Getting pricing and packaging right is crucial to unlocking a product's full potential, but many businesses shy away from changing their pricing strategy, resulting in lost revenue — which could be potentially up to 50% of their annual revenue.
Businesses that understand the value of adjusting and experimenting with their prices are in the minority, but they often try to create a platform for price experimentation themselves or use popular web experimentation platforms such as Optimizely or Google Optimize (soon to be sunset).
Establishing a reliable infrastructure for pricing optimization is not only expensive but also takes focus away from improving the products they offer. While web experimentation platforms can assist with simplistic, one-time tests, they are not designed to meet the evolving pricing needs of a business.
Let's delve deeper into why using web experimentation platforms for price testing is suboptimal.
Price testing is more complex than testing web variants
Platforms that allow users to test different variations of their website or app are useful for gauging performance, but they don't provide enough when it comes to testing prices.
There are many more factors to consider than just the visual elements of a web page. For instance, you may want to test different prices for different customer segments or examine the impact of discounts on conversion rates in a particular country.
Additionally, it is much more important to maintain a consistent user experience when testing prices than, say, a UX test on your website.
Measuring the results of price tests
Another key limitation of using these platforms for price testing is that it can be difficult to accurately measure the results. It's important to know how many users saw and clicked on a particular variation and how many of those people made a purchase and stayed on your platform.
Web experimentation platforms' A/B testing approach tracks clicks but lacks the detailed analytics needed to evaluate the success of price tests (conversion, ARPU, MRR, etc.). Additionally, some key KPIs such as Customer's Lifetime Value (CLTV) is not immediately discernible and require complex modeling of revenue events and product usage.
Modeling Customer’s Lifetime Value (CLTV)
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.
Cross-platform approach for pricing
These web-based platforms are not suitable for testing prices on mobile apps or other non-web environments. This incompatibility with different platforms can restrict the scope of your testing and make it harder to gain an accurate understanding of how users interact with prices.
There are various scenarios where you would want to go beyond an A/B/n split and serve specific prices and discounts based on various factors such as the kind of user interacting with the prices, the location of the users, the engagement on your product, etc.
This dynamic nature of pricing makes it very difficult to run global price tests using A/B testing platforms.
Pricing lifecycle management
When conducting price testing, there are several additional considerations to take into account, beyond the limitations of the platform itself. These include:
- 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.
- Psychological Factors: Additionally, testing prices should consider the psychological factors that come into play when customers make purchasing decisions. Prices that are perceived as fair or a good value can be more effective than prices that are too high or too low.
Customers often invest a lot of time and effort to make web experimentation platforms like Optimizely and Google Optimize work for their price testing needs, only to find that they can't make any decisions. A multitude of downstream KPIs must be taken into account for successful price testing.
Price testing is complex and requires a platform designed specifically for this purpose, such as Corrily.
If your 2023 plans include taking a strategic look at pricing, you can book a free pricing workshop with us. We would love to help!