Last week, we discussed Adjacent User Theory—a powerful growth framework for identifying opportunities to expand into tangential markets.

As a quick recap:

• Core Users: People your product was built for and who “get it.”
• Adjacent Users: People who could use it, but it needs to be modified or communicated differently to them. The goal of Adjacent User Theory is to identify the right adjacent users to expand into.

One of the powerful parts of this theory (that I didn’t cover last week because I think it muddied the conversation) is what I like to call “tipping points.”

Think of them as moments or conditions when a user is on the cusp of becoming more engaged or converting into a paid customer.

Products (ecomm and software) have tipping points at various stages.

Let’s dive into some examples.

Slack example of tipping points

The primary thresholds that a Slack user has to cross through are:

• Not Signed Up → Signed Up
  • Ex: A friend invites them to Slack
• Signed Up → Casual (occasional free user)
  • Ex: They open Slack whenever they’re pinged
• Casual Free → Core Free (active free user)
  • Ex: They open Slack to speak regularly
  • Ex: Maybe they even start their own Slack channel
• Core Free → Monetized (active paid user)
  • Ex: They upgrade to a paid user on someone else’s or their own Slack.

A tipping point is when they’re on the line between one stage and the next.

For example, Slack determined that the tipping point between a “Casual Free” and a “Core Free” was their weekly active use. If they used Slack 3 out of the last 7 days, there was a 50/50 shot they’d either churn entirely or become an active user.

Armed with this information, they knew they had to do everything they could to push people to be active more than 3 days per week. Things like:

• Encouraging them to download the mobile app
• Encourage them to enable notifications (of any kind)
• If they don’t enable push notifications, send them emails
• Seamless onboarding that lets them discover the value of Slack quickly

They knew where to focus their efforts.

Instagram example of tipping points

Here are the thresholds for Instagram:

• Not Signed Up → Signed Up
• Signed Up → Activated
• Casual → Core Usage

They discovered that if a “Signed Up” user had more than 10 followers in the first 7 days, there was a >65% chance they became “Activated.”

This is the counterpoint to the famous Twitter example where if someone followed a certain number of people, they’d be more likely to stick. Here, Instagram users were more likely to be engaged the more followers they had.

If you’re an Instagram user, you’ve definitely received these notifications before:

These trigger in various scenarios, but a common one is when someone creates an account on Instagram. It can fire them out to:

• Facebook friends
• Contacts in your phone (or if you’re a contact on their phone)
• Mutual friends
• Shared educational background on Facebook

These were created so that new users would quickly gather followers so that people became activated users.

How to find and act upon tipping points

I’ll leave you with some tactical tips on how to take action here:

#1. Make sure you’re tracking everything.

You can’t make decisions if you don’t know what users/customers are doing. Every click should be recorded and associated with the user. Do that in GA4 and/or Mixpanel/Amplitude and/or your ESP.

#2. Identify the various stages that a user goes through from discovery to full converted.

A stage can be defined by either them taking action (signing up, subscribing, purchasing) or by a more complicated set of variables (daily active, does X things per month).

See the next step for examples of different variables.

#3. Analyze the behavior of people who blew through thresholds and those who did not.

Try to identify the variables of “things they did/didn’t do” that lead to someone significantly more likely progressing through the threshold.

For example, this could be things like:

• Purchased the consumable product X times
• Have used the product on X of the last days
• Have used X and Y features (either at all or a threshold amount)
• Have opened (or clicked) X of the last Y newsletters/product emails
• Have at least X team members
• Follow your account on social media
• Have downloaded the mobile/desktop app

There are a lot of variables to consider!

Since this comes from Adjacent User Theory, you’ll also want to try to learn who these people are. Are specific personas getting stuck while others are not? If so, that could be an opportunity to change the flow/messaging to convert them better.

#4. List strategies for how to get people to reach and exceed the tipping points.

For example, if you identify that customers who buy at least 6 of your consumable DTC products are X% more likely to become paid subscribers (or loyal repeat purchasers), then list strategies to increase the likelihood they order at least 6 of the product (either all at once or in separate orders):

• Bulk discounts (shipping or discount)
• Sell a starter bundle
• Send reminder emails or texts to stock up
• Inserts into product deliveries that encourage repeat purchases
• Surprise gift (like a sample of a new flavor)

Now, go put this into action!

There are infinite ways to adjust your marketing or product to increase conversions.

Identifying tipping points helps you learn exactly where to apply force to get the maximum leverage. They provide clarity and significantly increase the odds of success for a test.