Flat rates sell better than pay-per-use
Flat rates sell better than pay-per-use
People prefer flat-rate plans over pay-per-use plans, even if that means they overpay. Both B2C and B2B customers have this bias.
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📝 Intro
We’ve known for a couple of decades that people prefer flat-rate plans (e.g. $50/month for unlimited mobile data), compared to pay-per-use (e.g. $5 per GB), even when they’re more expensive.
Today’s study wanted to understand if B2B customers act the same way.
As for most other biases, they do. After all, we’re still people, even when we’re representing a company.
Let’s dig in, and see what drives this - for both B2C and B2B customers.
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Previous tip: When to use video to promote your product (All tips here)
Offer higher priced flat-rate options to earn higher margins
Impacted metrics: Customer acquisition | Customer spending
Channels: Pricing
For: Both B2C and B2B
Tip type: New research (April 2021)
📈 Recommendation
When possible, offer both flat-rate and pay-per-use plans (e.g. choose between $5,000 for the entire project or $50 per hour).
Price your flat-rate plans higher than pay-per-use plans (relative to general and expected use). Many customers will still choose them, and you will have higher margins.
Some customers may start with pay-per-use plans, but you can upsell them flat-rate plans later on.
🎓 Effects
People are more likely to choose flat-rate plans over pay-per-use plans, even when they are more expensive. Business buyers (B2B) have the same bias.
For example, when experienced purchasing managers were choosing a plan for a new customer analytics SaaS:
68% chose the flat-rate plan when the pay-per-use plan would have cost the same, based on their average use
23% chose the flat-rate plan even when it was 20% more expensive than pay-per-use, based on their average use
15% still chose it when it was 50% pricier
The effect is stronger:
When past usage has high peaks, even though the average is the same (e.g. 500 hours/month average, with the busiest month being 900 hours/month vs 600 hours/month).
When the buyer has a strong relationship with the seller (e.g. a design agency they’ve worked with closely for 5 years).
The preference for flat rates seems weaker when services are highly interactive (e.g. design agencies, consulting) rather than hands-off (e.g. analytics dashboards, telecom plans).
People are biased towards flat-rate plans, and often choose them even when they would be significantly more expensive (Click to zoom in)
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🧠 Why it works
Several effects bias us towards choosing flat-rate plans, rather than pay-per-use.
For both personal (B2C) and business (B2B) buyers:
Insurance effect. We want to avoid the risk of having to pay more than we have planned for
Convenience effect. When we’re lazy and don’t feel like comparing plans, we go for the flat-rate one because it’s easier
Taximeter effect. We simply don’t like it when our cost increases the more we use something (i.e. that feeling of dread when you’re watching the taxi meter ticking up - one of several reasons why people prefer Uber)
Overestimation. We tend to expect that we will use something more than what we actually do
For B2B buyers:
Distrust effect. We sometimes worry the provider could overcharge us if we pay-per-use (e.g. if consultants are charging us by the hour how do we know they aren’t just taking it slow?)
Administration effect. We dislike the admin hassle that comes with different pay-per-use costs each month (or period of billing)
Remember, even in B2B you are selling to other people, so most consumer biases remain valid, to a certain extent.
✋ Limitations
Several studies have found this bias, so this effect is strong and applies to most situations, in both B2C and B2B.
This research tested hypothetical buying situations, rather than actual purchases. This makes the results less reliable.
The study focused on repurchase situations. People might be more likely to start with a pay-per-use plan and then switch to a flat-rate plan.
We don’t know whether a brand’s strength and familiarity influence whether people are more or less comfortable in starting off with a flat-rate plan.
🏢 Companies using this
Flat rates are common in many industries (e.g. gyms, mobile plans).
They also occasionally appear in industries where it’s less common, for example:
Consultants who charge a retainer are effectively charging a flat rate.
Beach-goers in Italy often pay to rent beach beds for an entire summer season (e.g. €2,000) rather than pay per-per-use (e.g. €50 per day)
Companies who are experienced in charging flat rates (e.g. telecom companies) already take advantage of this effect to earn higher margins.
However, many companies that could offer flat-rate pricing (e.g. SaaS startups) neglect to do so and are missing out on better profits.
⚡ Steps to implement
If you’re currently offering a flat-rate plan, you may be able to charge higher prices than you think, and many customers will still choose it
If you don’t currently offer a flat-rate payment option, think about whether your product or service can be sold on a flat-rate basis as well.
Think outside the box and test new ideas. You may be able to charge flat rates even for products that traditionally don’t. For example, a coffee supplier could charge a fixed amount per month, and ship different amounts of coffee to cafes based on varying demand (up to a limit).
Research and test different pricing to find the optimal balance of flat-rate and pay-per-use prices to maximize profits.
Be careful to still keep your pricing simple and easily understandable, to avoid overwhelming potential customers.
🔍 Study type
Online experiments (with 482 experienced B2B purchasing managers). Sweden and Germany
📖 Research
Kienzler, M., Kowalkowski, C., & Kindström, D. (April 2021). Purchasing professionals and the flat-rate bias: Effects of price premiums, past usage, and relational ties on price plan choice. Journal of Business Research.
🏫 Affiliations
Linköping University and Hanken School of Economics. Sweden and Finland
Remember: Because of the groundbreaking nature of this paper, it could be disproven in the future (although this is rare). It also may not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.
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