- WebTrends Digital Marketing Matutiry Model (DM3) (Web analytics vendor)
- Gartner’s Maturity Model for Web Analytics (Market analyst/Information technology)
- Capability Maturity Model Integration (CMMI) from the Software Engineering Institute at Carnegie-Mellon University (Academic/Software engineering)
- The Data Warehousing Institute Business Intelligence Maturity Model (Association/Business intelligence)
- Stratigent Marketing Analytics Model (Web analytics consulting)
- Competing on Analytics maturity by stage, by Thomas Davenport in "Competing on Analytics: the new science of winning" (Academic)
Previous posts in the WAMM series:
- Overview of the Web Analytics Maturity Model
- Definition of Web Analytics
- Components of the Web Analytics Maturity Model
In their model, WebTrends consider the first "pillar" to be the most important one, and most pillars are further sub-divided.
- Measurement strategy: A measurement strategy is in place and aligned with business objectives.
- Analytics resources and domain expertise: The number of employees dedicated to measurement and analysis, resources allocation and problem resolution skills.
- Data integration and visualization: Establish correlations between multiple data sources and communication of insight through the organization, presentation and delivery of data.
- Data analysis and insight: Skills to turn web-based data into insight.
- Adoption and governance: Role-based training, change management, security, data consistency and quality and process-driven governance.
- Ongoing optimization: A continuous improvement process to identify opportunities and test various optimization scenarios is in place.
More info: WebTends Digital Marketing Maturity (DM3)
Image credit: WebTrends
This model focuses on the responsibility to pick the right solution (or set of solutions) for the stated business goals. It is acknowledged that organizations can demonstrate a level of maturity for some tasks while not necessarily mastering everything at a specific level. These levels are merely guidelines to set goals and analyze the gap between current and desired state. Although being very pragmatic and straightforward, it addresses only one dimension of a successful analytical organization: the web analytics solution technologies and their usage sophistication.
In its DM3 model, reviewed later, WebTrends criticize the Gartner model as failing to "address social media, SEM and other digital marketing measurement channels". However, looking beyond the specifics of each level, managers should understand the concepts and level of sophistication demonstrated at each level, and therefore, easily acknowledge social media, SEM, mobile and such are covered at "Level 4 - Collaborative" of the Gartner model.
More info: Gartner Maturity Model for Web Analytics
Image credit: Gartner
CMMI was originally developed in 1989 as a tool for objectively assessing the ability of contractors’ processes to perform a contracted software project.
The main point of CMMI is the objective evaluation of the “ability to perform” and as been applied to many areas beyond technology and engineering, notably risk management and business process optimization.
- Ad hoc (chaotic): Typically undocumented and in a state of dynamic change, tending to be driven in an ad hoc, uncontrolled and reactive manner by users or events.
- Repeatable: Some processes are repeatable, possibly with consistent results. Process discipline is unlikely to be rigorous, but where it exists it may help to ensure that existing processes are maintained during times of stress.
- Defined: Sets of defined and documented standard processes established and subject to some degree of improvement over time. These (as-is) standard processes are in place and used to establish consistency of process performance.
- Managed: Using process metrics, management can effectively control the actual process. In particular, management can identify ways to adjust and adapt the process to particular projects without measurable losses of quality or deviations from specifications.
- Optimizing: Focus is on continually improving process performance through both incremental and innovative technological changes/improvements.
More info: Capability Maturity Model Integration (CMMI)
The TWDI model addresses the business intelligence maturity, a term coined by Gartner analyst Dresner in 1989 as the "set of concepts and methods to improve business decision making by using fact-based support systems".
- Prenatal – Management Reporting: Standard set of generic reports distributed without discrimination for actual needs. Inflexible and hard to modify, users tend to bypass the established solution.
- Infant – Spreadmarts: Individual, disconnected spreadsheets and desktop solutions with their own set of data, metrics and rules that are not aligned with the organization. Although they offer a low cost and locally controlled solution, they prevent management from getting a clear and consistent picture of the organization.
- Child – Data Marts: All knowledge workers are empowered with timely information and insight. Data is shared and standardized at the department level, offering a standard set of application, business data and metrics.
- Teenager – Data Warehouse: Definitions, rules and dimensions are standardized across the organization and deeper analysis is available through interactive reporting and analysis. Queries crosses functional boundaries and the datawarehouse becomes a tactical tool to improve process efficiency across the whole value chain, contributing to a fact-based decision making culture.
- Adult – Enterprise Data Warehouse: There is now a single version of the truth, data becomes an asset as important as people, equipment and cash. Scorecards and dashboards contribute to align every worker with the corporate strategy. ROI becomes positive and new, unexpected ways of using this knowledge emerge as a competitive asset.
- Sage – BI Services: Data is open to customers and suppliers, extending the value chain beyond the corporate boundaries. Knowledge workers don’t have to switch context to analyze data since the data, information and insight is embedded into operational applications and contribute to decision engines (think of fraud detection, behavioural targeting, and automated applications). Business Intelligence becomes ubiquitous and value increase exponentially.
More info: The Data Warehousing Institute Business Intelligence Maturity Model
Image credit: The Datawarehouse Institute
- A clear evolution of web analytics practices exists, and the progress of an organization can be ranked into one of several categories.
- Internal education and adoption of analytic techniques are leading indicators of how quickly companies build competitive advantages in analytics.
- Large budgets and big teams do not always correlate to success.
- Foundation Building: Focus on implementing web analytics. Limited executive support and no significant ROI. The availability of data spurs interest and builds momentum across the organization.
- Customization: The technology is customized to meet business-specific requirements. More relevant data spur adoption, drive ROI and secure funding for future investments.
- Optimization: Multivariate testing is introduced to gain additional insight about customer preferences and improve marketing program performance. Online data integration and the emergence of a continuous improvement process provide additional insight and set the stage for the next phase.
- Predictive Modeling & Targeting: Online and offline data are integrated to offer a complete view of the customer, allowing to deliver a highly targeted message for customers
- A model can provide the vision, framework, and tactics necessary to put in place a web analytics program and develop a competitive advantage
- Shows how web analytics can benefit marketing departments and contribute to a culture of analysis and insight
- Provides a framework to demonstrate how the technology contributes to the organization’s capability to compete with analytics.
Image credit: Stratigent
- Analytically impaired
- Localized analytics
- Analytical aspirations
- Analytical companies
- Analytical competitor
More info: "Competing on Analytics: The New Science of Winning"
For further information regarding the WAMM and its future evolution, including speaking, consulting and training, visit the Web Analytics Maturity Model area on immeria.net.
Coming up next: The Web Analytics Maturity Model!