidisciplinary team

Methodology

None

individual, Internal

Created by team

Internal - External

Agile

Tools

No Web Analytics

Out of Box

Custom Dashboards, Testing

Segmentation, Alerts/notifications

CRM, channel, behavioral,FP&A, predictive

 

Notes on the levels:

0
out of the box tools & reports
lacking formal training
ad hoc reporting

1
Resources Limited
Streamlining commencing
Reports stop at director level
Limited optimization
Success anecdotal

2
KPIs defined
Multidisciplinary team in place
Competitive data
Voice of customer
Social media
Mobile
Multivariate testing
Optimizations - channel
Personas defined

3
Online and offline correlations
Optimization - complete processes
Persuasion scenarios (waiting definition)
Continuous improvement/optimization process

4
1 or more executive advocates analytics
Predictive modeling
complex optimization techniques
analytics across multiple business units/functions

5
Strategic insight
Continuous improvement
Skilled resources
Top management commitment
Continual Testing + learning
Beyond online channel

 


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