3 Ultimate Pricing Test Mistakes That Will Wreck Business

Have you seen what happens when companies set their pricing wrong? Customers leave, revenue drops, and what should have been a growth opportunity becomes a disaster. When done right, though, optimal pricing can dramatically increase your revenue, profit, and cash flow.

Effective pricing is one of the most powerful yet overlooked business levers available to companies of all sizes. Even small adjustments can yield significant results when executed properly.

So you’ve decided to run a pricing test with questionnaires. But if you conduct it improperly, you risk losing your biggest clients or setting prices so low your margins practically disappear.

Understanding how much customers are willing to pay is essential, but the methodology behind your pricing research matters too.

Let me walk you through three critical pricing test mistakes that can devastate your business and how to avoid them.

Mistake #1: Throwing All Your Customers Into One Pricing Blender

Suppose you sell to both small businesses and large enterprises. Testing pricing with all customer tiers combined leads to suboptimal results for everyone.

Different customer segments have vastly different budgets, needs, and perceptions of value. Treating them as a homogeneous group ignores these critical differences.

Why it’s a disaster: You’ll end up with a price that doesn’t let you charge the maximum to large customers while simultaneously driving small customers away.

The fix: Test each customer segment separately. What works for one won’t work for another. Start with your most profitable segment, then work outward. For smaller users, you can create a cheaper tier by limiting product/service features. Ask them about their top 3 features in the questionnaire you send to find out which features to provide for each tier.

Mistake #2: Picking Answerers Randomly (Random Sampling Doesn’t Work)

Statisticians say to use random sampling; otherwise, the result is skewed. Pick answerers completely randomly.

For example, you’re researching the physical ability of people. The sample is skewed by picking only big people. The result doesn’t show the population as it is because you only pick extreme cases.

But when testing price, do the opposite. Skew the sample intentionally. Random sampling is disastrous in this case.

Why it’s a disaster: Suppose 20% of your customers generate 80% of your revenue (a common pattern in business). However, you pick the answerers randomly. As a result, you’re sampling based on customer count alone. The test results will be dominated by customers who contribute least to your revenue.

This Pareto distribution appears in many businesses. Making random sampling is particularly risky when you have that distribution.

The fix: Use Revenue-Weighted Stratified Sampling. Sample in proportion to revenue impact, not just headcount.

Using the example before, if 20% of customers generate 80% of revenue, weigh your sampling accordingly. When selecting 5 customers to survey, include 4 (80%) from your high-revenue segment and 1 (20%) from the lower-revenue segment.

This way, you can see the important segment more precisely with more samples.

Now back to pricing. Let’s look at a specific example:

Say you’re selling seats at $100 each and get these 5 survey responses. You ask them “how much is expensive but acceptable?” This shows the maximum acceptable price for each customer:

	Answer 1	Answer 2	Answer 3	Answer 4	Answer 5
Seats	1	16	16	16	16
Current $	$100	$1,600	$1,600	$1,600	$1,600
Max Price	$190	$200	$200	$200	$200

This table shows how different customers respond to the pricing test—their current spending and maximum acceptable price points as a result of the pricing test.

Without proper weighting, $200 looks optimal for total sales. I mean in this case, you’d lose one small customer, but generate $12,800 in total sales (200×16 seats×4), the highest amount. This is higher than $12,350 in total sales (190×(1+16+16+16+16) seats) at the $190 price point. (in actuality, not only revenue but also cost is an important factor—read this article ).

But wait! Your actual customer base has 4 times more small customers than big ones. And your sample has 4 times more big customers than small ones. To weigh this back, you have to multiply small users’ answers by 4 and also divide the big customers’ answers by 4 (or multiply the result of small users’ amount by 16). The properly weighted analysis shows $190 would actually bring the most revenue—that is $15,200 (190×1 seat×16+190×16 seats×4). At $200, you can get only $12,800 (200×16 seats×4), much lower in total revenue.

Important: Maximizing sales isn’t the same as maximizing profit or cash flow. Consider other factors like customer acquisition costs, fixed costs, variable costs, payment timelines, and churn rates when making pricing decisions to optimize your growth.

Mistake #3: Asking Users Instead of Decision Makers

Getting pricing feedback from regular users rather than decision makers can lead to seriously flawed conclusions.

There’s often a difference between those who use your product daily and those who approve the budget for it, especially in larger organizations with formal purchasing processes.

Why it’s a disaster: Users might enthusiastically say they’d pay more for new features, but they aren’t the ones approving the budget. When the actual bill arrives, the financial decision maker might have entirely different viewpoints.

The fix: Ensure your pricing research targets the people who actually make purchasing decisions. Their perspective on value and willingness to pay is what ultimately matters to your business.

Identifying the true decision makers can be challenging, but it’s worth the effort. Look for titles like CFO, VP of Finance, Procurement Manager, or department heads with budget authority, depending on your industry and customer size.

You may give survey respondents a gift, but it should not be something like an Amazon card that anyone can use. Pick items only decision makers would appreciate, like books you’ve written or premium versions of your products.

You must include a checkbox in the questionnaire. They must confirm that a decision maker is answering, and verify their email address and role information.

Well-chosen incentives not only improve response rates but also help ensure you’re reaching the right audience. The verification steps add another layer of confidence to your data quality.

Conclusion

Pricing is perhaps the most powerful profit lever available. Get it wrong, and performance suffers. Get it right, and your business will flourish. By avoiding these three mistakes—mixing customer tiers, sloppy sampling, and targeting the wrong decision-makers—you’ll craft pricing strategies that actually work.

Implementing these recommendations requires discipline and methodical execution, but the potential return on investment is enormous. Even a 1% improvement in pricing can translate to 10-15% additional profit for many businesses.