I’ve learned about the bullwhip effect in a supply chain operations course at Maastricht University back in the 2010s. We tried to make predictions for supply and demand for beer. We learned quickly that bad decisions by individuals in the chain can cause dire shortages or overstocks. Example: The brewery’s sales department offers a steep discount on craft beer near its end of shelf life. Demand suddenly sparks. But they don’t inform the production steering team, which ramps up the brewing of craft beer and adds a bit of padding to the next order of the special hop and malt that goes in it. When demand goes down after the promotion, the brewery has even more fancy craft beer on stock that nobody wants.
The bullwhip effect
These effects in the supply chain multiply further upstream, similar to the amplitude of a bullwhip. Incomplete information, bad planning, and a general lack of organization are the most common reasons for supply chain distortions. This is a common problem in complex supply chains spanning many departments and companies. But interestingly, you can observe a very similar effect in the revenue chains of fast-growing SaaS companies.
opsloft-bullwhip-effect-revenue-chains_image1The SaaS revenue chain
An inevitable side effect of scaling up SaaS revenue operations is a specialization of roles. Typically, you would expect most of the following teams as early as the B2B SaaS company crosses the €20-30m ARR mark. These players heavily influence the revenue journey and make communication and planning more complex:
Marketing:
- Demand generation
- Product marketing
- Field marketing
Sales Development:
- SDRs (sales development reps) that work inbound leads
- ADRs (account development reps) or similar to work named accounts
Sales:
- AEs (account executives)
- SEs (sales engineers)
Legal:
- Lawyers
Customer Success:
- Onboarding Managers
- CSMs (customer success engineers)
- CSEs (customer success engineers)
- Professional services
- Support
Revenue Operations:
- Marketing Ops
- Sales Ops
- Customer Success Ops
Typically these teams are not fully aware of what all the other groups are doing. If your day to day consists of MQL, MQL, MQL. You think about MQL. If your day to day consists of redlines, you see redlines everywhere. This is not necessarily a bad thing. If you care a lot about one thing and are smart about it, you will get pretty good at it. The difficulties arise if the overall system is not carefully balanced.
The marketing/sales value chain is a typical example. It is so frequently talked and written about because not many seem to get it right. In essence, every handover point is more complicated than a process that happens within one team. The marketing/sales handover (usually referred to rather imprecisely as alignment) is not remarkably different from any other handover point. Problems are generally just more visible, as the outputs are so easy to measure.
The simplified value chain reads:
qualified leads/accounts (marketing) → meetings (sales development) → pipeline €s (account executives)
Sales development is the connecting link between marketing and sales and plays a not to overstate role in the revenue chain. Sticking to the supply chain logic, SDRs consume an input (qualified leads/accounts), work their magic on it (thoughtful, valuable interaction), and thereby produce an output (qualified meetings/pipeline €s). If you know your inputs/outputs and related capacity, it should be reasonably easy to set up your steady-state system.
So much for theory. In reality, you will have to plan with imprecise averages and will face complicated constraints: territories, languages, products, seasonality, etc. For the example above, you likely end up with one of two scenarios: a) AEs are starving and complain that they don’t get enough meetings. Or b): AEs are overwhelmed, as they already are fully booked with pipeline, prospects are not adequately looked after and have a bad buying experience. Clearly, neither situation is good for the business.
Some more examples of distortions in the revenue chain: leads come in huge spikes after big industry events. Lawyers are overwhelmed by 80% of contracts coming in the last week of the quarter. Your Spanish speaking support staff gets entirely overwhelmed by two large Latin American customers in the onboarding phase. All are examples of an imbalanced system.
| Phase | Input | Output | Limiting capacity |
| Campaign | Attention | Lead | marketing |
| Qualification | Lead | MQL | marketing |
| Prospecting | MQL | SQL / Meeting | SDR |
| Discovery / Trial | SQL / Meeting | Pipeline € | AE / SE |
| Negotiation | Pipeline € | Contracted customer | Legal / Senior Executives |
| Onboarding | Contracted customer | Onboarded customer | CSM / CSE / PS |
| Impact | Onboarded customer | Successful customer | CSM / CSE / PS / Support |
| Expand | Successful customer | Expanding customer | CSM / CSE / AE / Product |
| Grow | Expanding customer | Promoter / raving fan | All |
Balancing the system
The Revenue Operations team has the unique advantage of full visibility and responsibility across the revenue chain and system. Like in any classic production process, Revenue Operations needs to orchestrate inputs, outputs, and capacity. Usually, you will have quite a good understanding of your core sales capacity. A simple example:
- 1 AE = €200k bookings per quarter
- 3x coverage = €600k going-in pipeline
- 1 SDR = €800k net new pipeline per quarter
- 1 AE = 0.75 SDR
- etc.
From these numbers, you can derive as a simple function the required capacity for other roles, e.g., the number of sales engineers. The downstream part of the revenue chain (core sales and customer success) can be relatively easily planned, while the upstream (marketing) is challenging to get right.
MQLs as a planning metric have severe downsides. While they are convenient to measure, they often don’t show an accurate reflection of sales readiness, which must be a requirement for a smooth revenue chain. The better metric in B2B SaaS is “SQAs” – Sales Qualified Accounts. We are seeing a continuous shift towards account thinking, but it is still hard to execute.
opsloft-bullwhip-effect-revenue-chains_image2Flattening of spikes
Even if we have the correct planning assumptions for inputs, outputs, and capacity on average, it stays tricky. Averages are seductive in general and wildly misleading for capacity planning. Even if the legal team can theoretically work 50 evenly distributed contracts per quarter, they nowhere near can work 40 deals in week 13. Systems need some slack, and if you have a successful growth engine, you can afford to build it in.
On the same account, try to flatten these spikes. Investors value predictable businesses. Extreme backloading of quarters, high dependency of specific seasonal events, and skewed results to specific regional territories make it very difficult to build a consistent growth engine. An excellent first step is to make these patterns visible based on data. Look beyond the averages.
opsloft-bullwhip-effect-revenue-chains_image3