Framework for subscription optimization: tips from behavioral science expert Sviat Hnizdovskyi (Applica)

In this episode, Applica’s founder and behavioral science expert, Sviat Hnizdovskyi talks about subscription optimization tips and his framework for experimentation that helps properly evaluate all the historical product experiments that were conducted in the past and determine the low-hanging fruits and bottlenecks.

For the episode 23 of the Subscription League Podcast, we had the pleasure of discussing tips and frameworks with a behavioral science expert and the founder of a product growth agency, Applica, Sviat Hnizdovskyi. Sviat shared valuable insights on how companies can improve their app subscription, LTV (customer’s lifetime value), and monetization. 

Read on to learn more about his tips for optimization subscriptions, Applica’s experiment history review framework, Sviat’s passion for behavioral science, and other key takeaways from the episode. Don't forget to listen to the full episode.

Sviat Hnizdovskyi’s Passion for Behavioral Science:

Sviat Hnizdovskyi has an academic background in social and cognitive psychology, and he applies his knowledge of behavioral sciences to help companies optimize their growth processes. He uses well-known frameworks, such as Psych, to understand users’ behavior and their motivations for sharing data about their fitness goals.

Experiment history review framework

The discussion with Sviat highlights Applica’s framework to optimize subscriptions called ‘experiment history review framework’. The framework has proven effective for apps that have already performed around 100 or more experiments. Sviat recommends exploring a few aspects when implementing the framework:

1. Understand your experimentation capacity

2. Focus on overall paywall strategy

3. Determine the subscription duration optimal period for your specific app. 

4. Optimize the first impression

Following are the 6 steps of the framework.

Step 1: Categorize historical experiments

Go through your entire history of experiments and assign each one a category. For example, in monetization optimization, some main categories are subscription pricing, trial length, paywall design, onboarding sequence, special offers.

You can expand or contract specific types depending on how many tests you've run in each category. For example, if you've conducted many onboarding experiments, you can break them down into subcategories: number of screens, screen order, screen content, and so on.

Step 2: Revisit the main metric change for each experiment

Evaluate your successes in each experiment. For example, determine the variant with the best chance in LTV that every single experiment brought, the worst change in LTV that it brought, and the difference between the two. It gives a hunch on how reasonable and effective your hypotheses were.

Step 3: Define the average metric change per test within a category

Now you’ll have to create a Pivot table. Determine the average increase in LTV after each test within the category for both best and worst variants, and count the number of experiments per category.

Step 4: Sort categories by most significant average metric improvement

Step 5: Keep track of diminishing returns (plateau) of optimization within each category

The last step in the macro evaluation of your experimentation history is building a chart to visually assess whether you have reached an optimization plateau in each category.

Step 6: Adjust your Impact scoring within ICE/RICE.

Adjust your Impact parameter of the RICE and ICE frameworks, according to the strength of the category, besides just the strength of the idea itself.

More about Sviat

Sviat Hnizdovskyi is currently the founder of Applica a London based product growth agency. Sviat has years of experience in behavioral economics, social & cognitive psychology, and data-driven digital product analytics. During his time at Applica, Sviat has helped companies like Loona, FitMind, Fabulous, Drops, and Freeletics increase significantly their app ARR. Recently, he  founded the Open Minds Institute - an international think & do tank that cultivates open-mindedness and behavioral change for peace & freedom.

Episode 23 Sneak Peek

On first impressions

“80% of the purchases are happening within the first 10-15 minutes of exploring the app. Therefore, it’s crucial to ensure that what you communicate from the very first screen is understandable.”

On the experimentation framework

“Categorize your experiments and perform sophisticated analysis once you have conducted a sufficient number of experiments.”

“Evaluate your experiments to understand which category is the most optimal for you to target with all of your future experiments.”

On the subscription optimization

“Understanding the subscription duration optimal period for your specific app. If it's health and fitness or anything related to education, it's mostly the choice between having monthly and yearly subscription or just yearly subscription.”

Episode Topics at a Glance

• Sviat’s Passion for Behavioural Science. 

• Strategy to increase ARPU 

• Tips for subscription optimization

• Experiment categories

• Applica’s experiment history review framework

• Pre-requisites to run Sviat’s framework. 

• Type of companies Sviat is working with to build this framework. 

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