How Product analytics influence trial conversion?

SaaS products offer trials to their customers to help them clear any doubts that they have and make an informed decision.

Companies are now counting on product-led growth. Good product adoption during the trial is crucial to ensure conversion. Beyond just onboarding tools it is important to understand the gaps product users are facing.

Getting to AHA!! moment in the trial
It has been talked about a lot and also known as Time to the first value. It is a nice notion but very complex to implement for most of the products. Most of the users have a specific pain point for which they are exploring the product. If the product solves that problem quickly that is their AHA moment. Depending on the type of the product it can mean tracking just a few features to even hundreds.

It is vital to correlate the persona of the user, business (B2B solutions), and features that have driven them for success.

The product should continuously evolve to ensure that users can find their AHA moment quickly. For this to happen, the interest of the user should be captured quite early in the trial journey.

Understanding the reason for signing up can be achieved by tracking of parameters like

what are the users searching on the help sections?
what are the set of features they are exploring? are they looking for help in that?
Have they achieved the goals of the feature? if not, where are the friction points.
Answering these questions and evolving the product accordingly reduces the time to discover the AHA!!! in your product, resulting in better trial conversions.

Segmenting trial users based on behavior and interest
I have talked about the various KPIs to track during trial, but in the end we need list of users who have or have not used certain features.

While working with multiple SaaS product teams I observed that the same yardstick does not apply to all new signups and trial users. Many signups are what I call “Pseudo Signups”, they just explore the product but do not have any intention of using it in long term. These users’ usage patterns should not be taken too seriously in the product roadmap.

If you are offering a trial in your application, it is important to track the usage patterns of all the users, weed out the pseudo signups, and focus on serious users.

Segmentation should be done on both the engagement and feature usage.

For engagement level segmentation, there should be active cohorts of trial users like

Pseudo Signups: users who have little to no sessions, most of the features partially used, jumping between pages too fast.

onboarding in progress: Users who are still getting onboarded. Even better would be cohorts based on different stages of onboarding.

Onboarded: Self-evident.

Engaged users: Users having multiple sessions, using features seriously, and responding to product messaging and other stimuli.
Product teams should also segment the trial users based on product adoption and usage. It should be applied to all the above cohorts.

Onboarding funnel: Understand the lost users based on various stages of the onboarding process.

Feature friction: What stage of a feature provides the biggest impedance for users.

System performance and usage: Check how page load times and API latency is impacting usage. Are these things impacting adoption.

Hero feature utilization: Features that drive conversion the most. Two cohorts must be created, one that has used it and one group that has not used it.
Challenges of tracking Feature adoption during trial
Features in SaaS products are quite complex. A user needs to perform many actions to get to a goal. Let us take an example to understand this better.

Feature : Update profile details User actions
Go to update profile page
Open a form to edit
Possibly click on submit
Get a success response from Application server
In this process, tracking only the Goal event is not sufficient. Users may be totally interested in using the feature but maybe facing a disabled button, bad UX, system performance issues, and API failures. Unless all the events are not tracked in connection with the feature, it’ll be impossible to track friction in the feature.

New releases and changing feature flow compounds this problem for Product teams.

At Fibotalk, we understand these problems very well and that is why we focus highly on ease of implementation and tracking all user actions by default. This enables product and customer success teams to understand user behavior in depth.

In B2B SaaS companies, you need to understand the account level usage as well to reduce churn. In Fibotalk, we connect all the user and account dimensions with each event to ensure deep-dive analytics. here is a detailed post on this topic.

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