Triaging Digital Analytics Requests

Last week, I spoke at an analytics event in Stockholm and had an interesting question from one of the attendees. Here was the question:

Our analytics team is constantly bombarded with requests, and we find it challenging to keep up. How do we triage these requests?

I have received this question many times over the years. At a high level, I attempt to avoid the bombardment of digital analytics questions with the following approaches:

• Executive Business Objectives – I have always advocated for a top-down analytics implementation approach in which the analytics team identifies the organization’s most pressing needs from executives and works primarily on those questions. A top-down approach creates alignment and prioritization since working on requests from executives should shield the analytics team from other requests.
• Self-Serve Analytics – I have found that most analytics teams receiving many digital analytics requests use a centralized vs. self-service model. A centralized model leverages a core team that performs most of the analytics for internal teams, while a self-service model empowers internal stakeholders to perform their analysis. The former is often easier for organizations to implement, but the latter can help minimize analytics requests. However, successfully rolling out self-service analytics can be difficult due to the need for training, implementation knowledge, etc.

While I may write about the preceding items in more detail later, I will provide a super-tactical approach to triaging analytics requests that you can implement quickly in this post.

Three questions

You can use three questions with your stakeholders to determine which items you want to focus on when it comes to digital analytics analyses. These three questions help you focus on what is valuable and can be used to triage digital analytics requests. Of course, you don’t want to be too tough on digital analytics requests, or you will turn people off, and they will go back to relying on their “gut” instead of using data. But if you find yourself buried in requests, these questions might help you focus on the most impactful requests.

When you are inundated with analytics requests, the first question I like to ask is:

#1 – Why do you need to know this?

Lots of people want to know things. They see data and analytics as a way to know it. But many times, people don’t have a genuine business reason for wanting to know what they are asking you to provide. I have found that if you give even the slightest pushback, you will find no meaningful business reason for their request. Many times, people are just curious.

If you ask them why they need to know what they are asking you to provide and they cannot provide a meaningful answer, move on to other requests. “I was just curious…” is not an acceptable response.

#2 – What would you change in the website (or mobile app) based on the data I provide?

In digital analytics, you don’t gain any value unless you turn data into insights and then change your digital property. Once you change your digital property, you can measure whether your analysis was correct and your organizational KPIs went up or down (depending on which direction you want them to go!). But often, the requests you receive are related to things that may never change or would be very expensive. For example, you might get a request to validate a hypothesis that the website navigation bar is ineffective. That is a great question and likely something worth investigating. But if the navigation bar was re-designed one year ago and cost a lot of money, does the organization have an appetite to throw that away and re-design it? Can the people asking you for the data influence that type of change? If not, I would de-prioritize this request. Sometimes, people at your organization want data to prove that what people did before was wrong, to settle a score, to prove a point, or to be political. By asking whether they can affect change based on your data, you can keep yourself out of these requests that will likely not help the organization.

#3 – How much money would the organization save or earn based on changes associated with the data you provide?

If one of your requestors makes it through the gauntlet of the previous two questions, this question can be used for final prioritization. At the end of the data, organizations invest in data and analytics to generate incremental revenue or cost savings. The best analytics teams are turning data into insights and insights into revenue generation or savings. While there is no perfect way to estimate potential incremental revenue or cost savings, anyone asking you for analysis should be able to provide an estimate.

For example, imagine you are approached to analyze what could contribute to the low lead generation form conversion rate. This analysis effort could be an expensive, time-consuming analysis. If the current conversion rate is 7.5%, how much would it need to rise to justify the investment in the analytics work required to boost the conversion rate? Would it need to increase by 1%, 2%, or 3%? Getting your stakeholders to do the math for you can help justify the investment in the analysis. There may be some legitimate questions, but even if you provided the best data and it drove terrific insights, the amount of money your organization made or saved may be lower than other analysis opportunities.

When I managed digital analytics at Salesforce, we used the amounts from this last question as the final arbiter of project prioritization. We compared the amount of time spent and the potential financial impact. Estimating time spent and potential financial impact is the best way to ensure that you leverage your limited digital analytics resources to the fullest extent.

Final Thoughts

As I mentioned, you never want to have an adversarial relationship with your stakeholders. It is a blessing that you have many people who value data and analytics at your organization. But there are times when analytics teams can be overwhelmed, and prioritization is needed. These three simple questions can help analytics teams determine which analysis efforts to prioritize.

Lastly, if you are uncomfortable asking your stakeholders these questions, you can also create a form they can use to submit the answers to these three questions and then follow up. You will be surprised how forcing just a tiny amount of effort on the part of your stakeholders can help you avoid a lot of insignificant requests over time!