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January 26, 2021
How Market Leading CEOs Use Data to Drive Dynamic Revenue Planning
By: Chad Wittenborn
Back in the fall, my colleague Tony Erickson wrote about the unique challenges of planning for 2021 and referenced the need to make smarter bets, and used casino imagery to drive the point home. Included in that article was a Dynamic Revenue Planning Tool that forces a belief-bet framework to shape your go-to-market planning.
In this blog, learn how to be a ‘card-counter’ at the casino by deploying a data-driven approach by not only estimating total raw potential within your accounts but by estimating an ‘expected bookings’ figure on an account-by-account basis.
First, though, let’s review some basics of account segmentation and coverage model best practices:
Perhaps the biggest mistake many firms make as they create their annual execution plans involves over-indexing on revenue coverage. They allocate the lions’ share of sales resources to accounts that are already spending the most with them. While it clearly makes sense to dedicate resources to retaining that revenue, the opportunity for additional bookings within those accounts is often limited.
Companies that consistently hit their number are intensely focused on potential spend. Their plans begin with segmentation data that highlights potential spend on an account-by-account basis.
While account segmentation can take different forms, there are 2 components that every company should understand about their existing and prospective accounts:
It’s no small task to create a data set that assigns a value for both of the above for every account in your universe – both customers and prospects. But once you’ve accomplished this, the rest becomes much easier.
With a robust account segmentation data set as your foundation, you can move to your sales coverage model. How many reps do you want covering different segments of your account universe?
SBI recently worked with a successful, high-growth software company that had robust data on their accounts. Historically, their sales coverage model had been based entirely on (a) how much each account was already spending and (b) how large each company was in terms of overall company revenue. Potential spend was not a factor in their coverage methodology.
Knowing that we would not only want to overhaul this coverage model but would then move down into individual territory design and quota assignments, we worked with the executive team to go one step further – to calculate ‘expected’ bookings for every account.
To clarify, this is not intended to be an exact, accurate prediction of bookings on an account-by-account basis. It’s simply an objective view of what you could reasonably expect for each account based on (a) Raw Account Potential, (b) Account Attractiveness/Propensity to Buy, and (c) Historical Trends and/or Market Dynamics that should be used to dampen the potential spend.
We’ve already covered (a) and (b), but (c) may be a new concept. You’re obviously not going to sell every product to every account over the course of the period you’re planning for, but consider how history can guide you to arrive at a realistic expectation for every account. What has your new logo penetration rate been in years past, and how has that rate accelerated? What trends do you see with cross-sell/upsell, and are there patterns in cross-sell/upsell penetration based on where that customer is in the customer lifecycle? How do all these rates relate to the potential spend you’ve calculated within each account? Just as a simple, illustrative example,
If 3 years ago we had penetrated 5% of all calculated cross-sell/upsell whitespace within our existing customer base and this rate had increased to 6% 2 years ago, and 7% 1 year ago, then you might reasonably estimate ‘expected bookings’ on an account-by-account basis for this year at 8% of all calculated whitespace within each existing account.
If you’re able to answer the questions above, you should be able to arrive at an expecting bookings figure for every account.
Once you have established the foundation discussed above, building a bookings plan and, in fact, building your entire go-to-market plan suddenly becomes much easier. The expected bookings by account should be leveraged to accomplish the following:
To go deeper on how you use granular account segmentation to assign expected bookings by account and leverage this output to establish your go-to-market execution plan, contact us, and ask about the possibility of a virtual workshop.
SBI research has deemed that annual planning is a thing of the past as the top 10% of market leaders rethink their revenue plans on a consistent basis to adapt to any market conditions. For a deep dive, Managing Director Gregg Blatt recently wrote about the importance of building revenue plans which are dynamic and adaptable. While the data-driven recipe above references the planning process, the fact remains we need to build revenue plans that remain relevant throughout the course of the year, especially in turbulent times.
Nearly a year has passed since the pandemic sent shockwaves worldwide, and it seems as though we have settled into our new normal. No one can predict what 2021 will bring, but it will be that much easier for CEOs to prepare for what’s next by leading with data and remaining agile throughout the process.
Download the Dynamic Revenue Planning Tool to help determine if you’re making the right bets for your company’s growth.
Chad deploys a highly analytical approach, leveraging a background heavy on growth strategy, sales effectiveness, and operations, to drive top-line revenue growth. Prior to joining SBI, Chad held a variety of growth leadership roles in industries ranging from Manufacturing to Healthcare. He has worked closely with sales, marketing, and operations leaders to identify obstacles to growth and to execute initiatives to overcome those barriers. Chad has delivered results by leading projects involving structural transformations of sales organizations, new service line launches, incentive plan overhauls, performance management programs, and CRM implementation and adoption.
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