One of the first things you do when kicking off an advisory engagement with a client is get familiar with their goals.
You’ll ask questions about where they want to be, what scale they want to operate at, and how you can help them get there.
At the start of your engagement, you start to dig into the goals of your client. They’ll start telling you about their aspirations, usually tied to a metric like revenue, profit, or moving a certain amount of inventory.
But their goal is really a two-dimensional depiction of what they want to achieve. What they don’t tell you is everything that needs to change to make those goals achievable.
A lot of the time, businesses set goals based on metrics. Often, they use the term KPI—key performance indicator—which is the practice of choosing a metric that tracks progress toward their goal.
These metrics are great as a north star, but they only show part of the picture.
Nothing ever happens in a vacuum. There are drivers behind outcomes: factors that drive both sales generation and fulfillment.
These are the layers of a client’s goals. When they say they want to achieve a financial goal, what they’re really saying is they want a business that’s built to achieve that goal. It’s up to you to uncover what those factors are so they know what it will take to hit their marks.
You’ll start to find the correlations between different metrics within the data. Some examples of this could be sales increasing with marketing spend or headcount increasing as production increases.
When finding these connections, start by drawing from your personal experience. If you’ve observed a trend with prior businesses you’ve worked with in the industry, you’ll likely observe something similar again.
It’s best to approach the data with a hypothesis. When there’s such a large amount of data at your fingertips, it’s easy to get overwhelmed and lost in the sea of it all. If you start with some hypotheses to test, you save yourself time and effort of digging around without direction.
Let’s take a look at an example of how you can use interdependent data effectively in an advisory conversation.
A client selling enterprise software comes to you who just cracked $1 million in revenue in their most recent fiscal year. They’re currently setting goals and have ambitions of increasing revenue by 30%. They approach you with their financial and operational data to find out if their goal is easily achievable.
As you start crunching the numbers, you see that their marketing efficiency ratio (the attributed sales revenue generated from a dollar of marketing spend) is approximately 5. To generate the desired $300,000 in extra revenue, they’d need to budget for an additional $60,000 in marketing spend.
But then you start looking at the sales funnel. Their marketing doesn’t generate customers, it generates leads. To sell a $10,000 software license, the client talks to an account executive. Additionally, every account has a point person to help with setup and support.
Looking at the operational data, an account executive closes ten clients in a year, and a customer support specialist manages five different accounts.
With all of this data, you can make assumptions to get a clearer picture of what’s necessary to achieve their goal.
To make an extra $300,000 in revenue, they would need to sell 30 more software licenses by spending an extra $60,000 on marketing. To convert these leads, they need three account executives who can close ten accounts in a year. Once these accounts are converted, they need six customer support specialists to manage the 30 new accounts. Using salary info, you can approximate the total cost of scaling up.
Now, you can provide the full picture of this client’s goal. You can tell them:
Business owners are typically naturally ambitious people. They’re great at setting goals, but it takes an analytical mind to understand everything that it takes to achieve them.
Even if you have the analytical abilities, you’re only operating on assumptions if you’re not basing your analysis on data.
By combining financial data and operational data, you make a powerful shift. Financial data is typically the what—that is, what the business wants to achieve. But the operational data contains the how—everything needed to make achieving the goal possible.
Through this practice, you don’t just help your clients understand what’s needed to hit their goals but whether they’re the best goals to aim for in the first place.
Let’s go back to our example of the software seller trying to grow by 30%. When you start to tally up the costs, it’s possible that the cost of hitting that goal offsets the extra revenue generated. It could make more sense for the business to set a goal based on profit rather than growing at a loss.
Leveraging interdependent data is crucial for advisors aiming to turn client goals into reality. Utilizing robust reporting and dashboard tools, advisors can offer actionable insights and a strategic roadmap for success. This approach not only highlights the end goal but also the comprehensive, data-driven journey to get there, ensuring a future where objectives are not just set but strategically achieved.