Guides and tips for price optimization

Finding the most profitable price for a product

In this tutorial, we will analyze historical profit conversions and learn how to modify prices to achieve greater profit.


The approach takes several steps and the process will cover a several week long pricing test.

  • Select product
  • Launch pricing test
  • Evaluate results

1. Select product

Select a product with enough historical data. This means that the product should be a popular item which receives at least a couple of orders per day. For this test, we assume our product is a special branded camp t-shirt.

Our purchase price (cost of goods) for this t-shirt is $12. We would normally sell this t-shirt for $20, but we don't know if this is an optimal price for maximizing profit. Thus we will test 2 price options: $20 and $25.

2. Deploy pricing test

Our test will consist of two pricing periods. We will set the t-shirt price to $20 for two weeks, and we will set the price to $25 for another two weeks.

Keep in mind, that the aim of price testing period is to collect enough data - sales and visits to be able to meaningfully compare the conversions of different prices. If enough data is collected, we can use a statistical test to compare the results.

Here are some tips for optimal price testing:

  • Timing - Pick two time intervals, where you expect similar amount of traffic and similar conversions
  • Isolation - Don't AB-test other product parameters during the pricing test
  • Duration - Generally try to test each individual price point for at least two weeks

3. Evaluate results

After testing of all desired price points is complete, we can find out which price is more profitable. Go to Profit Analytics page in FutureMargin dashboard and select the correct time range. In our case, the time range spans 28-days.

To meaningfully compare the results, it is important to normalize total profit with number of visits for each given period. FutureMargin automatically does this for all key metrics. Finally, we compare profit per visit for the two tested prices and we see that profit per visit is greater for the price point of $25.

We can now also observe actual visit counts and various conversion metrics and graphs for the two periods.

We also check the value of statistical confidence. In this case, the value is high enough. If it is not, we should increase the duration of each testing period, to collect more data for evaluation.

Setting margin strategy for a collection

In this tutorial, we will analyze conversions and learn to choose a margin strategy for category of products. Margin is simply a difference between selling price and cost of goods.

The category we are optimizing in this tutorial is: Women sunglasses with a shopping price range between $50 and $100. We want to decide, if it is more profitable to set a 30% or a 50% margin.


The approach takes several steps and the process will cover a several week long pricing test.

  • Create a collection
  • Launch pricing test
  • Evaluate results

1. Create a collection

Firstly, we will create a collection of products called Mid range women sunglasses in FutureMargin dashboard. This collection includes ID's for all women sunglasses products with price range approximately within $50-$100 we have on sale in our e-shop.

To create this collection, go to Edit collections in FutureMargin dashboard and upload a file with ID's of relevant sunglasses products in a text file. Give this collection an easily identifiable name. For more details on editing collections in FutureMargin, you can follow documentation.

2. Launch pricing test

We will test two different pricing strategies. The testing will cover two time intervals, each taking 14 days.

For each test, set the pricing for the Mid range women sunglasses products to achieve the corresponding margin price (30% or 50%). Set the prices from the midnight of the first day to the midnight of the last day of the tested two week period, so that the test covers exactly a two week period.

We recommend two weeks as the minimum time for testing one price strategy - to collect enough data. You can run the pricing variation for a longer period as well. Try to however select an overall time period where both price strategy variations are unaffected by external factors - e.g. Black Friday sales, differing customer acquisition strategies, short term seasonality etc.

3. Evaluate results

After both pricing tests have finished, go to Time Analytics page in FutureMargin dashboard. Select our Mid range women sunglasses collection and enter the two 14-day date ranges corresponding to the two margin strategy tests.

FutureMargin automatically normalizes the cumulative statistics with total product visits for each of the two time ranges. Using these conversion values we can compare results of the two metrics. Comparing just the raw profits or revenues would not be as meaningful, since both time ranges naturally received a different number of visitors.

We can now observe different conversion metrics corresponding to the two margin pricing strategies. For maximizing profit, the 50% margin (Date range 2) has resulted in better performance. For maximizing revenue, however, as lower price converts more overall sales, the 30% margin (Date range 1) provides better results.