Today, with economic factors manipulating consumer behavior, finding the sweet spot for pricing that tackles price challenges holds immense significance.
On one hand, you want to maximize revenue by broadening your profit margin; on the other, you want your prices to resonate with your audience. Adopting an experimental price testing approach that strikes two birds with one stone is key when faced with such a deadlock.
How? A/B testing for pricing facilitates you in experimenting with two different price tags to observe which drives more revenue. According to 99Firms, 75% of the top retailers make use of A/B testing to identify various pain points that ultimately help them generate a higher ROI.
That said, it’s highly recommended to develop a concrete pricing strategy prior to A/B testing, as it streamlines the process of choosing two realistic price points to experiment with.
By the time you finish this, you’ll have complete knowledge of how to create a pricing strategy, understand A/B testing, and learn from the common mistakes to implement “ethical” A/B testing for pricing.
Finding the ideal pricing point to drive more revenue has never been this easy.
Planning Ahead: Pricing Strategy
To ensure successful A/B testing for pricing, you must decide on what pricing strategy you want to carry forward. Throwing random numbers on the table or floating on assumptions would only take you so far; it hinders revenue growth.
However, before we dive into discussing high-converting pricing strategies, what exactly is a pricing strategy, and how does it contribute to helping your A/B test drive revenue?
Understanding Pricing Strategy
Price strategy refers to mapping out a long-term method that would allow you to determine the most suitable selling price for your product or service, whereas price testing is a data-driven procedure that finalizes a pricing point in the pricing strategy that serves the interests of both the business and the customer.
To put it simply, the credibility of your price points is directly proportional to the effectiveness of the results fetched by your price testing.
So, what are some of the renowned psychological pricing strategies that you can integrate into your A/B testing for pricing?
Psychological Pricing Strategies
Psychological pricing strategies are ethical pricing methods that psychologically influence customers’ perceptions concerning the value of your product or service.
Inevitably, pricing strategies based on psychological factors can help you to:
- Simplify the decision-making conundrum for customers
- Increase sales and average order value (AOC)
- Make a product or service more desirable
- Enhance the buyer's shopping experience.
Here are some of the best psychological pricing strategies to employ before carrying out your A/B testing.
Charm Pricing
The purpose of charm pricing is to pitch the idea of a fruitful deal in the mind of the customer all while making a minimal change in original pricing. It uses odd numbering to tap into people's emotional reactions.
The most common example involves the addition of .99 in pricing. Why? A Marketing Bulletin study proves customers perceive odd numbering as cheaper. There’s also the memory factor. If a service or product costs $9.99, customers tend to recall only the first digit.
Despite the price being minutely less than $10, valuing it at $9.99 tricks the mind into seeing it as $9 only. For these reasons, retailers strongly support this pricing strategy.
Odd/Even Pricing
Following the same logic as charm pricing, odd/even pricing reserves the odd numbers for subsidized items, such as $5.99 or $13.99. Customers tend to associate even pricing with high-value items, particularly for expensive and luxury items. Price tags like $80 or $250 are examples of even pricing.
Odd/even pricing is used to create an image of a product's value in the mind of a customer. By utilizing it, businesses can easily segment the purchasing power of buyers.
Decoy Pricing
Here, "decoy" refers to making an expensive alternative more attractive by including a second, less desirable option. Customers are influenced to choose the expensive option because the decoy alternative is priced similarly but doesn't provide as much value.
This pricing strategy is ideal for companies that want their target audience to opt for the intended package. Let's consider Starbucks as an example. Their sizes come in Tall (small) for $3, Venti (medium) for $4.50, and Grande (large) for $5.
Tall is the budget-friendly option, whereas between the other two options, Venti acts as the decoy. Given that the Grande is larger than the Venti, and the two sizes have a price difference of only 50 cents, people are motivated to choose a Grande more frequently.
Flat-Rate Bias Pricing
Flat rate bias refers to customers' preference for paying a fixed price versus pay-per-visit costs, even if the flat rate is higher. Customers who value dependability and simplicity in their payments view varying rates as a hassle.
This pricing strategy is frequently used in subscription-based services.
Finalizing a pricing strategy allows you to enter the experimentation phase better informed about your target market and what pricing direction to pursue.
A/B Testing: A Brief Walkthrough
A/B testing is a powerful method that compares the performance levels of two versions (A and B) so that the best option can be chosen based on its effect on the conversion rate.
Companies utilize A/B testing to personalize their websites, monitor landing page performance, and test out different price points to discover the “sweet spot.” A/B testing assists in bridging the gap between their revenue and user-focused objectives.
There’s an ongoing online deliberation on whether or not A/B testing for pricing is an ethical approach. To put it simply, the answer is both “yes and no.” If A/B testing for pricing is planted tastefully, it’ll sow the desired results for pricing.
Similarly, if it lacks precise execution, it can damage a brand’s image as well as diminish customer loyalty.
Mistakes to Avoid: Ethical A/B Testing for Pricing
If you are new to A/B testing for pricing, there’s a great risk involved in becoming prey to rookie mistakes that businesses make when conducting this experiment.
Do not worry. We have listed them all down so that your price-testing experiment reaches its pinnacle!
Conducting A/B Testing on Multiple Products
Testing pricing on multiple products at once may compromise your results' accuracy. It is challenging to identify what works because different customer segments with differing price sensitivities may be drawn to different products.
For better insights, concentrate on testing prices for a single product at a time. Factors such as product category, perceived value, and demand can skew results when testing multiple products.
For example, testing a $35 and a $250 sneaker will give you complex results as customers respond differently to cheap and expensive products.
Neglecting Target Audience Segmentation
Each type of customer segment reacts differently to price sensitivity. Ignoring these differences will bring about a price point that resonates with one group but isolates the other.
During A/B pricing testing, it's critical to consider target audience segmentation to obtain accurate insights and make optimal pricing decisions. Each customer segment exhibits distinct behaviors and price sensitivities.
Consider the distinction between new and existing users, or between small and enterprise clients. The process of audience segmentation guarantees the accurate representation of each group's pricing preferences.
This leads to more focused, ethical, and efficient pricing strategies that maximize revenue from different customer types.
Overextending the Price Testing Period
Running A/B tests for too long can expose your pricing experiments to changes in market conditions, competitor activity, or shifts in customer behavior, leading to potentially inaccurate conclusions.
To ensure that irrelevant data doesn't impact your decision-making, stick to a clearly defined testing timeframe. Let’s consider the Christmas season example.
If you extend the test beyond the holiday period, it could result in inaccurate final data, either due to market changes or competitor offerings.
Conducting A/B testing on a Small Sample
The sample size has a significant impact on A/B testing for pricing. Think about it. A larger sample size would allow you to collect more data; thereby, enabling you to critically analyze it and add statistical significance to customer behavior representation.
For example, testing a $14.99 item on a sample of only 40 consumers would result in unreliable data, in contrast to testing the same price point on a sample of 240 customers.
Unrealistic Pricing Approach
As a seller, you are aware of your product’s value. Running A/B testing for pricing by setting the price either too high or too low would not only distort data but also alienate your customers.
For instance, if you are planning on increasing the subscription fee from $30 to $85, such an unrealistic leap from one price point to another will negatively impact the customer experience.
Prioritizing Conversions Over Revenue
In A/B testing for pricing, the focus should always be on the revenue, not conversion. The sole reason is that changing a price point that highly resonates with your target audience might lead to more conversions, but that doesn’t necessarily mean it’ll grow your revenue.
For example, if a SaaS company drops its subscription fee from $35 to $15, the high discount factor might increase conversions, but it won’t fulfil the company’s revenue goal.
If you reverse engineer all these mistakes, you can conduct A/B testing ethically and close amazing sales.
A/B Testing for Pricing: E-commerce
In the e-commerce world, businesses prefer to incorporate penetration pricing (charging a lower price) because it attracts new customers. The go-to pricing strategy usually revolves around MSRP (manufacturer’s suggested retail price), dynamic pricing, and cost-plus pricing.
MSRP is the price that a manufacturer recommends to retailers. It is used in industries such as electronics, automotive, and appliances. MSRP helps keep prices consistent across all retailers, so consumers see the same price no matter where they shop.
Dynamic pricing varies in accordance with fluctuating market demand. Such a pricing technique enables e-commerce businesses to strategically regulate their prices; it helps them optimize profits and grant customers striking deals.
Cost plus pricing calculates a product's price by adding a margin to the item's cost. The margin is the desired profit. It is used by companies with stable costs and a desire to turn a profit.
After finalizing their pricing strategy, e-commerce businesses can proceed with A/B testing for pricing to determine the best possible price points that resonate and drive revenue.
A/B Testing for Pricing: SaaS
SaaS companies lack a benchmark to determine the value of their software service, unlike other business models where a product's initial manufacturing determines a suitable price. It is completely cloud-based and intangible.
This makes the pricing process a lot more complex. SaaS companies usually opt for competitive, penetration, or value-based pricing methods. Nevertheless, an optimized A/B testing tool acts as a saving grace here.
Relevic's "no-code" A/B testing tool simplifies the entire price testing process through effective segmentation and provides all the necessary statistical findings to identify the optimal pricing.
So, whether your business model is subscription-based or e-commerce, Relevic promises to boost your revenue growth at an impressive rate.
Closing the Deal
A/B testing for pricing is an experiment set to target revenue optimization that allows firms to experiment with various price points and determine the most effective plan.
Studying and applying psychological pricing methods like charm pricing and decoy pricing can help companies effectively engage their target audience. Avoid common pitfalls, such as setting unrealistic pricing and overextending test durations too much.
Innovative tools such as Relevic can help SaaS and e-commerce businesses simplify this process and achieve favorable results. It goes to say without hesitation that A/B testing for pricing holds great promise in enhancing revenue growth and improving customer satisfaction.
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