Hello folks! Just like last month, we’re hijacking our Feature Highlight column to bring you the sister paper to the Causal Hypothesis. As with any technological change, Process Mining, especially with our radically new approach, requires cultural change as well. We call this cultural change Process-Oriented Decision Making, and we’ve just published another paper talking about what this means and what the benefits of this are for organizations who struggle with taking control of their KPIs effectively. Hint: we think this means most organizations that use classical KPIs.
We call this second paper Measure How, Not Why: The Process-Oriented Decision Making Manifesto, and you can go ahead and grab it here or read on for a quick summary of what kind of thoughts it contains!
Anyone working in the Product Management space will be familiar with the terms Product Metrics and Growth Metrics as a way to split what metrics contribute to our value proposition and which are a result of it. While this concept is extremely important to creating actionable targets and incentives, it’s not unique to digital products and SaaS. Essentially, Growth Metrics describe high level business success like Revenue and Active Users, while Product Metrics describe how our users interact with the product – conversion rates, user retention, number of support requests, etc. While Growth Metrics are the goal – higher revenue means more business success – we get there by increasing our Product Metrics.
The same can be applied to a traditional business. To make the terminology more broadly applicable, we’ll refer to Growth Metrics as “What” Metrics and Product Metrics as “How” Metrics. Taking a classic Sales process, an example of “What” Metrics can be Yearly Revenue, while the “How” Metrics can be something like Sales Calls Per Day or Time to Offer (the time it takes for us to send an offer to a party we find out is interested). Both contribute to an increase in revenue and are much more locally actionable – while revenue is very often affected by global factors like recessions and industry trends, how long it takes us to write and send an offer is almost entirely in our hands.
Unfortunately, it’s much more common to see the main KPI being measured in organizations utilizing data driven decision making be Yearly Revenue than something like Calls Per Day or Time to Offer. We think this is because it’s much easier to generate this information, especially with classical Business Intelligence tools. How Metrics are often hidden behind layers of investigation and data availability, while What Metrics is the end-result of these complex processes and therefore often available at the surface.
This is tightly linked to Process Orientation – the idea that the organization’s mentality and structure should be oriented towards the core business processes that drive the organization’s value chain. Some might know this more closely as Customer Centricity. The idea is traditionally that measuring and incentivizing on the high-level goals will trickle down Process Orientation at a micro level and improve employee contribution to the factors that drive growth in these goals, but in our extensive experience, having high-level goals creates a feeling of paralysis and lack of agency, as those external factors often feel like a larger force than whatever we can do as individuals contributing to the machine.
This is why we call our proposed philosophy Process-Oriented Decision Making. In essence, we propose a complete rejig of the kinds of metrics organizations measure, dropping What Data as the target, and accepting How Metrics as the most effective way to drive organizational efficiency and performance. This is not a novel idea, and it has proven precedent on the market: giants like Facebook and Google target just one What Metric as a goal, but incentivize on the How Metrics that contributes to it. The Objectives & Key Results (OKR) framework, which has been gaining traction worldwide, suggests a shift like this as well.
There’s one key reason that Google and Facebook can use this approach natively – they grew up around this philosophy and built their measurement devices around it, embedding product analytics at every level of their customer and employee interaction flows. Classical organizations don’t have this luxury – or do they?
Nowadays, the vast majority of organizations of at least SME scale are deep into their digitalization journeys, which means most business processes are performed digitally. This means there are digital traces being left behind by every interaction with the digital systems. These can be leveraged to create these same kind of product analytics, for both internal and external business processes. How do we do this? You guessed it – Process Mining. By crawling these data traces and contextualizing and linking the resulting data points, we can create this Digital Twin of the organization, just like we described in our previous white paper.
Once we have this network of process and context data in place, we can perform root cause analysis on any What Metric goals that we decide to be of strategic importance to our organization and identify which How Metrics contribute to which extent to these goals – at unprecedented granularity. In doing so, we can increase the effectiveness and actionability of our organizational efficiency management.
While we’ve tried to do a good job of summarizing the essence of the paper, we’ve only really touched on the high-level topics. In the paper, you’ll find more in-depth explanations on these approaches, additional precedent, and, perhaps most importantly, a step-by-step guide to getting started on implementing this concept in your organization. Don’t worry – we intentionally left it less technical and much shorter than our previous paper, because we know that the target audience for this kind of dossier is much busier and more attention-bombarded.
If this has piqued your interest, go ahead and grab the Manifesto above. If you want even more details, or if you would like help customizing the implementation journey to your organization, please reach out to and book a non-binding consultation, or just book a call to have a general chat. We’re always happy to interact with our readers and hear more about their concrete challenges – it helps us understand how we can best help solve them.
Until then, keep innovating!