Pricing is one of the toughest decisions for many startup founders. Often it feels like complete guesswork but it doesn’t have to be this way. There is a systematic way to choose a price and in this post I’m going to show you how to choose the right price to maximise profits for your startup.
First of all let’s look at why pricing is so important. Your price, and revenue model, are an essential part of your business and have a huge impact on how only your profitability and funding requirements, but also on your marketing decisions and ability to acquire customers cost effectively, or at all. Price can also serve as an obstacle to buy and plan a major emotional, as well as rational, role in buying decisions. For example if pricing is too complex it can be barrier and if a product or service is free people can wonder what’s the catch (to this day I’m still asked how Skype makes money!), and for many large organisations a paid service can be easier to get signed off and feel confident in using rather than a free offering.
There are also numerous considerations to balance when choosing a price that will work for you start-up. These include:
- Competitor’s pricing
- Customer’s willing to pay
- Conversion, and revenue projection, at each price
So assuming that you’ve thought about all of the above here’s a pricing survey that will help you to understand what your users are willing to pay and what price will yield the greatest revenues for your start-up.
Obviously you don’t just want to run a survey asking users to tell you what they’re willing to pay, as there’s a huge amount of bias to this and you’re likely to get suggestions of very low prices and be unable to weight these prices based on how likely these users are to become paying customers. The survey I outline below is something I have used successfully with numerous startups to understand what pricing will yield the maximum revenue. It’s also the mechanic which Sean Ellis used when making pricing decisions in Dropbox and LogMeIn (Thanks to Sean for sharing this framework with me!).
Reaching a Price
Start with some conversational research such as user interviews to help you make an educated guess at a price that users might be willing to pay.
Now that you’ve found a price point, let’s call this the centre point price, its time to move on to your survey. The users you want to include in your research are the users of your product or service who have experienced that actual value of your product, so for example with Skype this would have been users who had made a minimum number of calls using Skype, with Soluto it was users who were already supporting at least one other person’s PC on Soluto.
Create a survey using your centre point price and two additional points, one 50% higher and one 50% lower e.g. $5, $7.50 and £2.50. Then run a survey to three different groups. The three surveys will ask the question below and will identical apart from price.
If [COMPANY NAME] did [Enter value proposition] and cost [show one price point] what is the likelihood that you’d buy?
- Probably not
- Possibly not
Once you get the results back you need to apply a virtual pinch of salt to understand how likely the users are to actually purchase given their responses. To do this apply the following formula to the results to get a demand curve at each price point surveyed:
- Of those who answered ‘Definitely’ assume 50% will buy
- Of those who answered ‘Probably’ assume 20% will buy
- Of those who answered ‘Possibly’ assume 2% will buy
Then model each price point to determine how much revenue you’d earn, and to find the max yield price for your startup.
If the results come out skewed at either end then re-run the survey with that as the centre point. So for example if you’ve surveyed $5, $2.50 and $7.50 and $2.50 comes out as the winner then re-run survey with $2.50 and also $3.75 and $1.25 to a new group of respondents.
The downside of this approach is that you will need a substantial group of users who are already using your product. In order to feel confident in the results you’ll want an absolute minimum of 50-100 responses at each price point.
Of course it’s also possible to AB test pricing and understand what will deliver the best yield from this. To AB test different prices, create multiple tests shown to different users (make sure the same user doesn’t see different prices every time they come back or refresh the page!) and track clicks, purchases and revenue at each price point to find the best price. If you choose to take this approach ensure that you hold everything else steady and don’t make multiple changes, or you won’t be able to tell whether the big red button or the lower price, were the driving force for the improved conversion rate.