Life is full of risks. So are technology projects. If you ever saw the Ben Stiller movie Along Came Polly (maybe binge-watching movies during the pandemic), you got to meet Rueben Feffer, a man who assesses risk for a living using a risk management software program.
In some ways, I can relate to the movie’s risk management character. As some of you know, when I worked at Omniture, one of my roles was helping customers fix broken analytics implementations. As I performed this role, I observed many key risk points associated with digital analytics implementations. As I have mentioned in past blog posts, many digital analytics implementations fail or underperform for various reasons, including consequential decisions made or approaches taken at these key risk points. Whether you work for an organization managing your digital analytics implementation or at an agency that helps clients with analytics implementations, understanding the key places that lead analytics programs astray is important.
In this post, I will share some of the key risk points that I have encountered over the years. As I describe these risk points, I will rate each on a scale of one to ten. If something is rated low, it means that mistakes made will not be as damaging as a higher rated item. I provide this scale to help you focus on the most impactful items when determining whether an analytics implementation will or will not be successful.
While this post’s content can apply to anyone associated with a digital analytics implementation, I’m writing it primarily for those who lead or sponsor digital analytics at organizations. Marketing leaders at organizations are ultimately responsible for ensuring that digital analytics programs and implementations generate value, and, as such, they should be keenly aware of these potential risk factors. They are also the folks most likely to be in a position to rectify any deficiencies in the cited areas.
As you can see, there are a handful of key points within the implementation process where you can significantly increase or decrease the odds of your success. While there is never a guarantee that an organization will be successful with an initiative like digital analytics, I have found that you can greatly increase or decrease your chances of success by being proactive regarding this list’s items.
To that end, I have created a simple spreadsheet that you can use to rate your team on these key risk steps and see where you stand. This spreadsheet allows you to rate your analytics program/implementation on the preceding risk items and then calculates your risk score.
If you’d like to create your own analytics implementation risk assessment score, you can access the spreadsheet and make a copy of it here.
If you have been reading some of my recent posts, you know that I have been working on a new software product called Apollo. Apollo is a new type of software called an analytics management system that improves and automates much of the analytics implementation process. It is no coincidence that many of Apollo’s core features help minimize the risks I have seen be associated with digital analytics implementations. Here are just a few examples:
Over the past few years, we have used Apollo to help improve and expedite digital analytics implementations. At least half of the key risk points described above can be improved with Apollo. Here is an example of the risk analysis spreadsheet shared above with example Apollo improvements:
Using this middle of the road implementation, Apollo can increase the chances of implementation success by more than one-third! The lower the current implementation scores, the more Apollo can help, which, in the following case, could increase the chances of success by almost sixty percent:
Hopefully this post inspires you to think about the key risk points in your digital analytics implementation. If you are honest with yourself about where your analytics program and implementation are today, you can take steps to mitigate risk and increase your chances of success. Hopefully the risk factors described above, all learned in the trenches of hundreds of analytics implementations, can help you focus your time on the ones most appropriate for your organization.
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