Providing a trial subscription is an intrinsic part of the marketing process for any SaaS company. But this is a very delicate phase of engagement where the customer is evaluating the product.
At Fibotalk, we have worked with multiple SaaS companies, and we have realized one thing: the same yardsticks do not apply to all. Instead, a customer’s affinity to convert from a free trial to a paid subscription depends on various factors such as business model, size of company, and nature of the product. This post looks at a step-by-step guide that tracks the relevant engagement points to arrive at the optimum set of metrics for trial conversion.
The main objective of tracking trial conversion is twofold. First, to segment the customer, and secondly, to track their progress through a funnel based on a quantitative analysis of the various interaction points.
Once a customer signs up for a trial account of your product, you can segment them into three buckets, Signed-up Users, Onboarded Trials, and Engaged Trials. You can broadly treat these segments as a three-stage funnel. This segmentation helps you build customer cohorts, which are analyzed separately for specific trends and patterns.
Depending on your product’s complexity, you might have to build additional segmentations for features and customer journeys. This approach is helpful to analyze the feature adoption and stickiness of your product at a later stage.
Every product has multiple interaction points. To understand these interaction points, you can take a space-time analogy of a retail store. A retail store arranges shelves in spaces based on a floor plan layout. It also monitors data points by analyzing how customers are behaving in front of shelves. These data points can be derived based on stock depletion rate, video evidence of customers crowding near the shelves, and the time spent on each shelf.
Your SaaS product also has screen space, consisting of menus, pages, and panels that contain various product features represented in UI flows and widgets. The time customers spend on the screen spaces and their cadence represents some of the critical interaction points.
Apart from space-time related interaction points, there is also a factor of entry and exit context for each customer. For example, a customer’s referral source or high dropout percentage in a specific UI space represents these contexts. These contexts determine the health of a customer’s engagement with your product.
Once you have established the interaction points and built your segmentation and sub-segmentation strategies, it’s time to start tracking your customers. We will use the terms customer and user interchangeably to refer to the end-user of the product. Customer is used in the larger context of analyzing a group, whereas the user is used to track an individual person.
After you have successfully implemented the anchor code in your application you will automatically start getting the page load time as a system event in your charts.
All you have to do is create a chart with desired filters to get a proper representation that fulfills your purpose. E.g- you can group your page load time chart with on devices to get an idea of how your application is performing on different devices like laptop/PC and mobile and many more.
In this way, Fibotalk proves not only to be one of the really powerful analytics tools but also user friendly and easy to implement solution to give maximum desired output without much effort.
The first thing to understand about trial customers is that they are still prospects. So, unless you are offering a generous free tier to build a community, you have to gather intelligence about their trial-phase activities.
If your SaaS product is a simple tool, there is nothing much you can do to gather intelligence except for checking stickiness. This aspect is covered later in this post. However, if yours is a complex product, you have many interaction points to track. For example, in a SaaS-based CRM suite, customers have to perform some elaborate steps like creating user profiles, verifying emails, feeding or exporting/importing data, installing plugins, or performing some actions to enable integration with external systems.
Each of these actions is attributed to an event. It provides a fair indication of how serious your prospects are. Further, you have to track these events within the initial few days of signup to ascertain their commitment.
In Fibotalk, you can easily perform trial-phase prospecting by building a cohort of users based on a First Seen parameter to check if they logged in during the initial days post signup (for example, the first three days). This parameter can be combined with others, such as Subscription Type or Plan Name.
Further drill-downs allow you to track user activity and average time per session for each user.
Once the prospect is committed, you have to slot them into progressive trial phases based on your pre-defined segmentation for trial engagements. This step is similar to the trial-phase prospecting, except for the duration. The trial-phase prospecting focuses on the customer during the first couple of days after signup. After that, they must be tracked over a longer duration, split into phases. These phases should be granular enough to capture incremental value creation for the customer.
In Fibotalk, you can create user cohorts as shown in the pre-trial prospecting step, for a four to seven days and seven to fourteen days, assuming that you have a fourteen day trial for your product.
You can further drill down the feature usage for each cohort.
Along with trial engagement, you have to track the customer’s activity while using the hero features. The frequency of this feature usage is a crucial indicator for gauging the customer’s interest. It is measured based on segmenting the progression of feature usage into a few categories starting from exploration to completion.
Under the Fibotalk dashboard, you can define custom trent charts tagged with plan or subscription type as trial, to track feature adoption based on common indicators like a click event.
Stickiness is observed based on the frequency and duration of user events. These are measured at a granular level, down to the individual user’s actions. Standard metrics for stickiness are login frequency, duration per session, and cadence of feature usage.
These metrics are mapped to predefined user journeys you want your users to follow during the trial period. User journeys provide a good indicator potential of trial conversion based on a day or week-wise analysis of the stickiness metrics.
Fibotalk allows you to measure stickiness through different views such as dashboard, user journeys, and usage summary to track every user.
Finally, you need to take care of the churn. Too many churning customers at the trial phase is an obvious warning. But, more importantly, you have to find the friction points causing the churn and any patterns associated with them.
The easiest way to do this is to monitor the trends for daily active and inactive users. Additionally, tracing of incomplete journey sessions must be performed to correlate possible anomalies with the churn events. In most cases, these anomalies show up in navigational or application performance issues that the users cannot overcome.
Like the filtering options you have seen earlier, Fibotalk lets you define multiple such cohorts to slot the users across the entire trial phase to identify churn. For example, you can create a cohort specifically with a rule “last seen earlier than 5 days”.
As we mentioned earlier, no one size fits all. The metrics that you measure for tracking the health of trial conversion are subjected to different benchmarks based on your industry, product category, customer segments and price point, and more. Therefore you must prioritize them before jumping into the product analytics bandwagon.
Here are some of the typical scenarios for SaaS products that will help you choose your metrics wisely:
SaaS has a vast horizon. On one end of the spectrum are simple tools such as meeting schedulers, task trackers, or simple financial conversion/calculation tools. These mainly offer free trials in restricted versions of their feature set, and they open up the restrictions with paid plans. For them, usage and friction are the most important determinants of value creation.
On the other hand, SaaS products like CRM, accounting, and collaboration suites constitute heavy SaaS platforms with elaborate configurations and a rich set of features. These are also high-value products, requiring a significant investment of time and money on the customer’s part. In such cases, retention and decision influencer behavior tracking is a priority. And since these products have extended trial periods, custom cohorts to detect early signs of churn must be established.
If yours is an early-stage SaaS product, your entire focus should be on feature adoption and stickiness only. But if it’s a mature product, you have to look at other time and duration-based metrics, such as time to onboarding, feature usage, and login frequency.
Broadly, the customer segment falls only under two main categories, B2B and B2C. In most B2B products, the product’s value is generally high, multiple seats/users are involved in product evaluation, and implementation support is needed. Every new customer account is valuable in B2B and must be individually analyzed. When a new customer signs up, it is important to know their interest level (Stickiness). You have to ask relevant questions, such as “are they using the most valuable feature?” or “what is the stickiness of their decision-maker?”.
B2C products are generally volume-driven and low value compared to B2B products. While the metrics remain the same, attributes such as geography, demography, and acquisition mediums play a role in influencing trial conversions.
If you have recently launched a SaaS product, sign up for Fibotak to get started on your product analytics sojourn with zero engineering overhead.