Customer Analytics

Where Does Your Program Stand? – Digital Analytics 3D Maturity Model

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There are many Digital Analytics Maturity Models out there. And over the years they have become more and more complex. I developed one a while back that at the time was more complex then most of what people were using but I thought it needed the added dimensions. Now I think many models have gotten too complex. So now, ironically enough, I have started to look at mine as brining back some needed simplicity.

For a great many companies I think what they really need is a broad understanding of where they are and where they need to go with their Digital Analytics programs. Some need to start with an initial understanding of where they are and other are down in the weeds and need to back up and look the bigger picture. This is where I think my model is most helpful. My model broke maturity out into just three separate areas.

It breaks down this way…

  • Management Maturity
    – The level of management involvement & sponsorship
  • Integration Maturity
    – The level of depth and breadth of use of the tool
  • Implementation Maturity
    – The level of comprehensiveness & granularity of data collected

Management Maturity breaks down into 4 groups…

  • Ad Hoc
    – Analytics folks are on their own, purely individual efforts
    – No defined rolls or responsibilities
    – No set process or policies
  • Facilitated
    – The organization encourages people to collaborate with some assistance
    – Provides limited resources
    – Limited and informal policies and processes
  • Managed
    – Middle management provides resources, people and policies
    – Middle managers define some rolls and assign or hire people
    – Middle managers define policies to facilitate the governance of the program within their sphere of influence
  • Driven
    – Senior Executives mandate a fully staffed and structures digital analytics program
    – Senior Executives ensure the availability of resources and require analytics staff to coordinate across departments or business units
    – Senior Executives define cross departments policies and processes to coordinate enterprise wide program

The tough part about management maturity is that only Managers or Senior Executives can drive this area of maturity. From a consulting perspective if they’re not on board there isn’t a whole lot that can be done about it.

Integration Maturity or level of depth and breadth of use of the tool starts with the most basic and works its way up to the most valuable…

  • Data Collection
    – This is the foundation. It obviously should be done at implantation. But collecting and storing data has no value in and of itself.
  • Report Dissemination
    – The next step, Visualizing and disseminating the data. A lot of companies get stuck here. There is often the assumption that if you just get the data into the hands of the people who it applies too they will use it effectively. This may not be the case. Not everyone who looks at a sea of numbers or squiggly lines on a chart can see the opportunities or issues hidden within.
  • Analysis
    – There must be people in the organization that can dissect and cross reference the data to try and surface problems or opportunities. But I see companies get stuck here to. Analysis gets done but no one can think of or is willing to suggest a way to try to fix the problem or take advantage of the opportunity.
  • Recommendations
    – This is where Digital Analytics starts to show a return on the investment. Answering the question “what should we be doing?” using data. If you’re in management and no one is making recommendations based on data you need to shake things up. If you’re an analyst and you want to be truly valuable you need to be making recommendations from your analysis or working with someone who can.
  • Optimization
    – This is the ultimate goal. I have it as the last step but it is really a cyclical process. Recommendations need to be implemented => then data needs to be collected => which is surfaced in reporting => which is analyzed => for more recommendations => which need to be implemented => etc. This is the cycle of continual optimization. There is a visual depiction of all the levels below.

integration maturity model

Implementation Maturity is defined as 5 levels of the comprehensiveness & granularity of data collected as pictured below. The headings and bullet points are general but help define what data you are looking at and what it applies to.

implementation maturity model

So that high level view you create of your Digital Analytics program is defined by setting the Implementation maturity as the X axis and the Integration maturity as the Y axis. Management maturity can be define by bubble on your chart for the full 3D effect but as I stated before if management isn’t on board then looking at these two elements can still give you that big picture of what value your company is likely getting out of their analytics tool.

 

value levels

Both these aspects of maturity can be improved by having a good analyst on board or at least having access to a good analyst. So when you are thinking of hiring or developing an analyst you need to think about their skill sets from both these perspectives. It is hard to find an analyst who has developed skills past report building but that is crucial if you are going to get return on investment from your digital analytics tool. If you find hiring or developing an analyst too difficult remember IBM has Digital Analytics Analysts available who can become part of you team and help drive maturity.

Now it is time to make an honest assessment of what data you are looking at and what you are doing with it to see where you stand.

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