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How to Introduce a Politics-Free Prioritization Model for Analytics Reporting

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keep-calm-no-politics-just-insightsBuilding a center of excellence analytics practice, whether it's web analytics or data analytics, is bound to get you a lot of attention. The self-perpetuating cycle of asking business questions, diving deep into analytics data, providing actionable insights, and testing data-driven recommendations often scales by orders of magnitude quicker than the human resources and tools often allow.

Eventually, every practice runs into the problem of having to prioritize competing stakeholder demands, which is a great problem to have, but without the proper prioritization model in place, many shops can quickly fall victim to politics and worse yet, analysis paralysis.

Establishing a prioritization model is fairly simple, and doesn't have to be overly complicated or involve any specialized tools of any kind. An Excel spreadsheet on a shared drive can do wonders, but here are some guidelines you can think about when starting your own prioritization model.

Establish Line of Sight

You can start establishing a prioritization model by putting the framework in place to track requests being submitted to the team and the lifecycle of insights through to completion. Think of this as your resource management cheat sheet. You only have so many resources, and you only have so much time, but sometimes you can cascade work from one resource to another, somewhat like a production line.

Allocate Business Value to Each Request

Weighing competing priorities by some kind of scoring criteria can be done many ways, but many practices seek some sense of monetary value for each request. Most leaders in the industry will look for a strategic value assessment, which is a KPI involving equal parts revenue generation potential, estimated cost savings, and increased customer satisfaction.

In many cases, if your stakeholders are asking relevant business questions, they're often looking to prove a business case with some inkling of strategic value, so you can often ask them for a conservative estimate that can be validated for reasonability.

Seek Out Executive Sponsorship

The same people who sign your paycheck and grant your practice additional budget and resources will want to know that you've been supporting their own goals and the goals of their C-suite peers.

As alluded to above, reasonability checks against the strategic value of each request should ideally come from someone in a leadership role. This also tends to reduce the potential for name-dropping and internal politics.

Maintain Strategic Alignment

Depending on where your practice lives within an organization, allocation of resources (and therefore work effort) should be determined and refocused throughout the year.

For instance, if the strategic alignment of the insights team is driving sales rather than reducing customer churn, there should be less overall prioritization granted to requests that are related to operational issues and customer experience. That doesn't mean issues of a severe nature can be ignored, but if at the end of the year the insights practice delivered 80 percent of their recommendations for customer retention questions, they may be misaligned and under-delivering with respect to their sales focus.

Keep in mind that resourcing and budget may come from other groups within the organization.

Determine Optimum Levels of Team Efficiency

As we all know, scale can easily be accomplished by driving up volume and driving down quality, but that approach is rarely sustainable, especially in a consultative business such as analytics and insights.

In order to effectively drive long-term value for the organization, and avoid potential burn-out of your team, you'll want to determine what the right mix of volume and quality might be, so that you can prove your case for additional resources, or allow your executive sponsors to decide prioritization for their business objectives based on current resourcing allocation.

On the flipside, you can also use efficiency indicators to assess leaders and laggards on your team and either balance the workload or look for coaching opportunities.

Conclusion

An effective prioritization model can do wonders in helping you scale your insights practice, and avoid a lot of internal politics associated with competing business stakeholders and limited resources.

By no means are the aforementioned prerequisites for an effective analytics center of excellence, but at the speed at which the big-data industry is growing are provided as guidelines to help many insights leaders scale effectively in an ever-increasing data-driven digital marketing environment.

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