Use Data to Help Leaders Define Current State
Use Data to Help Leaders Define Current State
Quick Summary
Problem: A leadership team’s inability to agree on the current state puts any strategic effort in jeopardy as there is not a shared sense of meaning, assumptions, beliefs, and expectations.
Opportunity: Rather than relying on one individual’s convictions or experience alone, utilize data to establish a baseline and allow that to challenge or validate what is true with what is perceived to be true.
Resolution: This process yields a collaborative conversation that recognizes any one person’s perception of reality may be incomplete. This allows leaders to move beyond their confirmation bias and begin to balance the quantitative with the qualitative.
The Case
If you’ve been in leadership any amount of time, you’ve learned two things:
You may use the same words as another leader, but they may not mean the same thing.
Your view of reality is limited and biased.
This is what makes working as a team so vital. When the rate of external market factors and realities are evolving beyond any single leader’s ability to process, assimilate, or integrate, it’s nearly impossible to make the best decision if you desire it to be objective, unbiased, and well-informed.
When I started working with one particular leadership team, their general perception was that their inability to drive growth in revenue was a function of uncontrollable factors related to donors. If this were true, then the leadership was not accountable for current results. And if we carry that to its philosophical end, it would not take us to a good place.
I was careful to take notes of the leadership’s discussion points. I also met with each leader individually to gather as much information as possible. When I had exhausted this part of the discovery process, it was now time to dig into the data.
My Hypothesis
I believed two categories were most likely to reveal the truth around the current state: the field team’s activities and donor transactions.
I completely dismissed the idea that a lack of revenue growth was the donor’s “fault” simply because there was no real way to quantify nor influence the outcome.
My hypothesis was simple: the field team was not spending time with the right donors in a way that was tailored to the individual donor’s capacity, behavior, and interest.
Since this was a preliminary effort, I limited the scope of the data to the previous three years of fiscal data. After using Tableau Prep to finalize the architecture and preparation process, I brought it into Tableau Desktop. My general rule of thumb is each visualization should answer one question. This limits the focus to achieving one insight across multiple dimensions and measures.
It’s not uncommon for me to blow through fifty or more of these before I start to see a pattern or trend in outcomes. Not limiting my thinking also allows me to bring a “beginner’s mind” to the project to ensure I arrive at a set of actionable insights that move the leadership team forward.
Key Learnings
I discovered two (among many) realities that stood in stark contrast to what the leadership team believed to be true:
PERCEIVED REALITY: Any increase in top-line revenue was the result of the field team member’s ability to move a majority of their portfolio to increase their giving.
ACTUAL REALITY: What I discovered was not at all consistent with this truth. The truth was there were a few sporadic, unanticipated, and unusually large gifts that were not the result of an increase from a large percentage of any portfolio. Instead, it was some outlier gifts that are impossible to forecast and were unlikely to be repeated.
PERCEIVED REALITY: A majority of donors in each portfolio were active and contributing and the current field team had maximized their utilization and capacity to develop those donors.
ACTUAL REALITY: What I discovered was the number of donor accounts in each portfolio was substantially higher than revenue patterns. This suggested, as you might expect, there were accounts in each portfolio that were unproductive. Thus, the field team was focused on reaching all the donors in their portfolio, regardless of performance by account, rather than focusing on the most productive and engaged donors.
Next Actions
I utilized the Tableau Story function (think of it like a PowerPoint presentation) to shape my findings and conclusions. I also incorporated the data into my suggested next actions. My suggestions for this team consisted of three immediate next actions:
Reassess the assumptions contributing to how donor portfolios are constructed, modified, and managed. I learned it had been decades since strategic decisions were made (let alone documented) about how a portfolio was assembled and evaluated. Further, as field team members left and were not replaced, those donors were equally parsed across the field team based on leadership’s perceived capacity—not productivity, fit, or utilization.
Implement a prioritization model to help each field team member know which donors to focus on and why based on multiple data points. This work needed to be completed by the leadership team. At the time, it was the responsibility of each field team member to set their prioritization methodology. (Yikes!) Anything that takes away from the core functions of a field team member (build lasting relationships and deliver on donor commitments) is too costly. Return this responsibility to the office, and I knew the field team would have more time to perform their most critical functions.
Implement an intentional donor development plan for the top ten donors in each portfolio. This would be the result of feedback and input from the field team member and data. When you treat all donors the same, you will perform below forecasts and projections. You need to scale your donor engagements and planning to the expectations and output potential of donors, this is particularly vital as you move up the donor pyramid.
There were, of course, many other next actions that could be included. I wanted to keep this team focused on the most pressing issues that, if resolved or improved, could have a direct impact on revenue over the next one to two quarters.
The Response
One word would describe the room after I finished my findings based on this limited data and a short discovery period: relief.
There is a strange thing that happens when you stop guessing and start basing your thinking and decisions on what is actually happening instead of what you perceive to be happening.
The leadership team recognized their role in contributing to their current state and made the commitments necessary to address the next actions discussed. What came next was a reset with the team, a reframe with the field team, and a lot of hard work. And I’m proud that this team saw a consistent increase in revenue over the coming two quarters.
Sometimes the urgent can replace our perspective on what’s important.
Data can help leaders find common ground upon which they can inform their observations to ensure their decisions and commitments align with anticipated outcomes.
Most important, embracing data as a means to shared truth empowers leaders to take ownership and find the confidence and conviction to create a more desirable future.
Ben Stroup is Chief Growth Architect and President at Velocity Strategy Solutions where he helps leaders design, develop, and deploy smarter business growth strategies. Ben is a futurist, disruptor, and data champion. He leads a team that takes a structured learning approach to business challenges, which allows them to assist leaders in bridging the gap between ideas, innovation, and revenue—taking ideas from mind to market.
Velocity Strategy Solutions is an on-demand, next-generation business strategy and management consulting firm which provides clients with a relentless focus on data, execution, and results that positively impact the bottom line. Velocity delivers integrated people and revenue strategies combined with a disciplined approach to growth architecture that elevates the capacity of leaders, teams, and organizations to succeed and win more.