Effective sales capacity planning is crucially important.

If you have too much capacity, you are hurting the firm’s profit margins and the sales team’s morale. Conversely, if you have too little capacity, you are leaving money on the table and underserving your customers.

There are plenty of articles on sales capacity planning. Many of them rely on the rules of thumb, like “thou shalt have two account executives (AE) for each business development representative (BDR) and three account executives for each sales engineer (SE).”

The common approach is to calculate the number of AEs based on the sales targets and then apply the AE-to-BDR and AE-to-SE ratios to determine the total size of the sales team.

But where do the ratios come from? Is it reasonable to assume that they are the same for all companies regardless of the industries, target markets, pricing policies, product offerings, or sales processes? Probably not.

Consider, for instance, a fragment of the sales process below:

BDRs receive marketing qualified leads (MQL) and convert them into sales qualified leads (SQL). AEs receive the sales qualified leads and turn them into opportunities (Opp). SEs help AEs turn opportunities into deals.

We know that \(L_{MQL}\), the number of MQLs that are active in the process, is equal to the rate \(\lambda_{MQL}\) at which MQLs are created multiplied by the average time \(W_{MQL}\) it takes to either disqualify an MQL or turn it into an SQL:

\[ L_{MQL} = \lambda_{MQL} \cdot W_{MQL} \]

The number of MQLs that the BDR team can handle is proportional to the size of the team \(C_{BDR}\) and its utilization \(\rho_{BDR}\):

\[ L_{MQL} \sim \rho_{BDR} \cdot C_{BDR} \]

We can write down similar equations for the account executives and SQLs:

\[ L_{SQL} = \lambda_{SQL} \cdot W_{SQL} \newline L_{SQL} \sim \rho_{AE} \cdot C_{AE} \]

To tie the two sets of equations and compute the AE-to-BDR ratio, we observe that, in a steady state, the rates \( \lambda_{MQL} \) and \( \lambda_{SQL}\) are related via the MQL-to-SQL conversion rate \(R_{MQL \rightarrow SQL}\):

\[ \lambda_{SQL} = \lambda_{MQL} \cdot R_{MQL \rightarrow SQL} \]

After some rearrangement, we get the  formula for the optimal AE-to-BDR ratio:

\[ \dfrac{C_{AE}}{C_{BDR}} = \dfrac{\rho_{BDR} \cdot W_{SQL}}{\rho_{AE} \cdot W_{MQL}} \cdot R_{MQL \rightarrow SQL} \]

Based on this formula, if you reduced the time it takes to qualify an MQL and increased the BDR team utilization, you'd need to hire more AEs. You'd do the same if you increased the MQL-to-SQL conversion rate. If you add a new AE but your BDR team is underutilized, there is no need to add a BDR. Etc.

In summary, the optimal AE-to-BDR ratio is determined not by a rule of thumb but by the parameters of a particular sales process, parameters that can be measured.

Changing any of the parameters can affect the process in unpredictable ways.

For instance, it can take up to year to fully onboard a new enterprise account executive. During this period, the time \(W_{SQL}\) and the conversion rate \(R_{SQL \rightarrow Opp}\) will be slowly drifting, and the optimal AE-to-BDR and AE-to-SE ratios will be slowly changing as the team is shifting to a new point of equilibrium where it can be fully productive.

It takes a lot of skill and insight to maintain the balance. Great sales managers used to intuit the ratios by walking around and spending time with their teams. If your BDR team was busy, you could hear it. The level of team utilization was directly proportional to the level of noise on the sales floor.

In the new hybrid workplace reality, managing by walking around is not enough. Sales leaders must learn to fly by the instruments and make data-driven decisions. AI and ML-enabled sales intelligence tools, such as Morebell Revenue Intelligence, can help.