Designing for scale – How do you maximize throughput for your operation?

Last week, I was in a meeting where we brainstormed ways to accelerate and increase throughput for our prospect management within the constraints of the limited headcount we have available. Like many operational flows, the problem boils down to a typical prospect maturity funnel: you have many prospects coming in, but only a few of them will make it all the way through to actual customers.

Don’t fix the problem – address the root cause

The initial inclination of the group was the same that countless other teams fell for: put most of the people where the majority of the work is. That’s wrong!

If you want to scale an operation, optimizing within existing constraints (e.g., headcount), you MUST put your resources where they have the most impact, not where you face the largest volume and amount of work. You must concentrate your resources at the BOTTOM of the funnel, not the top.

In an ideal world, your people would ever only need to focus on those prospects who will make it all the way through the funnel. They would guide them from prospect to customer. Unfortunately, in the real world, you don’t know which select ones out of the huge prospect pool will make it all the way.

Reshape the funnel and rebalance your resources!

The trick to scaling is to invest as little of your precious limited resources at the top of the funnel and instead deploy automation, crowd-sourcing, self-service, or even relaxing verification criteria. Reduce the funnel size as cheaply (in terms of your constrained resources) and quickly as possible and apply your constrained resources where they need to bring a select few across the finish line.

Think really hard about what compromises you can make at the top of the funnel in order to allow more focus on the bottom. In the end, it doesn’t matter how much you put into the process – it only matters how much you get out on the other side.

In our example, we were looking at incoming college applications for prospective students. There are tens of thousands of those, and the staff processing them is limited. A way of solving the tension is to deploy automation (what amount of the applications is so cut and dry that an algorithm can move them forward), crowd-sourcing (what criteria can we share amongst the network of institutions instead of re-inventing everything), self-service (what information can we have students provide instead of us hunting it down with other institutions), and relaxing criteria (do we really need to check the complete set of details for prospects or can we delay that deep dive until someone became a student or is about to).

Designing for scale requires you to take a pause. You must look at the complete flow. You must decide where you can make compromises and where attention to detail and thoroughness matters. In many cases, it will require you to do the opposite of what your instinctive reaction would have been.

If you want to check out a fun read on this topic, I recommend “The Goal” by Eliyahu M. Goldratt (

I read that book decades ago and then again just a few years past when I had to solve a challenging supply chain problem. However, I have to admit that I only fully understood then concepts when I had to build a hiring system at Amazon that supported more than ten million applications per year. The team worked hard across all dimensions of the problem space, and I learned a lot of valuable lessons through the process.

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Lessons from Amazon, a global pandemic, and other crazy times
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