Thursday, September 24, 2009

PostRank Analytics

Couple of days ago I was solicited to take a look at a new offering by PostRank called... PostRank Analytics!

The service is pretty clever and offers insight into your blog performance by merging social media measurement and quantitative data. The graph bellow shows an overview with timeline, indicators when you posted on your blog, and overall stats. Clicking on one of the post indicator leads to a more detailed report.

Learning how your content is performing with your audiences, improving your blogging, and developing relationships with your readers should be as important to you as to larger media outlets. But web analytics itself, such as Google Analytics, isn't adressing very well the social media aspect of your blog performance.

This detailed report shows individual posts with indication about the relative PostRank Engagement index, page views, time on page, and number of mentions in several social media outlets such as Twitter, Digg, Delicious, Reddit, etc. For individual posts, you also get all Twitter mentions.

Basically, PostRank Analytics allow you to better identify when, where, and how your audiences are engaging with your content, not only in the last week or month, but also today. It will help you find readers on all the important social hubs where they consume, organize and share content. You can not only see overviews of your audience engagement activity, you also see it as it happens, enabling better connection for timely conversation.

My take

A very interesting service worth giving a try. With so many data sources and different ways to look into social media and general web analytics data, PostRank Analytics provide an easy to use and pretty complete picture. Even if only $9/month, my worry is the number of clients willing to pay for a service that gathers data readily available for free from different sources, and particularly when so many bloggers are relying on a collection of free tools. Still, the insight and visualization is pretty useful, and serious bloggers should at least give it a try for a month (for free!).

Monday, September 21, 2009

Getting started in web analytics: career advice

I often get asked about tips & tricks to get started in the web analytics field. This post is a summary of various resources to get started.

Education or training?

Education is about gaining knowledge and competencies, such as mastering the concepts of statistics, marketing and technology in order to truly understand what web analytics is all about. Training, on the other end, is more about acquiring specific skills, such as hands on learning of how to use Google Analytics. Generally, employers will look for trained people, while employees will seek education as a way to advance their career. The balance between the two is important to increase and sustain your value in the market.

Getting started

There is no magic trick here. Do your homework! Read, learn, try. Network, get involved, share. Nowadays you can get started very easily and quickly. Google Analytics as certainly democratized the web analytics industry, but don't be a fool and think this is enough. Web analytics is not about the tool you use, it's about a whole lot more! Check out the Web Analytics Maturity Model for more insight on all aspects of web analytics. Expertise and experience can only be gained through time and expanding your horizons.

Getting out of the catch 22

A frequent issue for those starting is the field is finding a way to gain hands on experience while not being employed. Without access to a playground to put your newly acquired knowledge and skills at work, it's pretty hard to move on. Not enough experience to find a job, no job to gain experience. How do you get out of this catch 22?
  1. Your own site. Create a blog or a small site about something of interest to you. This is relatively easy and can be free. On the positive side, you have total control and you can touch on the three main dimensions of web analytics: marketing & business strategy, technology and analytics. The challenge here is to have enough traffic to make it interesting!
  2. Get involved with a non-profit or a local site. There are plenty of them, and they will generally accept some help. With local sites, the challenge might be the limited traffic and relative simplicity of the site, but it's a start.
  3. Crowdsourced analytics. the Web Analytics Association is working on a project that would allow WAA members and UBC students to volunteer work on a large-scale non-profit organization. More to come...

    Ressources to get started in web analytics

    • Google Analytics training: several GAACs (Google Analytics Authorized Consultant) are offering training
    • Vendor training: visit your vendor's website for information about product-specific training
    Social & networking events
    1. Web Analytics an Hour a Day, Avinash Kaushik
    2. Advanced Web Metrics with Google Analytics by Brian Clifton
    3. Always Be Testing by Bryan Eisenberg
    4. Check my full bookshelf for additional readings
      Forums & others

      Finding a job in analytics

      If you have other good starting resources to suggest, let me know!

      Friday, September 18, 2009

      Web Analytics Maturity Model: Case Study of European Car Manufacturer

      In this case, the WAMM was used to assess the current “state of the union” and as an effective communication and change management tool at an European car manufacturer.

      The ecosystem consists of nearly 50 different versions of its web sites, each one characterized by specific marketing activities, car models, cultural aspects and local legislations. The group also manage other sites such as media, B2B, social presence and a typical corporate presence. With a multitude of visitor personas, business objectives, success events and supporting metrics and KPIs, web analytics plays a crucial role in providing insight to more than a dozen stakeholders. In the competitive automotive market, and especially during hard economic times, web analytics is used as a competitive asset.
      We are using the WAMM as support for developing a Web Analytics roadmap and strategy. It is a long road to implement culture change in a large organization - especially when you are on the IT side. It already triggered interest; that is a good start!
      Web Project Manager & Web Analytics Specialist.

      Think globally, act locally

      While local agencies handle the localized aspect of online/offline marketing and advertising activities, the head office group serves as an internal agency responsible for the hosting, content management and web analytics services. The group also provides the design, site templates, global content and assets as well as online tools such as the Car Configurator and the Dealer Locator.

      From a marketing measurement perspective, the group provides two important services:
      1. Measure the marketing performance against business objectives defined at the global level – improve and optimize the content and tools provided by the group.
      2. Provide the NSMCs with personalized metrics so they can optimize their own activities at the local level.

      WAMM Assessment

      This organizational structure is common in the automotive industry and other global organizations. Objectives and Scope are high, while Management commitment and general adoption of analytics still have to be demonstrated. The team strive to handle multiple jobs at the same time and because of that, although the web analytics tool is good and quite sophisticated, it can’t be fully leveraged.

      Web Analytics is mostly perceived as an IT project servicing online marketing needs, particularly Search Engine Optimization and online campaigns.

      As mentioned previously, the TME team is using the WAMM to define and communicate goals, create a sense of urgency, demonstrate the complexity and requirements that will lead to the best outcomes.

      To move from the actual “analytically initiated” state to the desired “analytically operational” maturity state, this organization will need to:
      1. First, identify a senior executive who will act as a champion, someone with strong political skills, a stakeholder with some versed interest to make things happen for web analytics.
      2. Secondly, the team will need to be extended, either by hiring skilled personnel to fill the knowledge and expertise gap on the business, technological or analytical dimensions, or better still, bring current employees who have demonstrated interest for analytics.
      3. Lastly, the analytical process will be defined and owned by the newly formed team, with approval and support from the manager, and communicated to stakeholders.
      From this point on, proper definition of objectives & scope will lead to an appropriate use of the technology and not only facilitate the continuous improvement of online processes, but also a growth in analytics maturity.

      About this organization

      This car manufacturer oversees the wholesale sales and marketing of two brands, parts and accessories, and manufacturing and engineering operations. Operations in Europe are supported by a network of several localized marketing and sales companies across most European countries, several thousand  sales outlets, and manufacturing plants.

      For further information regarding the WAMM and its future evolution, including speaking, consulting and training, visit the Web Analytics Maturity Model area on

      Coming up next: other case studies!

      Wednesday, September 16, 2009

      Adobe + Omniture + WPP = game changer

      Twitter was on fire!

      A short post to share my thoughts. I won't go into a long rambling about it.

      Some deep thinking and speculation from all horizons. For the most part, people didn't see it coming... why did Adobe purchase Omniture? To make Flash tagging easier?

      That’s not the point.

      This picture clearly says what it's all about:
      Adobe + Omniture means they will be looking at the whole value-chain process of developing website content and applications. This is far beyond making it easy to tag Flash!

      Also, don't forget the huge strategic agreement with WPP. Add up Adobe + OMTR + WPP and you’ve got a major player who can change and steer the industry.

      Thursday, September 10, 2009

      Web Analytics Case Study: Quebecor Media - Canoe

      This is the first case study of using the Web Analytics Maturity Model in the field and a couple of others will be published in the coming days.

      In this case study, the WAMM is used to highlight the amazing accomplishment Quebecor Media realized over the past three years.

      When Simon Rivard joined Quebecor Media as Vice-President of Marketing in November of 2006, he didn’t only bring over fifteen years of marketing experience, he also brought a strong belief for online marketing analytics. Quebecor now had a web analytics champion on board, the first and most critical success factor for a successful web analytics program.

      Rivard is responsible for the marketing strategy of over 200 websites ranging from news portal to music & bookstore to auto and home classifieds, dating, as well as newspapers and TV stations. Deploying a web analytics solution, let alone educating everyone to the importance of data driven decision making, was a daunting task:
      Evolving the web analytics practice within such a large organization requires time and a good understanding of the critical success factors, otherwise risks of failure are significant. At Canoe, we intentionally planned the work over a three years timeframe to allow enough time for each required ingredient (knowledge, technologies, process, etc.) to be defined, understood and owned. Trying to skip some steps revealed to be a mistake and we had to step back. After all, implementing web analytics is a classic example of change management involving a business culture shift.
      Simon Rivard
      Marketing Vice President
      Quebecor Media
      While some managers welcomed the move with enthusiasm, there was also some skepticism and passive resistance, questioning and challenging the strategy. Communication and education, as well as sensibility for political issues were key. But sometimes putting the foot down to impose a decision was also required. For example, vendor selection and the deployment strategy were sensible topics. At this scale, free vs paid played an important role, but there were also other factors such as the ability for the solution to grow from “initiated” to “integrated” and eventually, “competitive”. Several deployment strategies were considered, but the decision to start with a globally standardized minimal implementation proved to be the right tactic.

      WAMM Assessment

      Simply put, three years ago the organization was just getting initiated to analytics, trying “stuff” here and there without coordination or specific plans. As is often the case in large organizations, there was already an untapped pool of talented and skilled personnel. They lacked coordination and a favorable environment to create a critical mass and move beyond local, specific tactical initiatives.

      Moving from “analytically initiated” to “analytically integrated” required lots of efforts, but the accrued knowledge is now influencing decisions at the CXO level. The work isn’t over yet. Two multidisciplinary teams, one in Toronto and the other in Montreal, are handling the configuration, service and analysis for hundreds of websites and stakeholders. Gradually, as each internal client becomes more educated and aware of the benefits of web analytics, responsibilities are delegated and the multi-disciplinary teams keep evolving into ever more complex and sophisticated analytics.

      About Quebecor Media Inc.

      Quebecor Media Inc. is a communications company with operations in North America, Europe and Asia. Quebecor Media owns operating companies in numerous media-related businesses, including: cable, newspapers, television network, a number of specialty channels, a network of English- and French-language Internet properties, magazines and book publishing, production, distribution and retailing of cultural products, DVD and console game rental and retail chain.

      For further information regarding the WAMM and its future evolution, including speaking, consulting and training, visit the Web Analytics Maturity Model area on

      Coming up next: other case studies!

      Friday, September 4, 2009

      Web Analytics Maturity Model

      Now that I have offered a definition of Web Analytics, explained what is a Maturity Model and we have reviewed six existing models, it's time to look at the Web Analytics Maturity Model itself!

      Previous posts in the WAMM series:
      1. Overview of the Web Analytics Maturity Model 
      2. Definition of Web Analytics 
      3. Components of the Web Analytics Maturity Model
      4. Review of Maturity Models
      "Experience is what you get when you did not get what you wanted."
      Randy Paush, known for "The Last Lecture" (must see video) and professor at Carnegie Mellon University

      From "impaired" to "competitive"

      Remember "maturity levels" defines an evolutionary plateau toward achieving a mature process. In the studied models, the number of levels ranges from four (DM3) to six (TDWI) and uses qualitative terms such as “Initial”, “Chaotic”, “Ad hoc”, “Pre-natal” and “Aware” up to “Optimizing”, “Sage” or “Pervasive”.

      For the proposed model, each Key Process Area can be graded on a scale from “1 – Analytically impaired” through “5 – Analytical competitor”, as summarized below:
      1. Analytically impaired: Characterized by the use of out of the box tools & reports, limited resources lacking formal training (hands on skills) and education (knowledge). Web Analytics is used on an ad-hoc basis and is of limited value and scope. Some tactical objectives are defined but results are not well communicated and there are multiple versions of the truth (side note: just think of the challenge so many analysts face when they have to define what is a "visitor", and why clicks-through metrics provided by their ad network don't match their Google Analytics campaign metrics) .
      2. Analytically initiated: Working with metrics to optimize specific areas of the business (such as marketing). Resources are still limited but the process is getting streamlined. Results are communicated to various business stakeholders (often director level). However, web analytic might be supporting obsolete business processes and thus, be limited in its ability to push for optimization beyond the online channel. Success is mostly anecdotal.
      3. Analytically operational: Key Performance Indicators and dashboards are defined and aligned with strategic business objectives. A multidisciplinary team is in place and use various sources of information such as competitive data, voice of customer, and data from social media or mobile analytics. Metrics are exploited and explored through segmentation and multivariate testing. The Internet channel is being optimized, personas are being defined. Results start to appear and be considered at the executive level. Results are centrally driven, but broadly distributed.
      4. Analytically integrated: Analysts can now correlate online and offline data from various sources to provide a near 360° view of the whole value chain (see “Limits of web analytics” note). Optimization encompasses complete processes, including back-end and front-end. Online activities are defined from the user perspective and persuasion scenarios are defined. A continuous improvement process and problem solving methodologies are prevalent. Insight and recommendations reach the CXO level.
      5. Analytical competitor: This level is characterised by several attributes of companies with a strong analytics culture:

        1. One or more senior executives strongly advocate fact based decision making and analytics
        2. Widespread use of not just descriptive statistics, but predictive modeling and complex optimization techniques
        3. Substantial use of analytics across multiple business functions or processes
        4. Movement toward an enterprise level approach to managing analytical tools, data, and organizational skills and capabilities.
      The last two levels are gradually leaving the realm of “web analytics” to enter that of “business analytics”, as defined by Davenport in "Competing on Analytics".

      Limits of web analytics

      There is a distinction to make between “web analytics” and other disciplines such as “analytics” and Customer Relationship Management (CRM). Too often, management and practitioners expect web analytics to work like CRM or other core business systems. For example, online sales as provided by web analytics solutions should not be used as a valid representation of financial figures for the purpose of accounting. They do not aim and do not account for cancellations, errors and returns.

      The table below contrasts some elements of web analytics and customer relationship management:

      Web AnalyticsCustomer Relationship Management
      Individuals accuracyBased on cookies and other techniques, sometimes authenticated1:1 – Authenticated most of the time
      System typeAnalyticalOperational and analytical
      AccuracySampling and margin of errorClose to 100% accurate

      Can you think of other contrasting elements between web analytics and other disciplines such as BI or CRM?

      Key Process Areas

      Key Process Areas are similar to “critical success factors” (CSF), a term initially used in the world of data and business analysis. It identifies the elements that are vital for a strategy to be successful. Most organizations have perceived Web Analytics as a technological tool for solve problems in individual areas such as online marketing. But those initiatives are largely characterized by a lack of coordination and structured methodology. Unsurprisingly, Web Analytics Critical Success Factors are not that different from those of any other strategy involving strong commitment and cultural business changes, be it Customer Relationship (CRM), Business Intelligence (BI) or Process Optimization (ex. SixSigma) programs: human factor, processes and technology needs be addressed.

      Changing the corporate culture, employee behaviour and business processes, is certainly the most difficult and risky part of any major organizational change. By its nature, developing an analytical culture is an iterative and continuous learning process. While data analysis can contribute to business improvement by answering pending questions and validating hypothesis, they also lead to further questioning and new hypothesis to explore.

      Because web analytics touches on aspects of marketing, BI and process optimization, among others, it is easy to recoup some commonly identified CSFs and reveal the following items:
      1. Management, Governance and Adoption
      2. Objectives Definition
      3. Scoping
      4. The Analytics Team and Expertise
      5. The Continuous Improvement Process and Analysis Methodology
      6. Tools, Technology and Data Integration
      The first element is unambiguously and unanimously the most critical factor in most of the reviewed models. Case studies (to be posted later) offers several examples of the importance of Management, Governance and Adoption. To be successful, executives must recognize web analytics is more than a reporting system and represents an effective way to identify weak points and improvement opportunities. They must perceive analytics (not just web analytics) as a mission critical and competitive resource that can empower each department. Sophisticated use of analytics can contribute to three key elements of a successful organization:
      • Efficient and effective execution.
      • Smart decision making.
      • Optimized business processes.
      However, we must stress that web analytics, as commonly thought of, is nothing more than a tool providing indicators and metrics. Making sense of them requires knowledge and expertise, as highlighted in the next section.

      Common Features

      Common features are attributes that indicate whether the implementation and institutionalization of Key Process Areas is effective, repeatable and lasting. The following common features are identified:
      • Commitment: Is the level of commitment from the organization appropriate and defined by organizational policies, structure and management sponsorship?
      • Resources: Are resources to accomplish the task readily available: tools, training & education, organizational structure and people?
      • Process: Are the roles and procedures to perform each activity defined, tracked and adapted if necessary? Are recommendations implemented and reviewed to contribute to the overall learning process?
      • Reporting and Analysis: Are out-of-the-box reports used or complex multiple-data-source regression analysis conducted to provide customized Key Performance Indicators and dashboards?
      • Tools: What are the tools, their features and capabilities, is their use optimal and effective?
      • Quality: Are mechanisms in place to insure the data being collected is adequate and of good quality? Are reports, insights and recommendations audited to insure their quality over time?

      Key Practices

      Each Key Process Area is described in terms of key practices that contribute to satisfying the goals. The key practices describe the means and activities that contribute most to the effective implementation and institutionalization of the key process area. They describe “what” is to be done.

      Key process areas of the Web Analytics Maturity Model include:
      • Data collection methodologies (log files, tags, network probes, etc.) and data modelization
      • Reporting & Analysis
      • Problem resolution techniques
      • Defining Key Performance Indicators and Dashboards
      • Communication
      • Exploration and visualization tools methods
      • A/B and Multivariate testing
      • Personalization and behavioral targeting
      • Predictive analytics
      • Process analysis and modeling

      For further information regarding the WAMM and its future evolution, including speaking, consulting and training, visit the Web Analytics Maturity Model area on

      Coming up next: conducting a Web Analytics Maturity Model assessment and several case studies!

      Tuesday, September 1, 2009

      Review of Maturity Models

      This is a long post part of the Web Analytics Maturity Model (WAMM) series. This time, I'm looking into six maturity models from various domains:
      • 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)
      Those models will serve as inspiration to develop the Web Analytics Maturity Model.

      Previous posts in the WAMM series:
      1. Overview of the Web Analytics Maturity Model 
      2. Definition of Web Analytics 
      3. Components of the Web Analytics Maturity Model
      For more information visit the Web Analytics Maturity Model area on

      WebTrends Digital Marketing Maturity Model (DM3)

      The DM3 model, announced in the spring of 2009, claims to offer a "framework and objective criteria to determine the sophistication of an organization’s measurement and analysis skills, staffing and best practices across multiple digital marketing disciplines, from web sites to social media". The proposed model defines six core areas, each one graded on a four level scale.

      In their model, WebTrends consider the first "pillar" to be the most important one, and most pillars are further sub-divided.
      1. Measurement strategy: A measurement strategy is in place and aligned with business objectives.
      2. Analytics resources and domain expertise: The number of employees dedicated to measurement and analysis, resources allocation and problem resolution skills.
      3. Data integration and visualization: Establish correlations between multiple data sources and communication of insight through the organization, presentation and delivery of data.
      4. Data analysis and insight: Skills to turn web-based data into insight.
      5. Adoption and governance: Role-based training, change management, security, data consistency and quality and process-driven governance.
      6. Ongoing optimization: A continuous improvement process to identify opportunities and test various optimization scenarios is in place.
      Still in beta, this model certainly needs further refinement. The sub-division and ratings are based on strong experience but lack empirical approach and supportive evidence. The desire to push for an industry standard is certainly good but the actual approach centers heavily around this vendor consulting services. As others have criticized, the spider-chart is "making an assumption that certain groups of activities come together" and "gives the impression that adjacent pillars are somehow correlated because the area that is formed by joining their scores depends on both scores and doesn’t give each one visual independence of the other".

      More info: WebTends Digital Marketing Maturity (DM3)
      Image credit: WebTrends

      Gartner Maturity Model for Web Analytics

      This model was first published in 2008 and is aimed at "CIOs and other IT and business leaders responsible for the strategic success of their organization's Web optimization strategy". In this model, Gartner analyst Bill Gassman reiterates the fact "the problem tends to be a lack of skills and processes rather than issues with the tools themselves".

      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

      Capability Maturity Model Integration (CMMI)

      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.
      1. 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.
      2. 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.
      3. 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.
      4. 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.
      5. Optimizing: Focus is on continually improving process performance through both incremental and innovative technological changes/improvements.
      CMMI has been criticized as being "overly bureaucratic and promoting process over substance" and being a "classical engineering approach that does not take under consideration numerous human cognitive, organizational, and cultural factors essential for the success of every project". Those criticisms should serve as a reminder in defining a simple, realistic and readily applicable model for web analytics.

      More info: Capability Maturity Model Integration (CMMI)

      TDWI Business Intelligence Maturity Model

      Wayne Eckerson is the creator of The DataWarehouse Institute (TDWI) BI Maturity assessment tool, Director of Research and author of "Performance Dashboards: Measuring, Monitoring and Managing Your Business". The TDWI model main purpose is to "gauge where your datawarehousing initiative is now and where it should go next". Each of the six stages are defined by a number of characteristics: scope, analytic structure, executive perceptions, types of analytics, stewardship, funding, technology platform, as well as change management and administration.

      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".
      1. 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.
      2. 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.
      3. 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.
      4. 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.
      5. 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.
      6. 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.
      According to Thomas Davenport, analytics represent a subset of business intelligence. While BI can answer questions such as "what happened; how many, how often, where; where exactly is the problem; what actions are needed", analytics can answer "why is this happening; what if these trends continue; what will happen next; what is the best that can happen". From this perspective, we can asses a Web Analytics Maturity Model could borrow from the TDWI model.

      More info: The Data Warehousing Institute Business Intelligence Maturity Model
      Image credit: The Datawarehouse Institute

      Stratigent Marketing Analytics Model

      The Stratigent model outlines the four phases through which companies typically progress on the path to becoming data-driven organizations. A study conducted in 2007 revealed that:
      • 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.

      1. 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.
      2. Customization: The technology is customized to meet business-specific requirements. More relevant data spur adoption, drive ROI and secure funding for future investments.
      3. 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.
      4. 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
      The model is very interesting and highlights several aspects relevant to the definition of the Web Analytics Maturity Model:
      • 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.
      More info: Stratigent Marketing Analytics Model
      Image credit: Stratigent

      Davenport’s Maturity of Analytical Capability by Stage

      Davenport, in "Competing on Analytics", proposes a maturity model encompassing three vital areas of a successful analytical company: organization, human and technology capabilities (what others refer as "people, process and technology") defined in 5 stages:
      1. Analytically impaired
      2. Localized analytics
      3. Analytical aspirations
      4. Analytical companies
      5. Analytical competitor
      From a business perspective, "Competing on analytics" is without a doubt one of the best primer on the topic of analytics and its concepts can readily be applied to web analytics. Furthermore, some of the mentioned analytic competitors are well known for their online activities: Amazon, Google, Netflix, and Yahoo!, among others. For those reasons, the proposed model appears to be very close to the desired specification of the Web Analytics Maturity Model.

      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

      Coming up next: The Web Analytics Maturity Model!