Beyond the Funnel and the Difficulty of Sales as a Science
The shift toward remote/hybrid selling is shining a light on the folly of organizations that rely on a small subset of sales superstars to hit their number.
In part, that’s what Jacco Van Der Kooij and the good folks at Winning by Design warned about (“science over superstars”) in their 2018 book, The SaaS Sales Method.
What does it mean to begin to measure sales as a science? Why can’t most organizations say that they do? What are the implications of this?
- Measuring ‘sales as a science’
- Challenges for small and medium organizations
- Challenges for larger organizations
- Implications for all organizations
The basics of measuring ‘sales as a science’
Van Der Kooij et al. have written a book that is right up our alley.1
We particularly like the high-level takeaways in the chapter devoted to measurement. The chapter concludes with this diagram. We like that it replaces the rudimentary funnel and that it’s inherently cross-functional. It’s one of the most complete roadmaps to B2B sales measurement we’ve seen that fits within the margins of a page.
To summarize the basic measurements in the diagram, modern sales organizations need a handle on:
- volume-based metrics: how many leads, opportunities? (funnel part size)
- conversion rates: what % convert to the next step in the sales process? (marked as CR)
- temporal measurements: how long does it take for conversions to occur? (marked as T)
But if you are a CFO, CRO, CEO, or COO, here’s an experiment: Hand this diagram to your revenue operations team and watch their reaction.
It’s a safe bet that you will first find eager agreement in the room. Then you will sense unease. What are the causes of this unease?
Why sales as a science is hard for small-medium sized organizations
The truth is that this type of measurement is difficult to achieve for any sized team in a way that is real-time, verifiable, and trended over time.
Smaller to medium-sized organizations must at minimum:
- implement best practices around data collection and data stewardship
- build out (piece-meal) a tech stack on top of Salesforce.com
- work to align the above two over the next 12-18 months
At the end of those 18 months and at the risk of speaking on behalf of your revenue ops team, a source of unease will remain. It will sound something like:
“Yes, the data now exists in Salesforce.com2. But to assemble it into a meaningful time-series format requires data engineering.”
“We have a ‘scoring model’, but to improve the conversion rates, we’ll need more carefully deployed machine learning.”
“We’re still months away. Certain analyses are still ad hoc. We need more vendors. It’s about time we hire a dedicated engineering resource for sales.”
Why sales as a science is hard for larger sized organizations
Larger organizations typically have hired vendors and headcount to a point of quasi-paralysis. A portion of the infrastructure exists to measure sales as a science, but it exists as a Frankenstein, patched together technologically and organizationally.
Reflecting on the internal barriers to embracing sales as a science at a larger organization, you might hear:
“To make a small change to measurement, I need sign off from sales ops, the business systems team, and the data team.”
“Even if we had the ‘transform’ in the Extract-Load-Transform from Salesforce to our data warehouse, I still don’t think SQL will work for this…”
“My team only owns data after the sales opportunity is created in Salesforce. Someone else’s issue.”
To put a larger organization on the path toward measuring sales as a science, leadership must at minimum:
- announce programs that support a cultural shift toward embracing complex data and away from relying on instinct
- find opportunities to eliminate vendors that promote departmental silos
- create a cross-departmental task force and incentives to get teams working together to improve and standardize measurements of volume, conversions, and time
Sales as a science is hard. What are the implications?
If you are a CRO, CEO, or COO, there are a few practical implications.
First, your organization likely lacks clear line of sight on the inputs that are the drivers of your business. Fundamental questions to which you seek answers cannot be answered or cannot be answered fast enough. We estimate that a majority of the 150,000+ executive teams whose organizations use Salesforce.com operate with an incomplete grasp of the inputs and outputs of their business.
A second implication is that your organization lacks the ability to reliably predict the acquisition of new customers. This can lead to over-/under-hiring, over-/under-spending, and broken promises.
Finally, this means that your organization has an opportunity to align resources in a way that achieves the most, most efficiently.
Measuring sales as a science can be difficult for the reasons above, but it can also be difficult because doing it well requires participation
from far beyond the sales team. The ramifications of partial measurement also stretch far beyond sales. It is worth the effort.
QFlow.ai is a machine learning-driven abstraction layer that sits above Salesforce.com and maps relationships between business inputs, conversions, and results. Customers describe QFlow.ai as a thoughtful combination of data visualization, prediction, attribution, and sales forecasting. With under 60 minutes of setup, QFlow.ai is the fastest path toward establishing sales as a science in a way that is real-time, verifiable, and trended over time.
If you’re interested in learning more, find a time to talk here.
Footnotes:
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The book is replete with intuitive diagrams that sales managers will want to reference rather than attempt to re-create in Powerpoint, offers a handy glossary for all-things-B2B-sales, and relentlessly encourages organizations to run a sales process that is cross-functional and oriented around the customer’s needs across key points in the customer lifecycle. Winning by Design assembled and published this book two years prior to QFlow incorporating in 2020. We only recently read it and were nonetheless thrilled to have our worldview validated. ↩︎
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and Gong, Outreach, Pardot, Salesloft, ConnectAndSell, HubSpot, Marketo, along with an increasing number of tools that generate data. ↩︎