“How will my customers react if I change the price of my product or service?”
Sounds familiar? It’s a fundamental question every business owner asks (or should ask) themselves.
As a core principle, businesses can expect demand for their products and services to decrease when they raise their prices. But how much a price increase will impact on demand depends on a wide range of other factors, including the type of product being sold, customer attitudes, current market conditions, levels of competition and so on.
Getting a clear sight of these ‘unknowns’ is crucial to determining the impact a price change will have on your demand. In this article, we will explain three practical methods for accurately assessing the effect of increasing or decreasing price:
1. By asking market experts,
2. By surveying your customers,
3. By carrying out price experiments.
Before we dive in, first have a quick recap of the theory of how and why price affects demand.
the price-demand curve shows the relationship between price and demand
In two previous blogs, my colleague Fabian gave a brief overview of the law of supply and demand. We then went on to explain the price-demand curve and the role it plays in making pricing decisions. In brief, the law of supply and demand tells us that, as the price of a product increases, demand for it will fall.
The higher the price – the fewer people buy.
If you plot price against actual or forecast sales quantities, you will usually see a curved line as in Fig. 1. Let’s take the example of our previous blog posts of milk sales to illustrate the price-demand curve.
What’s clear from this diagram is that the higher the price for a litre of milk, the lower the quantity sold. This means higher prices drive down demand.
But what’s useful about our milk graph is the fact that it allows you to calculate a revenue figure for every price, simply by multiplying by the quantity sold.
So on the milk curve above, if you started at €1 per litre, you can see you would sell 100 million litres, so your revenue would be €100m. At €2 per litre, demand falls and you only sell 60 million litres, but 60m x €2 gives you revenue of €120m.
Using that price-demand curve, you can see that a price increase to €2 would harm demand, but still a positive impact on your overall earnings. Thus, the price demand curve is super helpful in determining the revenue optimal price. However, the problem is that most businesses do not know their price demand curve.
That’s where our three practical methods come in. We also let you know which one to use based on your needs and resources at hand.
1. Ask the experts in your market
This is what we call the ‘quick fix’ – as it is arguably the most straightforward option. If you want to predict how a price change will affect demand and ultimately your profits, asking internal or external experts in your market for their views is likely to give you some workable figures faster than any of the other methods.
In this approach you ask internal or external experts to give subjective estimates what impact they feel an increase or decrease of 5%, 10%, 15%, and so on would have on demand.
This method is best applied, when asking your customers is too expensive or would take way too long. This is often the case for launching new products and services and you don’t have any existing sales data to use. Asking expert opinions is also useful to use alongside other methods to help verify your findings.
Ideally, you would survey 5 to 10 different experts, ideally from people with different roles and positions in the industry, and take an average of all responses to get as rounded and reliable a picture as possible.
As well as being quick and relatively inexpensive, surveying experts is especially helpful if you need some informed opinion on your market.
If you have surveyed experts, you may find it difficult to plot precise quantity values for different price points, especially if you have asked several different questions. It’s sometimes easier to collate data to show how percentage price changes – plus or minus 5% or 10%, say – would lead to percentage changes in demand. This can then be plotted onto a different type of price-demand curve, as shown in Fig. 2.
From this graph, you would conclude that price changes around +/-5% only result in minor changes in demand, which is unlikely to have a significant impact on your revenues or margins. Price changes within this range would, therefore, be acceptable. But as you can see, price increases above 5% result in a sharper drop in expected demand, and probably wouldn’t be wise for your business outcomes. A conclusion from a graph likes this would be to slightly increase prices.
2. Survey your existing customers
The question of how price changes impact on demand ultimately falls under the realm of marketing, and like all marketing questions, the most obvious route to an answer is to ask your customers. Surveying customers about how price changes would affect their likelihood to buy a product might be seen as the ‘standard’ approach and is viewed as providing robust and trustworthy answers because the data is ‘straight from the horse’s mouth’ – the people who buy your products.
In this approach, your customers are asked what impact price changes will have on the demand for your product.
Examples of variations on questions might be as follows:
● Would you be more/less likely to buy product A if its price was increased/decreased by 5%? What about 10%?
● How much would you be willing to pay for a product that can do x? What about if it could do y and/or z?
● What is the absolute maximum you would be willing to pay for product A? What about for features x, y and z?
● What do you think is a fair price for product A?
Quizzing customers about the impact of price changes is a fairly easy and standard way to estimate price demand curves. However, the downside is that the price becomes the focal point of the survey and your customers aren’t providing answers from a position of detailed knowledge about the subject, only voicing an opinion about how price affects their feelings about buying. There is, therefore, a greater risk of answers being skewed by the way questions are worded, not giving an entirely true picture of what people think.
For that reason, customer surveys tend to be quantitative rather than qualitative, or a mix of both. That means surveying a large enough sample size to make averages statistically robust, which in turn usually means asking hundreds of participants.
Customer surveys take time and can be costly.
It’s good practice to ask the same or similar questions in different ways, either on the same survey given to all participants or asking variations of the same questions to different sample groups. This minimises the risk of any bias caused by how questions are worded and make the data collected as detailed and nuanced as possible.
You typically start your survey with a small subset of customers to test different questions and then roll it out to 300 or more customers to have a sufficient sample.
3. Conduct price experiments
One of the biggest trends to impact on marketing over the past decade has been a move away from focus group and survey-oriented customer intelligence gathering to what might be called ‘live’ experiments. These mean the following: giving customers a controlled set of options and seeing what choices they make.
If you wanted to assess the impact of a price change you would carry out what is known as a split or A/B test. A certain group of customers would see price A (say a 5% rise) and another group would see price B (say a 5%) decrease, with a third ‘control’ group, offered the product at the present price. You could then gather data about how the changes affected demand simply by recording the volume of sales.
To give accurate and reliable results, however, only one variable should be changed in an A/B test, with all other factors kept constant. If more people see price A than price B, for example, the demographics of the shoppers are different, or shop layout or user experience differ, it is hard to draw robust conclusions that the results you get are down to price only.
In a store, it’s hard to control so many different variables at once, which helps to explain why A/B testing has grown in popularity with the rise of eCommerce. Through an online store or web search or even social media, it’s relatively easy to send different people to different landing pages for the same product, keeping numbers constant and controlling for known demographic factors. Online activity also gives you a greater range of data to work with on top of plain sales figures. For example, you can count click through rates from an advert to a product page to gauge initial interest, you can measure cart abandonments at different prices and so on.
Overall, with a large enough sample size (again, test and control groups should be counted in the hundreds), pricing experiments should give you the most accurate and robust data to use to plot your price-demand curve.
Once you have your results, you can then carry out analysis to decide if the expected impact of a price change would be good for your business or not. If you have carried out pricing experiments, with split tests at enough different price points, you might be able to plot a standard price-demand curve as shown in the milk curve (Fig. 1) directly.
now it’s your turn
Which of these 3 options is the right one for you?
👉 We recommend you choose to approach market experts if you are looking for quick results and don’t have sufficient customer and sales data at hand for the moment.
👉 If you have a loyal customer base and the resources to interview at least 50 customers, the survey of existing customers would work best for you.
👉 For the best and most reliable results, pricing experiments are the way to go. We recommend this method especially for online businesses, which can conduct price experiments easily in their online shops.
th!nkpricing is a brand of Smart Pricer. We are making professional pricing accessible to everyone by offering a platform to understand, simulate, and optimize your pricing with machine learning-driven algorithms and advanced demand prediction.
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