Pricing a B2B software product
Pricing a new products is one of the most difficult things a start-up can do. We set out to find the answer to “how much should this sell for?”.
Business tools, software especially, get put through intensive scrutiny before the company’s decision maker sanctions a purchase. These tools are rarely bought on impulse, which means that the price point at which they’re sold is an important feature. It can make or break any new product.
The market for b2b Software As A Service (SaaS) products is highly competitive, meaning entering the market at the optimum price point for your target customers will ensure its steady growth and eventual success.
We set out to answer the question of “how much should this sell for?” while working on the release of a cloud based point-of-sale software, into the small business market of South Africa.
Choosing a method
There are currently two main schools of thought when it comes to pricing research: Conjoint Analysis and the Pricing Sensitivity Meter.
Conjoint analysis is the most accurate, but it requires users to have an inherent knowledge of the product before pricing it. It’s used in the pricing of most consumer goods (soft drinks, cereal brands etc). However, will not work when the product is complex and needs to be explained beforehand.
This is why the Price Sensitivity Meter (PSM), created by Dutch economist Peter Van Westendorp, is the best to use when pricing business tools.
It captures the value potential that users attach to a product by giving them a walk-through followed by a series of 4 pricing questions:
1. At what price would it be a bargain?
2. At what price would it be so expensive that you wouldn’t consider using it?
3. At what price would it be expensive, but you’d still use it?
4. At what price would it be so cheap you’d think it’s poor quality?
Cognitive biases to be aware of
Anchoring:
Mentioning anything price related before the survey will cause the Anchoring effect. Your demo must have little to no mention of **any** numbers, especially those about pricing.
Lowballing:
Direct questioning leaves the possibility of switching users into negotiation mode. This will lead them to ‘lowball’ their answers thinking they might benefit from in the long run. Tell users they can get free early access to the product for taking part in the survey to get around this.
Respondent Awkwardness:
Asking financial questions can be awkward. Users might seek to impress you or even refuse to answer. Tell them their answers are anonymous to prevent this from happening.
Planning the approach
Organising Interviews:
Speaking to the ‘decision maker’ of the business is essential. At the end of the day these will be the people saying yes or no to your product when it goes to market. Email beforehand and organise a time and place to conduct the interview with the owner.
Collecting data:
For a truthful valuation the user must have a clear understanding of the product they are about to price. This means a full software feature run down. They must then gauge the product’s value within and give an answer to each of the 4 PSM questions.
People tend to dodge pricing questions which makes verbal questioning tough. Hand the person a google form on an iPad and explain that the answers will be anonymous. This will ensure they give you an unbiased set of answers.
How many people you should speak to
To get a statistically significant answer you must first estimate your potential users.
This was our calculation:
Total businesses: ± 600 000
Percentage that are eligible : 42%
Potential user base: ± 232 000
This meant our study needed 96 responses
Use this sample size calculator to find out how many you need
Analysing the data
Plot your data as a Cumulative Frequency Graph. The intersecting points will then reveal the following key metrics:
Pricing Range: Showing the range of acceptable prices
Optimum Price Point: A price which would ensure the fastest growth
Indifference Price: A price below which customers are indifferent to pricing changes
Tracking user responses
All form entries filter to google sheets. Set up your sheet to organise the data for creating a graph at a later stage.
Checking the answers
Answers should follow a logical sequence i.e. the lowest acceptable price cannot be more than the highest acceptable price.
Answer 2 is invalid. The user did not understand the survey and their answers cannot be used.
Monitoring distribution
Flag and remove any answers that are greater than 1 standard deviation away from the mean. This will ensure the data is not swayed by outliers.
Interpreting the results
After plotting results on a cumulative frequency graph, they should look like this:
Our study found that the optimum price point for the software was $18/pm.
Anything below $14/pm would be too cheap and anything above $28/pm will be too expensive.
Profit can be maximised by increasing the price towards $21/pm. Although, anything above this will slow growth as it will be seen as premium.
