Underlying Principles vs Frameworks: OKRs

Nov 23

Growing up as a digital native, I learned many concepts as they were being developed (analytics, SaaS, Cloud).

Today, folks are presented with established concepts (aka, frameworks) and adopt them without knowing the underlying principles.

One of these frameworks is using Objectives and Key Results (OKRs) to run your company, organization, team, product.

As part of the OKRs framework, we SHOULD collect data, watch trends in the data, dream of a future state where that trend is better, and create work to move that trend in our preferred direction.

However, most companies seemed to have just started at the end by dreaming of a better future (aka, setting goals/OKRs) and not investing in collecting data, teaching their employees to find trends and certainly not connecting everyday work to improving these trends.

Use the graphic below to evaluate your progress in using data to improve yourself, your teams and your company.

Where are you in this progression?

“Swimming in data”
“Figuring out what’s relevant”
“Picking a focus”
Evangelizing “Why it matters”

Case Study: From 0 to 60 Million

When my team grew a website from 0 to 60 million annual visitors, we obsessed about results. We didn't specifically set goals or use OKRs. But we analyzed the heck out of visitors' behaviors.

We picked a couple Key Performance Indicators and obsessed over growing those KPIs. I suppose you could call our goal... "make a lot of money".

To get good at this, we evaluated dozens of possible metrics. Only a few of them qualified as Key Performance Indicators since their movement (up or down) signified something substantial had happened to the business.

There were many times where we recognized we were not collecting important data, collecting unnecessary data or had faulty data collection. So we fixed it.

If you're currently focused on OKRs, consider zooming out to see if you and your teams are looking at important KPIs and analyzing if you've got the right data to make decisions.

Becoming an Analytics-Driven Organization

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Underlying Principles vs Frameworks: OKRs

Today, folks are presented with established concepts (aka, frameworks) and adopt them without knowing the underlying principles.

One of these frameworks is using Objectives and Key Results (OKRs) to run your company, organization, team, product.

As part of the OKRs framework, we SHOULD collect data, watch trends in the data, dream of a future state where that trend is better, and create work to move that trend in our preferred direction.

Video: When Am I Done Solving a Problem?

An exploration into how the early wins from the 80/20 rule inevitably hit the ceiling of an invisible asymptote, exposing our battle with the law of diminishing returns.

Our Half-Baked Adoption of OKRs

Many companies adopt the Objectives and Key Results (OKRs) framework.

They create goals but struggle to achieve them.

Only some of this failure can be blamed on the goals themselves.

Part of this failure can be attributed to a lack of an analytic culture and a lack of an analytic mindset in your key employees.

How do you know you're successful today?

It’s a simple question.

But often there’s an unsatisfying answer.

The first step towards understanding whether you're successful today starts with recognizing (and using less of) the following types of data: “Anecdata”, Reactive measurements, and Internal metrics.

Product Ops: Managing Data Analysts

Data analysts provide the eyes and ears for Product Leaders. A few simple tips can maximize this relationship.

Over the years, I've managed and helped grow the career of many analysts and data scientists.

How Leaders Can Motivate Teams to Analyze Data

Making data easy to access is not enough to avoid the “analytics-free” environment.

Leaders need to provide the proper motivation.

A broad definition of motivation includes compelling teams to change their behaviors, holding them accountable, avoiding demotivation and praising teams who are early adopters of consistent data analysis.

Case Study: Creating team-level success metrics

At a recent client, some product groups (3 to 5 teams each) were using analytics but most were not. In one of these groups not using analytics, there was an overwhelming push to deliver. Once they delivered, they moved on to the next feature without measuring success or failure.

Making it Easy to Access Data

BJ Fogg, PhD, a Stanford behavioral science researcher and author of Tiny Habits, has created a simple, yet powerful model of how to change behavior.

If users are more motivated and the desired action is easier to do then the behavior change is more likely to happen.

I’ve applied his Behavior Model to data analysis transformation.

When leaders make data easier to access and motivate their teams to analyze data then more decisions will be based on data analysis.

A Note on Analytics Design

Teams can and should design the collection of their own data.

An Unsettling Trend: The Analytics-Free Environment

Too many Product teams operate in an “analytics-free” environment.

They don’t track product usage, business results or customer satisfaction.

Without analytics, we lack the "eyes and ears" to see and hear what’s happening with our users.

Make data-driven decisions by turning your vision into a math equation

Many Product teams struggle about what to do next.

Learn to prioritize everyday work using business outcomes. It made our website profitable and grew our traffic to over 5 million monthly users.

Be lazy and smart. Find and automate your leading indicators.

Find your leading indicator. Then automate it.

Read about how using a leading indicator can make you a better Product leader.

Improve Conversion by Using a Consumer Decision Tree

When confronted with too many options, customers will stop in their tracks.

Too much choice creates confusion which prevents conversion.

A time tested way to solve this problem is to create a Consumer Decision Tree within your application or website.

Jim coaches Product Management organizations in startups, growth stage companies and Fortune 100s.

He's a Silicon Valley founder with over two decades of experience including an IPO ($450 million) and a buyout ($168 million). These days, he coaches Product leaders and teams to find product-market fit and accelerate growth across a variety of industries and business models.

Jim graduated from Stanford University with a BS in Computer Science and currently lectures at University of California, Berkeley in Product Management.

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Underlying Principles vs Frameworks: OKRs

Case Study: From 0 to 60 Million

Becoming an Analytics-Driven Organization

SF AI Hackathon