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Price Optimization Testing - How Do I Do It?

Posted by Drew Chambers on Apr 2, 2019 8:22:58 AM

What can I charge? What should I charge? What will happen if I make a price change?

Every business looks at their competition to get a sense of what the ‘right’ price is for their product. But you aren’t your competition and that approach may miss important context from customers that goes deeper in their ability and willingness to pay for what makes your product different from your competition.

Corus worked with a computer accessory company that was facing a challenge in determining the right price for their customer niche. With a unique product, they were looking at competitors producing products that cost $10, $25, and sometimes $50. But their product is special, and they thought that they might be able to charge $250 per unit.

We were able to find an audience of their target customer segment and our research found almost no upper limit to the price that their customer would pay for the special and unique features that our client offered. We also found that discounting at volume was a real driver for a primary enterprise buyer, and so list price of $1,000 was not only possible, but revenue maximizing across channels. The extra $750 is pure margin per unit and has been instrumental in the revenue growth of our client.

Corus Price Optimization Testing Analysis

We can’t promise that you can raise prices four-fold, but we can help you to find the right price to maximize profits. Curious how we do it? Reach out to learn more!

How to optimize price based on customer input

Oh, you’re still reading? I guess you like the idea of making more money just like we do. We’ll share a little bit more about the few key methods to use to test price. Speaking of price, we collect the data we need for each effort, starting at just $0.15 per question answered, and our software is always free. The price for our guidance of each type of effort is listed below.

Gabor-Granger:

Named for Nobel Laureate Clive Granger and Andre Gabor, the intent behind this method is to determine the highest price a respondent is willing to pay for a particular item. This is usually done by randomizing price within a range and then asking the respondent whether they intend to purchase the product. Because each respondent has an associated highest price, a demand curve follows by plotting each tested price against the percent of total respondents willing to buy at that price. Multiplying the two then calculates the expected revenue per respondent, which then allows for a maximum to be found.

Van Westendorp:

First introduced in the 70s by Peter Van Westendorp, this type of pricing approach consists of asking each respondent four questions:

  1. At what price would this product be too expensive for you to buy?
  2. At what price would this product be too cheap as to question its quality?
  3. At what price would this product be priced a bit on the high side, such that you’d have to think it over before buying?
  4. At what price would you consider this product to be a bargain?

These answers are then plotted as cumulative frequencies (with #2 and #4 above inverted), which leads to intersections. There are a number of ways to interpret these intersections, which you can discuss with your analyst. That said, a typical “acceptable price range” tends to fall between the intersection of “what would be too cheap”/“a bit too high” on the low end and the “too expensive”/“Bargain” intersection on the high end. The intersection of two extremes (“too expensive” and “bargain”) represent a balance and therefore can represent “optimal pricing point”.

Monodic Split Cell: Sometimes bundling and framing of products needs to be tested to assess the impact of the full range of offers on revenue per customer. In this case, we present a range of options to customers within defined bounds and determine which path a customer chooses to determine the revenue maximizing product range and bundles to be offered.

Conjoint Analysis: We dive deeper into conjoint analysis in another post (here). If you’re familiar with conjoint, then its application to price testing is obvious. Asking respondents to choose between multiple options allows us to assess the relative importance of each feature… one of which is likely to be price. If we are presenting cable service subscriptions with variable price, channel mix, etc. we not only get the relative importance of the factors, but we can also 'monetize' each feature. This analysis allows us to provide guidance on the optimal price given a range of product constructs, even if they were not presented to customers. (If you’re capable of the type of regression that generates these insights, you can now use the Corus platform to design these studies for FREE.)

Demand Modeling: If your product is in-market, you have good customer demographic data, and you’ve varied price, we can model price sensitivity based on transactions to understand the impact of future price changes. This analysis is complex and requires clean data and careful considerations of operational complexities. But if it is done right, it can also enable regional or segmentated price discrimination strategies based on acceptable variables (i.e. not protected classes).

Bringing it in for a landing

If you're looking for immediate bottom-line impact, there are few better places to start than optimizing its price. It’s that simple.

Read more about other capabilities here: Data Analysis 

Topics: survey, dataresearch, price testing, price optimization testing

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