In various industries like gaming, e-commerce, and finance, there are tremendous pressures and expectations placed on Product Developers and monetization managers to find creative and ROI-driven ways to increase revenue. In order to generate scale, companies are utilizing various testing practices to capture, analyze, and predict data. These practices rely heavily on A/B testing and hands-on manual work. Thankfully, Simpool, an Israeli-based startup, has developed an AI-based sandbox that analyses end users’ journeys, enabling them to answer essential questions for monetization. Simpool’s sandbox allows numerous ‘what-if’ scenarios and empowers developers to make solid future decisions that result in happy long-term paying users. This can arguably lead Simpool’s platform to become the biggest sandbox venue for developers and monetization specialists. 

We happily had the chance to interview Guy Bar Sade, Co-Founder & CEO at Simpool, and ask him various questions relating to his startup’s progress, as well as the developers industry as a whole.

Q: How does Simpool help companies answer the “What if?” question?

Guy:  Managing an online application is not easy. It means an endless battle for profitability, and it’s getting more complex as CPI prices vary and constantly increase as the market gets crowded.

These constant efforts to optimization usually mean using a traditional AB testing approach, which relies on exposing different user groups to different application variants and then choosing the best performing variant. The major cons of this approach are :

1. It requires the technical capability to tun a valid AB test, with its strict rules

2. A valid AB test must contain enough samples(users) in order to be significant. These users cost a lot of money.

3. The interesting impact of a test usually doesn’t end after a few days but can suppress for weeks and months.

Simpool’s unique technology reduces these dependencies significantly. By Utilizing a machine-learning algorithm to an advanced simulation engine that offers its customer an unlimited ‘data sandbox’ where they can explore hundreds of different variants, with no limitations. The engine is solving users scale, measurements policies, and time-to-results, and the result is being served in minutes. 

Q: How can Simpool be helpful for Product Developers? 

Guy: Using Simpool, product developers can maximize their work efficiency and innovation aspirations as significant barriers have been removed. Concerns and risks of harming up and running applications with a faulty new variant and bottleneck development can be applied with minimum effort into Simpool artificial ‘sandbox’ and tested. True, results are never 100% match reality, but 80% are good to understand whether a strategy is good or not. 

Q: How does the AI-based sandbox work?

Guy: Simpool’s integration and working process are quite straightforward:

1. With a server-to-server API integration or SDK, Simpool extracts anonymous, user-level historical data.
2. This data is manipulated, validated, and structured in the machine learning process.
3. Every user action has been classified based on dozens of different parameters which describe the user, its state, its engagement habits, and paying habits.
4. The engine uses its statistics model, per game, to evaluate the expected user behavior while changing the app configuration and the expected path.
5. Every path is being analyzed to understand the potential business effect ( most common is predicted Lifetime value). 

Q: What industries can benefit from Simpool? 

1. Gaming – Gaming spaces hold a classic use case for Simpool, as optimizations efforts rely primarily on retention, conversion, and monetization aspects, and also this space is rich with data points. 
2. Playing with game difficulty and its contradiction with monetization strategy leads to dozens( and more) of different options, and it gets even more complicated while considering that other users with varying patterns of engagement require different ‘experiences.’
3. Customer loyalty management – loyalty management clubs rely heavily on exchange rates. Exchanging rates can drive different results, given other user profiles. Having the ability to pre-understand the impact of each configuration is a powerful arm, especially when financial liquidity involves.
4. E-Commerce – Simpool can imitate the e-commerce funnel( CPI, conversion, pricing, and shipping cost to optimize profitability. Also, multi-product shops rely on repetitive purchase patterns, which require both a smooth shopping experience and added value – up the sale, post-sale, and benefits, which can be estimated through simulations.

Q: As we know, you have vast experience in the gaming industry; where do you think mobile gaming is heading? 

Guy: I believe that the industry will turn into both :

1. Metaverse-based content, more real-life user-generated content which will connect real-world actions into a gaming experience. 
2. Cryptocurrency(NFT) based games will become more dominant, which leads to more complex internal economy management, but will also open the door to distributed content between apps.
3. Near-individually tailored content and game experience will be based on a robust segmentation service. One-size-fits-all is out of the loop.

Q: How are sandbox optimization models going to affect traditional A\B testing models? 

Guy: While AB testing is a ‘used’ strategy, I believe that predictive methods and advanced ‘What-if’ engines will be able to provide solid answers. We can see this concept being used in the aviation world while training pilots over edge cases or even in the medical world, where doctors can be trained in simulators before performing complex surgeries. 

Online space will follow this trend, and it will also empower segmentation applications, MLops space with heavy data preparation processes, which are essential to model user behavior.