Activation Velocity: The KPI You Need To be Tracking in User Onboarding

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Covered today:

The story behind how and why Shopify shifted KPIs used to measure user onboarding.
More on GTM for your eardrums.
More on GTM for your eyeballs.
Startups to watch.
Hottest GTM jobs of the week.
GTM industry events.

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User onboarding is the linchpin of the customer journey. It’s similar to making a first impression where eye contact, a handshake, and a smile significantly shape initial perceptions and future interactions.

User onboarding sets the tone for the relationship to come. Often, it’s the customer’s first real interaction with a product post-purchase. A positive onboarding experience can set a constructive tone for the user’s entire lifecycle with the product, fostering trust and engagement.

This is true both for both sales-led motions and product-led motions.

For product-led, those employing a product-led growth (PLG) motion, the process is highly data-driven with specific KPIs serving as gauges for impact and success.

With that, measuring the right metrics is key.

To help dive into a core KPI associated with user onboarding, I teamed up with product expert and user onboarding consultant, Adam Jarczyn.

Below, Adam breaks down the KPI Activation Velocity and shares the story of how Shopify adopted this KPI for user onboarding, the impact that this had, and how it can be considered for any company.

Let’s get into it.

Improving User Onboarding by shifting from Time-to-Value to Activation Velocity – a success story from Shopify

The story behind how and why Shopify shifted KPIs used to measure user onboarding

Joining Shopify and inheriting the KPI Time-To-Value

Adam led user onboarding at Shopify. When he joined, there were 3 key challenges:

The team wasn’t hitting its performance targets.
The team reported to Revenue leaders who didn’t understand why the team was missing targets – was this a team problem? A communication problem? A management problem?
It was unclear what the impact was on merchants (Shopify’s customers).

The initial KPI was time-to-value (TTV). It’s the standard metric of success in the user onboarding world and describes the average amount of time it takes a user/customer to activate.

For Adam, TTV felt wrong. The KPI wasn’t rooted in any feasibility. Averages hide insights and he felt TTV couldn’t describe the nuances or uncover why the team was missing targets.

Adam, reporting to Revenue Leaders, needed to explain a) why onboarding underperformed and b) outline improvement plans. Held to sales team standards, he needed to forecast, analyze data comprehensively, and control inputs to predict monthly outcomes.

Adam’s approach was:

Evaluate if the TTV metric was feasible. If not, make a case for what it should be.
Demonstrate he could forecast Activations, project monthly targets, monitor for early warning signals, and show the team could impact customer outcomes and revenue.

Adam’s solution was to reset leadership’s understanding of baseline performance using a new metric: Activation Velocity. Applied comprehensively, Adam could answer the same questions as his sales counterparts.

What is Activation Velocity?

Activation Velocity is a metric that describes how a cohort (group) of users reaches a key onboarding milestone (Activation) over time. Below is a sample Activation Velocity chart. It shows the % of users who activate over time for five cohorts of users (January to May):

Let’s look just at January. Assume 100 users sign up for your software in January, all on Jan 1. You want to understand how those 100 users become paid users (Activated) over time.

Out of 100 January sign-ups, 25% activated by January 14 (end of week 2), and 65% by January 28 (end of week 4). By week 10, 83% had activated. This pattern of activation, visible in the 5-line graph above, shows cohorts typically activate at similar rates. But the February group appears different. This will be addressed later.

Creating your first curve is relatively simple. You only need two data points for every user/customer and you can analyze in a simple spreadsheet. The data points are:

Start date: the date a user completed a simple signup form (e.g., email and password).
End date: the date the user became a paid user (activated). If a user did not activate, the date would be empty.

Why Activation Velocity is powerful

There are 5 key benefits to visualizing users/customers in cohorts using Activation Velocity:

1. Not all users are equal

Not all users will go through an onboarding at the same speed. Some are more tech-savvy. Some are experiencing a current pain and are highly motivated, Some may just be exploring.

TTV dilutes this nuance while Activation Velocity reveals this spectrum of motivations. It allows you to identify a true baseline of performance. Looking back at the 5-line example, a trend emerges about how fast a group of users activate. TTV can’t show you that.

2. Invest in the steepest part of the curve

The steepest part of the curve indicates peak momentum in onboarding. That’s your biggest window of opportunity. Helping as many users as possible in that window has the most impact on activation.

In the example below, the steepest part of the curve is between Week 1 and Week 4. A user has a 65% probability of activating in the first 4 weeks. After Week 4, there’s only an 18% probability to activate.

Focus on helping the most users in this period.

At Shopify, Adam found that the initial 6 weeks were crucial (steepest). His team decided to contact users every five days during this 6 week period. After the 6th week, the team stopped. Adam observed a significant improvement in Activation Velocity.

3. Reconsider how you invest in the flattest part of the curve

Similar to focusing on where momentum is highest, consider the investment in low-momentum (flat) periods. For instance, in the graph below, after week 4, activation probability drops to 18%. Initiatives like win-back campaigns or assisting users older than 4 weeks may require high effort for low-probability gains.

At Shopify, after Week 6, Adam’s team stopped their 5-day outreach program. They focused on what they could to make the best possible experience for the first 6weeks of usage.

4. Using Activation Velocity for early detection

Time-to-value is a lagging metric. For example, if your TTV benchmark is 60 days, you need to wait at least 60 days to know if your cohort hit that benchmark. That’s way too long to wait. Using Activation Velocity, you get early detection.

Look at the graph above. Users who signed up in February lag behind by week 2 compared to January. What caused this? Was there a product or marketing change? Market shifts? Seasonal factors in February?

At Shopify, Adam used this approach to quickly identify troubled cohorts. He could dig in early to understand why that cohort was underperforming and course correct.

5. Your leadership team will understand

Activation Velocity made it easy for Adam’s leadership to understand onboarding performance. It was simple to spot trouble areas or areas of improvement. The faster cohorts activated (steep), and the higher the % of a cohort activated (how high the line plateaued), the more revenue for the business. With this tool, Adam could tell the story of onboarding, make a case for the team’s growth, and shift the leadership perspective that onboarding is a revenue driver, not a cost center.

Optimizing using activation velocity

Here are a few ways Adam applied Activation Velocity to optimize at Shopify:

1. Scaling onboarding

The onboarding team initially relied on human-led onboarding. However, sales growth outpaced their capacity, prompting Adam to test a content-led approach. He took a segment of users and dripped them with content. Using Activation Velocity, he could compare the performance of human-led vs. content-led.

The results: customers in the content-led journey activated faster and the approach was materially more scalable.

2. Finding blockers in an onboarding journey

Activation Velocity curves need just two dates: a start date and an end date. Adam replaced the sign-up as the start date with the date when a user first used a specific product feature. The end date remained unchanged. This adjustment allowed Adam to assess the correlation between a product action and Activation.

A flat curve suggests users are stuck, either due to a prolonged gap between a product action and activation or a low correlation between them.
A steep curve implies the product action is critical for activation or occurs at the end of the onboarding experience.

Adam recommends using the above approach to find areas of onboarding delay.

3. Updating the definition of Activation to improve retention

Don’t dwell on defining activation too long. As onboarding improves, you’ll naturally wonder, “What do users do after they activate?” This is where great onboarding improves retention. For instance, if you’re satisfied with trial-to-paid conversion, consider updating activation to trial-to-three-month retention and follow the same approach to improve.

Experiment with Activation Velocity

Starting with Activation Velocity is straightforward, requiring minimal data to gain insights into your onboarding performance. Like any growth initiative, it’s an iterative process. Begin by understanding your baseline and gradually collect more data on feature usage or other user interactions. This allows for creative optimization.

Once optimized, broaden your focus by refining your Activation definition.

Activation Velocity is a great way to understand and communicate the performance and impact of onboarding.

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That’s it, that’s all.

Having recently made a handful of software purchases with user onboarding experiences ranging from exceptional to below average, the impact of this stage has been particularly evident.

It sets the tone for the relationship, much like the first impression you make when meeting someone new.

I hope this not only informs your go-to-market strategy but also inspires you to be particularly mindful of the importance of that initial encounter whenever you meet someone new.

Barker