How Market-Leading CEOs Use Data to Drive Dynamic Revenue Planning

Dynamic Revenue Planning is essential to successful Go-to-Market execution. Executive intuition will always have its place, and any well-constructed plan will involve a mix of art and science. Our recent experience with a leading technology firm led us to take data-driven planning to a new level.

A recent SBI research report recounts the unique challenges of planning for 2021 and referenced the need to make smarter bets.  Included in that report 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:

Are You Covering Revenue or Are You Covering Potential?

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.

It All Starts With Segmentation

While account segmentation can take different forms, there are 2 components that every company should understand about their existing and prospective accounts:

  • Potential Spend – If your sales team could sell ‘everything in the bag’ to an account, how much would that account spend? We know we won’t sell everything to every account, but this is a valuable data point to understand the ‘art of the possible’ within each account.
  • Account Attractiveness or Propensity to Buy – Expressed as an ‘account score’ based on attributes of every customer and prospect account and how closely they match your Ideal Customer Profile – this is simply a measure of how likely an account is to buy from you.

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.

Moving on to Coverage

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.

Calculating ‘Expected Bookings’

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.

Use Expected Bookings to Map Out Your Execution Plan

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:

  • Roll up an overall bookings target that can be sliced and diced by geography, vertical, stratification tier, etc.
  • Evaluate the number of reps required within each segment using anticipated rep productivity and expected bookings per account as inputs
  • Establish rep territories that are equitable within each segment, based on the aggregated expected bookings of all accounts within a territory
  • Establish rep quotas based on the expected bookings from each account assigned to each rep

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.

Revenue Planning Is Not a ‘One and Done’ Exercise

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.