Today’s post comes from Justin Mares, 500 Distribution Hacker-in-Residence, co-author of Traction Book and formerly the Director of Revenue at SaaS developer tools company Airbrake (acq’d by Rackspace in 2013). 

So you’re now convinced you should improve your activation, and are willing to dedicate some time to it.

Not much time — luckily, improving activation often only takes a few minutes a week to set up and run a new A/B test — but enough time to make it a priority for your company.

What should you focus on?

The first step is to map out the ideal path a customer takes after signing up for your app. To do this, ask yourself:

In an ideal world, what steps would every new user take after signing up?

To figure this out, begin with measurement.

 

How to Map Out Your Ideal Customer Flow

At Airbrake, the first thing we did to improve our lagging activation was measure the key actions our most successful customers took.

Airbrake is an error tracking tool developers use to capture any issues that might occur in their software. So, in order for a customer to get maximum value out of the product, they had to use the product to actually capture and track their errors.

This wasn’t just a hypothesis: after looking at the data, we saw that users who captured an error with Airbrake were 300% more likely to stay a customer than those that didn’t. Capturing an error with Airbrake was our activation threshold. Knowing this, we re-built our activation flow to help get as many people possible to that moment where they captured their first error.

In the Airbrake case, our ideal customer flow looked like this:

  1. User signs up for Airbrake
  2. Indicates which programming language (Ruby, Python, PHP, etc.) they use
  3. Installs and deploys a few lines of code in their app
  4. Captures their first error
  5. Marks their error as resolved

Take a minute and map out the ideal flow in your app — it should be simple, and should only require a few minutes to outline.

Once you have this ideal flow sketched out,  and have started measuring the steps in your funnel at which people are falling out, then you want to start running A/B tests to increase the percentage of users that move from one step to the other. Focus on improving your steps with the greatest drop-off first, especially those that occur earlier in the funnel. Then you’ll see gains all the way down.

Let’s go back to the Bingo Card Creator example from part 1 of this series.

 

Screen Shot 2014-06-11 at 9.02.29 PM.png

 

In Bingo Card Creator’s case, the ideal customer signs up, hits their dashboard, creates a list, customizes their list of bingo cards, schedules a print run and then downloads the cards.

Given these steps, you can see the largest drop-off in the activation funnel is from the “Create List” to the “Customize” step. This is the step you’d want to optimize.

 

How to Optimize Your Activation Threshold

In your early stages of optimization, you’ll want to collect qualitative data around why your users are failing to customize their lists. You can get this data in a few different ways:

  1. calling people who haven’t finished a key step (bribe them with gift cards if you have to, but you’ll be surprised at how far you can go by just being friendly and asking),
  2. setting up a survey tool like Qualaroo
  3. watching users go through your onboarding process, ie at Starbucks.

For example, let’s say you talk to 15 people who have created lists of bingo cards but haven’t customized those lists. And, after talking with them, you found that 9 of them couldn’t think of how to customize a list, and planned to finish it “later.” That’s a huge insight!

You’ve now figured out a major roadblock to activation.

You can now use this information on drop-offs to design an intelligent A/B test and see if it increases the number of people customizing their bingo card lists.

For example, you might try sending a triggered email to users who’ve created lists and haven’t customized that list for 24 hours. This email would then show users how to customize lists, and cover the reasons why someone would want to do this. You could also test making customization easier, suggesting different ways in-app that users could customize, or have a popup or product walkthrough that shows users why they should customize a list, and touches on how to do it.

In short, there are a lot of creative solutions to improve drop-off points.

Some other ideas for ways you can increase activation rate and smooth out those drop-offs:

1. Have amazing support. Support can be critical for users who might be confused after signing up. One useful trick is to tag users who’ve signed up in the last 30 days and make sure they receive priority support. Not only does it lead to a great customer experience, but if you can quickly resolve an issue before your users get frustrated there’s a good chance they stay active in your app and don’t just get frustrated and bounce.

2. Automated welcome email. Colin of Customer.io talks about this tactic of sending a personalized email from the founder within an hour of a user signing up. In this email, just ask how you can help or check and see if they’re confused about anything. This gives new users the ability to ask any questions they may have, and gives your team an understanding of the kinds of problems new signups are facing. Some companies take it a step further and try to call every new user that signs up. Often, these conversations are great times to discuss why a user signed up, what they’re hoping to get out of your product, and get a sense for how likely someone is to upgrade down the road.

3. Onboarding flow. Creating a first-time-user onboarding tutorial or flow can be critical in including your activation rate. Again, Twitter is great here – your experience when signing up for a new account is completely different than the experience you have the rest of your time as an active Twitter user.

4. Lifecycle emails. We touched on this above, but lifecycle emails that expose new features to a user – especially if you can base them on actions a customer has already taken in your app – are huge for improving your activation rates. These are the kinds of emails you’ll get if you sign up for Twitter but don’t follow anyone. Twitter knows the number of people you follow is a key activation metric for them, and will send emails reminding you to follow a few people (and suggest individuals you may want to follow).

 

Conclusion: 6 More Steps to Better Activation in SaaS

We’ve covered why activation is important for SaaS, common problems SaaS companies face in activating their users, gone through how to increase your activation rate, and lastly given some ideas about tests you can run to improve this key metric.

In summary, if you’re interested in improving this critical metric, here are the steps you should take today:

  1. Set up proper analytics. More on that here and here. You can’t measure what you can’t improve!
  2. Track multiple steps in your activation funnel. If you don’t have a clear idea of what actions should constitute an active user, take a look at your best (or most profitable) customers. What actions did they take after signing up? What actions did they take before becoming a valuable customer? Those are the ones you’ll want to encourage other users to take.
  3. Create an activation pipeline like the one for Bingo Card Creator above, and take note of the step with the largest drop-off rate.
  4. Design an A/B test that uses some of the techniques we covered above to improve activation.
  5. See what kind of impact your test has had on the activation numbers. If activation has gone up, roll out the improvements and begin another test!
  6. Rinse and repeat until your activation rate is where you’d like it to be (at least above 60%).

In the next post, I’ll cover another key metric many SaaS companies struggle to improve: retention. Until then, stay tuned.

Justin Mares is a Distribution Hacker-in-Residence at 500 Startups. Justin is co-author of Traction Book and formerly the Director of Revenue at SaaS developer tools company Airbrake (acq’d by Rackspace in 2013).

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