Launched in 2002, and revised in 2012, the SiriusDecisions Demand Waterfall has become the de facto standard for managing demand generation processes.
In this two-part article, we explain how it works and why it is so useful.
In addition to the way that the marketing qualification stage incorporates the key function of telemarketing, there are two other parts of the Demand Waterfall that I would like to highlight:
#2: MGL/TGL/SGL – Three funnels in one
#3: The SGL – Pushes, sandbaggers and best case syndromes
To see the summary of the earlier point, please refer to part one of this article.
#2: MGL/TGL/SGL – Three Funnels in One
Sirius recommends three different but complementary ways of monitoring conversions within the new waterfall: Marketing Generated Leads (MGL), Teleprospecting Generated Leads (TGL) and Sales Generated Leads (SGL).
The best way to explain this is to quote a use case from my own experience. The four demand generation channels measured in my use case were web leads (which at the time was the single inbound form, also known as “contact us”), campaign responders (which were responders driven by mainly outbound direct email), ISR sourced (which were the tele-team generated leads from account based cold calling. Note that we were also 100% dedicated to pipeline generation – there were no closing or sales administration type tasks.
We owned 30% of the pipeline revenue creation in the Americas – which means the SGL would be 70%. Sales were tracking at a 30% close ratio (40% in Latin America). Within the 30% of the SQLs my team owned, the mix was 60% web leads sourced, 35% ISR sourced and 5% campaign responder sourced. So if I take those numbers and align to the new waterfall, our benchmark was 19.5% Marketing Generated (MGL), 10.5% Teleprospecting generated (TGL), and 70% Sales Generated (SQL).
The waterfall conversions across those channels varied a lot. For web leads we had an 85% conversion of MQL to SQL. (However, note that we were not measuring SAL at the time – my team qualified or generated the leads, created an early stage pipeline opportunity (MQL) and then the closers would convert them into forecasted deals – SQL, future opportunities – the grave yard, or rejections). For campaign responders we had a 15% conversion of MQL to SQL and for ISR sourced we had a 50% conversion of MQL to SQL. We did not have great data on the SQL to Close/Won as sales was famous for moving my team’s SQLs back into a future lead stats if the deal did not close in 90 days (even though our sales cycle was 9-12 months SQL to Close) and then when the deal heated back up and was ready to move further down the sales cycle, they would create duplicate opportunity, Close/Loss my team’s opportunity and pass it off as sales generated (more below on that – aka the sandbag).
#3: SGL – Pushes, sandbaggers and best case syndromes
While it is essential for Sirius to include this source as part of its Demand Waterfall, it is in my opinion perhaps the hardest number to validate. Having worked in sales operations for a billion dollar software provider and carried a bag of my own, I know several things about this metric.
First the push. It always amazed me how often sales management would allow deals to stay active at the early pipeline stages with revenue attached to them, hardly ever questioned by management as they were pushed from quarter to quarter. There always seemed to be a deal size that reps would figure out which was enough to keep their active SQL targets in line with measurement, but not too big and certainly not attached to any “key account.” This art of pipeline manipulation distorts the view of reality.
Second is the sandbagger. This one almost everyone knows about. There should almost be a rule that the SVP of sales has to approve the deal personally if the sales cycle is less than 30-60 days for B2B solution selling. Without that approval, there should be a formula applied to the deal that overrides the opportunity create date and for customer accounts – the opportunity create date for the next deal should be auto-generated from the last PO date.
Third is the best-case syndrome. This type of SQL is related to the best case scenario deals which can also be defined as “wishful thinking pipeline”. This type of SGL is also the one I advise marketing to proceed with caution when deploying the waterfall model. Sales may get very gung-ho about working with marketing and start to mandate that sales has more aggressive targets for net new pipeline generation quarter to quarter. We did this as part of the model at a previous company and the result was sales putting fictional deals into the next quarter pipeline, spinning a creative tale of how they found the deal and were working on getting the pre-sales team in for a scope/discovery meeting, how large the deal could be, but too early to tell yet (which is the most critical part of the story telling – because it set these deals up to be pushed into future quarters).
So the net, net with SGL is to focus on discipline – a theme hammered home in every customer and analyst presentation made by SiriusDecisions over the years. That is the only way SGL is going to be a benchmark that can live honestly alongside MGL and TGL.