Tl;dr - Goodhart's Law highlights potential problems with setting revenue growth KPIs. The solution isn't to not set them, but to set them thoughtfully while recognizing the complexity of inputs. A successful predictable revenue program will experiment with many input variables to determine which reliably and positively impact the important outputs. This approach should feel natural for industrial manufacturers who naturally manage production and operations this way. It's time to bring comparable rigor to managing revenue growth.
Understanding Goodhart's Law
Goodhart's Law is simple. It states that when a measure becomes a target, it ceases to be a good measure.
In other words, in a revenue growth context, if you believe that sales activity like emails and cold calls are precursors for sales meetings, you might track those activities in hopes of driving more meetings. Similarly, if you think it's reasonable that website traffic will impact leads, you'd track traffic so you could grow it to generate more leads.
But setting either of those as targets invariably means you/your team will adjust behavior to hit the target rather than allow the behavior to influence the outcome. That's not specific to sales or marketing, of course.
When people are pressured to meet a target, they can do one of three things according to Donald Wheeler. They can
- work to improve the system
- distort the data
- distort the system
A sales rep might send hundreds of poorly researched and unpersonalized emails because that's faster and easier than cold-calling. That would distort the data. A marketing manager might shift paid ads budget to driving traffic using broad match, increasing visits while knowing they'll all bounce. That would distort the system.
But improving the system is a long-term, exhausting project and especially difficult when it overlaps multiple functions like sales and marketing.
Therefore the solution, according to Brian Joiner1, is to:
- make it difficult to distort the system
- make it difficult to distort the data
- give people room to improve the system (voice of the process) - how inputs affect outputs
This is really, really difficult. Causal relationships are complex. The complexity means there are multiple inputs, each of which will impact the output differently, and further, combinations of which will impact outputs differently still.
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an imagined manufacturing WBR |
Join explains that it's important to understand the system well enough to differentiate between the "voice of (management)," which stipulates targets & goals, and the "voice of the process," which is a nuanced understanding of how the system works.
Please don't misunderstand. Goals, targets, activities, and behaviors are crucial to managing revenue growth. They're an appropriate way to help folks keep in mind various accountabilities. For example, you probably ask reps to divide time between target account nurturing, regular prospecting, pipeline and deal management, and existing account management. If you don't have targets for each, it's easy to devolve to working on only one or two. Targets help us course adjust.
Understanding How it Applies to Manufacturing Revenue Growth
The problem is that revenue growth is incredibly complex. There are so many inputs (overlooked in many cases) that impact outputs, the oversimplification of targets fails to yield results.
For instance, let's say that a company has traditionally generated most of its revenue from trade show leads. If they want to grow revenue and assume that what's worked to get them here will also work to get them there, they'll invest in shows. Should they spend more on their traditional shows? Or less, putting the difference into new shows? Or should they increase their show budget and do more shows, spending more on each?
Are the leads they get from shows today as sales-ready as a decade ago? Are buyer expectations the same? Are competitive pressures the same? Are the same types of buying roles attending shows? Has their product mix changed in the meantime? Should the displays be adjusted?
How can lead capture be used to connect immediately (in the booth) with visitors using digital tools? How are sales reps trained to adjust their show lead follow-up to address changing buyer habits? How are they adapting sales enablement to engage expanding buying teams?
What marketing and sales technology is being used to enhance show follow-up?
etc.
etc.
etc.
The point is that even with one simple example, trade shows, the complexity of inputs is massive.
Add in all of the digital marketing and changing sales expectations, and extrapolating simple input targets to the output of pipeline and revenue is impossible.
That's the premise of ORE™ (Overall Revenue Effectiveness.) Just as Deming's Statistical Process Control helped solve for manufacturing vagueries (identifying inputs that will drive the output of quality production), ORE identifies the many strategy, marketing, sales, and technology inputs that impact the output of revenue growth.
ORE is a revenue growth framework that helps industrial manufacturers apply the model that works well on the back end of their business, to the normally more chaotic revenue growth front end.
How to Drive Predictable Revenue by Listening to the Voice of the Process
If you think about traditional manufacturing, the "voice of the customer" (management) would call for a certain number of units/hour and an extra quality inspector at the end of the line. But that didn't account for variations in raw material supply, voltage fluctuations, assembly line conveyor speed, time & motion requirements for line workers, etc.
Eventually, curious executives began to listen to the "voice of the process" (and their line workers, frankly, who often understood the challenges better than they did.) They began to understand the complexity of the inputs and more accurately understand the correlations and causations in terms of outputs.
A key step was prioritizing inputs for improvement and control.
On-time production of consistently high quality was an output that couldn't simply be willed into being. It's the product of inputs that can be measured, improved, and controlled.
So how do we translate revenue growth strategies into executable and repeatable steps to drive predictable revenue?
By bringing the same sort of mindset from your manufacturing operation to your marketing and sales.
Predictable revenue for industrial machinery and capital equipment manufacturers is a business challenge. But it's not a black box as often feared.
Amazon's Weekly Business Review
In response to the inherent weaknesses in Goodhart's Law, common wisdom is that you must simplify the inputs you track. Only focus on a small number that you believe to be really predictive, we're often told.
It's worth noting that in the revenue growth arena, many manufacturers don't even make it that far. Their tracking, or pipeline reporting and forecasting, is often limited to revenue, bookings, qualified deals, quotes, and meetings. The inaccuracy of forecasts and unpredictability of revenue demonstrate the inadequacy of those KPIs. After all, aren't they outputs rather than inputs?
But is a really small number of inputs really the best answer?
Amazon has taken a radically different approach. Their WBR (weekly business review) is reported to happen weekly, take 60-90 minutes, and track 400-500 metrics divided into groups of "controllable input metrics" and "output metrics." While every metric is acknowledged, most of the time is spent discussing aberrations in the input metrics. Certainly, Amazon's business is larger and more complex than yours, but don't overestimate the difference.
The Right Metrics to Drive The Right Outcomes
Arriving at the right set of metrics is a process that requires experimentation, debate, and gradually accumulated insights and lessons learned. The advantage of Amazon's approach is that output distortions become quickly apparent so that tracked inputs can be modified, and the negative consequences of Goodhart's Law don't gain a foothold.
Your number of input and output metrics (your dashboards and reports) will be different than Amazon's. But you should understand that for an output (say "hot opportunities"), there are likely 50-100 inputs. Simply tracking three inputs (meetings, prospecting activities, and quotes won't help you improve....and tracking quotes is really dumb anyway!!)
It's also worth noting that Amazon's WBR is chaired by the finance department, which only has end result skin in the game. In the case of a WBR that an industrial manufacturer might run as part of a predictable growth initiative, that solves for some of the friction that naturally occurs between marketing and sales; and between marketing operations and sales operations.
Predictable Revenue Growth for Industrial Manufacturers
While I know that this model can improve the volume and consistency of industrial sales, there are several important warnings and caveats.
- You don't know what you don't know - In the tech space, there's intense pressure from investors that drives management, sales, and marketing to innovate relentlessly. Lots of dumb ideas and many amazing ones come from this crucible. In contrast, the knowledge, experience, and common wisdom in most industrial firms are from a circa 1990 vintage. That's not enough to inform an effort like this. Want a snapshot? Check out this self-paced diagnostic to help you understand where you might need some help to augment your program to the point that you can really identify and accurately track 250 or more revenue growth inputs.
- Testing is faulty if it's not properly designed - It's easy to deceive yourself as you identify key input metrics. If you had consistent electrical issues with some boards on your manufacturing line, you wouldn't just buy a Fluke multimeter for each assembly station. You'd make sure that the board supply was correct, QC incoming boards, improve lighting at the appropriate station, test soldering and assembly jigs, etc., etc. Too often, though, I chat with manufacturers who say something like, "We tried a blog, and it didn't work." Well, success in SEO, ranking, and traffic relies on deep buyer research, keyword research, editorial planning, 10+ carefully optimized attributes for every 1,200-word post, well-written content, etc., etc. Doing it doesn't count. It has to be done right.
- There will be a lot of cultural resistance - You have long-time, deeply entrenched cultural and organizational attachment to doing what you've done. You probably have a few sales reps who are in the "I've forgotten more about sales than you'll ever know" category. Your marketing team is likely under-resourced. Your production experts - the ones who guided you through the statistical process improvement on the back end - will likely scoff at the idea of applying it to revenue growth.
- It's a long journey - Plan on three years to iterate enough, and do enough, to see measurable and demonstrable improvement. It's not a quick fix of spending a few grand on some paid ads.
- Marketing and sales must change structurally and organizationally - Think about how different your production floor is today than it was in 1980. It's organized differently. You have a different number of employees (even controlling for growth.) The job titles and qualifications are different. Functions that used to be in-house are now outsourced. Marketing and sales will be similarly transformed, and the most important step will be integrating the departments. Yes, they're different. But the overlap is huge, and success will depend on deep collaboration. Additionally, you'll have to create some roles you're currently missing, including editorial content roles, sales operations, and others.
With the intervening years, memories or the angst and disruption of your manufacturing transformation may have faded. Nevertheless, they were real then. And in retrospect, necessary.
This will be similar.
Your Manufacturing Improvement Journey
Being data-driven is hard. There are mistakes and painful lessons learned. Yet you achieved it with your manufacturing operations.
Marketing and sales may feel more unpredictable, but they're not. They may likely involve far more variables, and that complicates the process, but it's not an excuse not to do it. After all, you know how.
Predictable revenue, like predictable production from your manufacturing line, is achievable. Building the right set of input metrics to impact the important revenue outputs should be part of every company's revenue growth framework.
Interested? Check out this ORE diagnostic to get a quick sense of where you stand and to help prioritize your journey to create metrics that will help effectively implement your revenue growth strategies.
Background on Creating a Predictable Revenue Model
Achieving predictable revenue growth is a goal that every business owner strives for, and it requires the establishment of a well-defined and structured predictable revenue model. Above I take a creative look at how the metrics you measure matter. Here, let's delve deeper into how a predictable revenue system can be implemented, and how it can transform your sales processes and efforts.
The cornerstone of a predictable revenue model lies in understanding your potential customers and how they interact with your sales team. To consistently generate revenue, it's essential to comprehensively map out your sales processes. This involves identifying every touchpoint from lead generation to closing deals and beyond. By breaking down the sales journey into its constituent parts, you can create a predictable revenue system that is based on data-driven insights and well-defined strategies.
A crucial aspect of implementing a predictable revenue model is tracking progress meticulously. Just as in manufacturing, where precise measurements and quality control are essential, your sales efforts should be measured and analyzed rigorously. This allows you to identify areas where improvement is needed and make data-driven decisions to optimize your sales processes.
One of the key advantages of a predictable revenue system is that it takes the guesswork out of business growth. Instead of relying on hunches and intuition, you can rely on data-backed insights to make informed decisions. This not only reduces risk but also enhances the efficiency of your sales team, enabling them to focus their efforts on strategies that have a proven track record of success.
The sales team becomes more adaptable and responsive to changing market dynamics in this data-driven approach. You can quickly identify trends and adjust your sales strategies by closely monitoring the metrics that matter. This agility is invaluable in today's rapidly evolving business landscape.
To summarize, implementing a predictable revenue model is a transformative journey for any business owner. It involves understanding your customers, mapping out your sales processes, and establishing a data-driven system to consistently generate revenue. By doing so, you not only reduce uncertainty but also empower your sales team to adapt and thrive in a competitive marketplace. So, take the leap into the world of predictable revenue, and watch your business grow with confidence and clarity.
