What's the scientific method to finding out the ideal price point ?

Short answer:

An elasticity test involves varying the price of a product and observing the impact on sales volume. The result is a table of values, which, once presented as a graph, generally gives a bell curves. We visually deduce the best option.

Long answer:

I sell products, I set prices according to my own criteria including my costs and my margin.

1. Why change prices?

Consider these cases:

• I need to increase my volumes in order to obtain a better price from a supplier,
• I need to increase my booking with a supplier to obtain a better price,
• My competitors are much more expensive, do I discount for no reason?
• My competitors are much cheaper, can I match without going bankrupt?

I answer these questions by doing a price elasticity A/B test. If conditions permit, we will test several prices at the same time, otherwise we will do a series of tests.

2. How does it work and look?

I test my current price, called Control, against one or more alternative prices, called Challengers. Each new visitor arriving on my website will be sent to one of the price proposals called Experiences. For each experience, I count the number of visitors, the number of products sold and the booking. I stop the test when I have reached a significant volume of traffic and sales and the confidence index is above 95%.

Note: the confidence index of a test is the probability that the results observed aren't due to chance, but reflect a real impact on user behavior. A confidence rating above 95% is generally considered valid.

The result is a table summarizing the captured values and three calculated values: the Conversion Rate CR% (Units / visitors), the Average Order Value AOV (booking / visitors) and the Revenue Per Visitor RPV (booking / visitors).

..and Voilà !?? Just look for the best values in the CR% and RPV column.

Not convinced yet?

Let's take a simple example to understand the method and how to read the results:

I sell a software teaching foreign languages for € 50,00:

• Control: this is the current price point: € 50,
• Challengers 1 and 2: I test two lower price points, € 40 and € 45, to maximize volume,
• Challengers 3, 4 and 5: I test three higher price points, € 55, € 60 and € 65, to maximize booking.

I let the test running for a couple of weeks, collecting data: actual Traffic, Units sold and Booking. In a table like below, I place the data.

In my results table #1 above, the CR% column gives me the conversion rate and RPV column gives me the revenue per visitor. The highest values give the optimum in terms of volume and booking which are highlighted in grey.

For easier reading, in table #2, I normalize the results by recalculating Units and booking for an equal number of visitors in all experiences. CR%, AOV and RPV values remain unchanged from table #1 to #2.

In table #1, the volume (Units) of Challengers 2 is lower than Control because the traffic volume is not the same. Likewise, the booking of Challenger 4 is higher than Control.

In table #2, normalization allows a direct comparison: we immediately see that Challengers 2 and 3 are the best options.

I can plot the volume and booking curves as a function of price as below.

I obtain visual representations:

• We typically get bell curves because customer behavior is not linear; there is elasticity in the price. In my example, the conversion rate at € 40 is lower than that of € 45.
• If there is no price elasticity, the curve is a straight line.
• We also observe there can be two distinct optimum values and not just one.

3. What next ?

Now, I have a tool to help me take educated decisions regarding price points. The right value to pick depends on my strategy.

In case I run a subscription business, I need to calculate the lifetime value of each price point to pick the best long term option.

Key Takeaways

1. Price elasticity tests reveal optimal price points. It's a scientific method for determining the effect of a price change on volumes sold and booking.
2. A/B testing can debunk pricing myths. I must avoid prejudices: the cheapest price is not necessarily the most popular; the most expensive price is not necessarily the one that brings me the most booking.
3. Investing in an A/B testing tool is a must have to take educated decisions based on data. Depending on my needs, I decide to give priority to volume or booking by choosing the right price point.

#Subscription #RetentionMarketing #PriceTest #Churn #ABTesting