I'll share the marketing digital analytics roadmap I built at work in this post. It may be good to evaluate where you're on the roadmap and assess whether it aligns with your current state.

Usually, a team changes a lot in a company, maybe within a year, if not in a few years. But, on the other hand, companies in Silicon Valley move fast, with no exceptions for the companies I've worked at.

It is always a good idea to reassess the plan you had in mind and reflect on what you had built. That will help you guide to the next steps in your planning in your roadmap.

Digital Analytics Maturity Phase

The roadmap I'm talking about is more of a digital analytics maturity line rather than an advancement of data practice or usage of tools. So please note that this is not about metrics framework stuff.

In the past, some of the roadmaps I've seen from analytics conferences were very useful; they guided me to think about my starting point, tools to consider, and skills to consider learning to help my company or the department mature with digital analytics.

However, I have noticed that many digital analytics roadmaps are geared towards adopting tools and integrating data platforms.

Something web engineers or people responsible for tracking implementation would be interested in.

The following diagram I created plots the level of advancement in data practice and the maturity of each phase.

Each phase is different for each company as every company solves different problems. In addition, I'm sure every marketing team is structured differently, and the objectives and goals vary.

Here, you'll see my first roadmap created in the past that has evolved. But, you can still take an older diagram and use it to see if your company has adopted any of this.

I created the second diagram below in 2017. Keep in mind; every company is different in approach and priorities. Still, fundamentally, given our access to technology and evolution, I thought this would be a good version 2.

The most important thing you need to take into consideration when looking at this roadmap is to ask yourself:

What business problem is being asked to solve?

The framework is created for my situation at that time without considering the specific business problems you may be in to solve. Hopefully, you can be inspired by them.

More recent version of my Digital Marketing Analytics Roadmap

I kept the recent version more high-level. What's important to note is that my subjective assessment of many companies is that they have barely got out of the first two phases.

Why do I say that?

Because according to Accenture and Qlik's research, 60 to 73 percent of all enterprise data is never analyzed.

Check out my article, "Why Do Companies Fail in Data-Driven Decision Making."

The research indicates many businesses have a challenge trying to get insights and value out of typical analytics, where solving that is a focus from the early phases of my roadmap.

Other research also shows over 40% of the skills gap are coming from:

• Using Digital Analytics tools
• Using Business Intelligence tools
• Conversion rate optimization

In 2016, 62% of the marketers responded that they do not have a documented data analytics strategy. Fast forward to 2022; I don't think that has changed.

In many discussions of maturity or roadmap framework, you'll see that Predictive and Prescriptive analytics are the future, but the reality is that many businesses are not there yet. For example, the 2021 CMO survey indicated low adoption of AI and ML, which is important for supporting predictive and prescriptive analytics.

I feel like there are bigger problems on hand that many marketers are still trying to get a grip on.

Like, as cross-channel attribution, multi-touch attribution, getting clean data in a timely fashion, etc.

Hopefully, this blog post ignites the idea of reevaluating our digital analytics roadmap. There is no one straight answer, so hopefully, my diagram kicks off some new ideas for you.

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