The Myth of the Best-in-Class Waterfall Benchmark

According to the Social Security Administration, the median per-capita annual wage in the United States in 2015 was $28,851. The “best in class” salary (let’s use the top 1 percent of earners) is estimated to exceed $400,000.

Based on this information:

• How does your current salary (let’s assume it’s $100,000) compare to this benchmark?
• Should you run into your boss’s office and demand a hefty raise? Or should you instead feel very grateful for your current salary?
• From a planning perspective, would you base your household budget on best-in-class income?

Clearly, answering these questions requires context. First, your current career choice (teacher, marketer or NFL quarterback) is a major factor-shaping circumstance.

But because many of you are marketing professionals, let’s use that as a beginning point on defining context. As a marketing professional, are you happy with your $100K salary? Again, the answer needs more context. If you're a recent college grad, you’ve landed a great salary. If you're a mid-level marketing manager, you're probably satisfied. And if you're a CMO, it's likely you feel significantly underpaid. Other factors, such as industry, geography and company size, also help to set context in answering the key question of “Am I fairly compensated compared to my peers?”

So, what does all this have to do with b-to-b marketing and sales?

Clients often ask me,  “What are the best-in-class conversion rates?” for key stages in the SiriusDecisions Demand Waterfall^®. And just as salary comparisons require context to define the right peer benchmark, Demand Waterfall conversion benchmarks also require context.

Demand Waterfall conversion rates are impacted by a number of different factors, including demand type, lead qualification level, deal size, customer type and the channel. Collectively, these parameters define the context for the Demand Waterfall.  

Based on context, the Demand Waterfall will have significantly different shapes. Let’s look at several different contexts and see how the conversion rates across these waterfalls differ: 

• Early-stage market and solutions with new concept demand type. With this demand type, the prospect is often not aware of the impact a business problem is having on his or her business and doesn’t have a funded initiative to solve this problem. As a result, salespeople must be visionary in helping the prospect quantify the value and understand solution approaches, and positioning his or her solution as the right alternative to warrant changing budgets and initiatives. With new concept demand, sales-ready leads are lightly qualified based on role, company and general interest in solving a business problem. These waterfalls are “open” at the top, with inquiry to TQL conversion ranges from 15 to 25 percent.  Later-stage conversion rates (TQL to closed/won) are low, ranging from 2.5 to 4.5 percent, due to challenges in obtaining funding and changing priorities.
• Mature markets and solutions with established market demand type. With this demand type, the prospect is currently solving the business problem and has funded initiatives. Sales-ready leads are often deeply qualified using BANT or other propensity models. These waterfalls are more constrained at the top due to the tighter qualification threshold, with INQ-TQL rates ranging from 5 to 7 percent. Later-stage conversion rates (TQL to closed/won) range from 12 to 18 percent due to the existence of funded initiatives.

Just as using the wrong salary benchmark can lead to frustration when looking at your pay stub, using the wrong waterfall benchmarks can cause problems for marketers:

• It can drive the wrong behavior. Waterfall benchmarks are inherently linked to qualification levels, and qualifying to the wrong levels based on a different context can impact overall waterfall performance. For example, setting goals based on established market benchmarks when the appropriate context is new concept demand types can lead to overqualification of leads early in the funnel and restrict total waterfall throughput. The key is to qualify to the appropriate level based on the waterfall context.
• It can lead to the creation of the wrong budgets and plan.  Reverse waterfall analysis is used to determine the volume of inquiries (and other stages) needed to meet revenue goals. This inquiry forecast drives program planning and budgeting, determining the program types and budget needed to reach waterfall volume goals. Planning marketing budgets and programs based on the wrong waterfall benchmarks is like creating your household budget based on someone else’s income. The budget will not reflect reality and often leads to shortfalls.

As you’ve seen, there is not a universal “best-in-class” benchmark that applies to all waterfalls. It’s critical to select the appropriate benchmark to establish goals and budget and build a strategy to improve productivity and enhance marketing’s alignment and impacts. SiriusDecisions benchmarking and advisory services can provide appropriate benchmarks based on peers with similar waterfall contexts.

I'll be presenting on demand creation in May at Summit 2016 in Nashville. Register today!