Which is More Important to Sales Productivity: Divison of Labor, or Quality?
I received the following interesting question from Becky Smith, Founder and CEO, www.triagetraining.com
Hi Mr. Webb,
I’m an avid reader of your missives – thank you. I too follow Justin Rolf-Marsh at Ballistix. As I read your article of how the sales process was broken down, it occurred to me that he had done a salesperson activity breakdown and found that only 14% of their time was spent in face-to-face meetings doing selling. His approach is a division of labor where the salesperson, as the throughput constraint, has maximum time on task for the most important function they perform – where their skills are required and not easily exchanged with a more clerical/admin-type position.
Coupled with your position of using the web as a filtering mechanism and funneling leads to sales that are most appropriate vs. funneling every lead to sales, how much upside might a company find if they combined both approaches?
Here is my response:
Thanks for your question – it is a good one.
The answer to your question is that, yes, using both Justin’s Theory of Constraints approach (adapted from Goldratt’s famous book “The Goal“), and my sales kaizen approach do indeed have a dramatic effect on a company’s growth and profitability.
As I’m sure you know, one of the ways of expanding the bottleneck in the Theory of Constraints is to ensure that the quality of “material” sent through the bottleneck is as high as possible; so as to eliminate adding any value to scrap. In this case, of course, we mean the quality of the sales opportunities in the funnel.
From what I understand, Justin believes that the availability of the salesperson’s time actually spent selling is the bottleneck. So, he cleverly divides the work so the salesperson is doing little else – he creates a sort of production line where lower paid people do the appointment setting, service tasks, and so forth.
If you can make this work, I agree that it would tend to maximize the utilization of the salesperson’s time. Making it work requires enough deal flow to keep the “funnel” full, as well as reorganizing the entire system of work: breaking down the process tasks and distributing non-selling tasks to other lower paid people, and doing it successfully.
The yield of the overall process should increase as a function of the increase in actual selling time salespeople can spend in the new system. If available selling time goes from 20% to 100% of salespeople’s time, say, then theoretically the throughput is 5 times the previous system’s capacity.
However, as you are pointing out, if the quality of the deals is low, amount of gains is limited.
To use an analogy of one of Deming’s famous teaching examples: If you have to sort through piles of white beads to get to the blue ones, your potential yield is still limited to the number of blue beads actually in the pile.
That is why I’ve taken the quality and productivity sciences approach to the sales process: by measuring the quantity and quality of deal flow (i.e., customer actions through the stages of their customer’s journey), you can use data to locate the causes of the problems, and provide clues for what changes will improve the system.
In effect, instead of taking the ratio of white to blue beads as a given, we can begin working on the process that produces blue beads, and improve it.
As to how much the gains could be – the sky is the limit as far as I can see. Most companies have not even come close to scratching the surface on this.
For example, in the webinar we did last week for SPIF! Members, we showed how to achieve forecast accuracy in excess of 90%. It is not all that difficult if you are scientific: identify, count, and analyze observable characteristics of your prospects (and your relationship with them) and correlate this to results (wins, losses, and no decisions). As it turns out there is a close link between the concepts of quality and of sales qualification.
Who would have thought you could readily achieve sales forecast accuracy of 90% even just a few years ago? Now you can, and the impact on throughput is enormous.
Some of the audience’s questions were about how to derive deeper correlations in the data – figuring out indicators for prospects who are ready to buy now, for example, or who would respond better to certain value propositions based on demographic data.
These are the kinds of analyses that can enable sales processes to start moving really fast, and exhibit yields of 80%, 90%, and higher instead of the 1% to 2% many companies put up with today.
This is the direction the sales process improvement world is going. So, from what I have seen, Justin is definitely onto something, but it isn’t the entire story.
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