Multiple factors need to be considered when pricing your products because your customers’ ability and willingness to pay change depending on purchasing power parity, currency exchange rates, seasonalities, economic (market) situations, and even competitor pricing.
By using price experimentation as a means to develop and refine your pricing strategy, you’ll be proactive with your pricing which in turn ensures that you don’t unnecessarily leave money on the table.
Experimenting, especially when done right, allows you to increase your revenue and conversions, and ultimately, your customer’s lifetime value (LTV).
This is where Corrily’s Experimentation Platform can help, offering a robust infrastructure which allows you to scale your experimentation efforts.
What is an Experimentation Platform?
An experimentation platform allows companies to run experiments to test their hypotheses, gather data and make data-driven decisions to propel the the business forward. There exists web experimentation platforms, most of which are suboptimal.
In the context of Corrily, our Experimentation Platform provides a reliable infrastructure for companies to test pricing and packaging hypotheses and launch experiments to have a data-driven approach in learning what works and what doesn’t.
Why is an Experimentation Platform useful?
Test and validate hypotheses
By using a user-friendly and reliable platform to run experiments, companies can easily test their ideas based on their business needs, and quickly validate their hypotheses that help guide their wider strategy.
Improve conversions and revenue
Experimentation platforms allows companies to test different strategies and even personalize product offerings that help them better understand their users based on real user data. This leads to higher customer satisfaction, improved conversion rates, and overall revenue growth.
Experimentation platforms provide in-depth analytics which allow companies to make informed decisions based on data. It also fosters a culture of continuous learning within teams and across the entire company.
Why use Corrily’s Experimentation Platform for your monetization experiments?
Cross-platform and multi-channel approach
Corrily provides an end to end experimentation platform for all things monetization — pricing, packaging, discounts and promotions.
We abstract the complexity involved in running experiments in-house, and provides a single source of truth across all channels (web, mobile, and email).
Integrations to generate revenue analytics and insights
Having detailed analytics are key to evaluate the success of your experiments. An experimentation platform like Corrily helps you track metrics such as conversion, MRR, ARR and LTV across your different markets and segments, and identifies opportunities to improve monetization.
Accurately computing CLTV requires integration with downstream payment gateways such as Stripe, PayPal, Adyen, and subscription management platforms such as Chargebee or Recurly. Without these integrations and corresponding data, it is impossible to model revenue metrics, particularly forward-looking ones like CLTV.
Corrily offers out-of-the-box integrations with majority of these platforms, while custom integrations are also easy to set up.
Monetization lifecycle management
When conducting price testing, there are several additional considerations to take into account, all of which are included in Corrily’s Experimentation Platform. These are:
- Currency: Depending on the target audience and geographies, it may be necessary to test prices in different currencies. This requires taking into account currency conversion rates and fluctuations.
- Price Formatting: The way prices are presented can also influence how customers perceive them. For example, prices presented as rounded numbers may be perceived as more attractive than those with many decimal places.
- Localization: Prices should also be localized to the country or region where the customer is located. This may involve adjusting prices based on local taxes, tariffs, or other regulations.
- Discounts: Along with the price, testing different discount strategies can be important. This could include testing different types of discounts, such as percentage-off discounts or buy-one-get-one-free offers, as well as the timing and duration of the discounts.
Our machine learning models are pre-trained, and continuously improving over time based on your business-specific data and anonymous data across our entire customer base.
This, in combination with our data-efficient sampling algorithms, results in 30-40% improvement in experiment convergence over traditional AB/n experiments.