And how you can use simulations to make better decisions
What is the point of increasing a price by 20% if sales drop by 30% at the same time?
Every pricing manager is familiar with this question. And it is more difficult to answer than it seems at first glance. This is because pricing decisions rarely have a linear effect.
In this article, we explain:
- why price changes rarely have an isolated effect
- how to assess their impact on sales and revenue more realistically
- and how simulations can help you professionalize your pricing.
The three levers of pricing – and how they influence each other
The basic formula for revenue is simple:
Revenue = price × sales
In practice, however, this equation is only a small part of the truth. Because:
As soon as the price changes, sales usually change – in unpredictable ways.
- A higher price can lead to fewer purchases.
- A lower price can open up new target groups.
- And sometimes sales remain the same even though the price rises – e.g., with loyal customers or niche products.
In pricing theory, the effect of price changes on sales is described by the price elasticity of demand. It indicates the percentage change in sales volume when the price changes by one percent. Elastic demand behavior (elasticity < –1) means, for example, that sales decline when prices increase, even if the unit price rises.
In many markets, this relationship is not linear. Small price changes can trigger large sales reactions, while larger changes sometimes have surprisingly small effects.
A simple example – with a surprising effect
A product currently costs $10 and is increased to $12 – a 20% increase.
👉 If sales fall by 30%, the result is:
- Before: $10 × 100 sales = $1,000 revenue
- After: $12 × 70 sales = $840 revenue
- Loss: $16 or –16%
This means that even though the price increases, revenue decreases.
This is not an exception, but happens more often than you might think – especially when price elasticity is not taken into account.
Why price elasticity is difficult to assess
In theory, price elasticity is a clearly defined measure – but in practice it is difficult to calculate accurately. This is because price changes rarely occur in isolation: they are usually accompanied by numerous other interdependent effects, which distort the analysis of pricing effectiveness. Such effects depend, for example, on:
- Product category (commodity vs. premium)
- Competitive situation
- Brand strength
- Availability (own and competitors)
- Historical purchasing behavior
At the same time, many companies lack sufficiently differentiated data to determine reliable elasticity values.
Consumer behavior is also not constant – psychological effects, threshold prices, or different reactions in customer segments make assessment even more difficult.
Simulations offer a practical alternative here: They combine real sales data with well-founded assumptions and enable different price scenarios to be systematically played out – a realistic approximation where exact predictions are hardly possible.
How simulations help to realistically assess price changes
Modern simulation tools make it possible to combine price changes with real sales figures from the past and use them to create forecasts.
Typical workflow:
- Analysis of the status quo:
- What prices, sales, and revenues have been achieved to date – per product, brand, or category?
- Price proposal based on rules:
- e.g., margin targets, competitive behavior, inventory, product attributes
- Multiple simulations with assumptions:
- How much are sales likely to change if prices are adjusted by x%?
- Calculation of the net impact:
- Revenue potential, contribution margin, price realization, price enforcement – for each product and totaled
Important: Simulation is no substitute for market research, but it provides a quantifiable basis for decision-making – often more sound than decisions based purely on gut feeling.
Which assumptions make sense – and what are the risks?
Two assumptions must be made in the simulation:
- Expected change in sales (%):
- e.g., –10% with a price increase of +5%
- Expected price realization (%):
- e.g., 90% if discount campaigns are often effective
Tip: Start with realistic values at the category level and refine them at the product level if necessary. Never apply old values across the board – observe how similar products have reacted to price changes.
How does oraya’s price management solution rupio help?
With rupio, you can:
- Create rule-based price suggestions (based on margin, competition, inventory, etc.)
- Start simulations based directly on these suggestions
- Make assumptions at category or product level
- Make results immediately visible – for 1,000 or 100,000 products at the same time
This is what it looks like in rupio:
And best of all, you can compare variants at any time – e.g., with more aggressive or conservative assumptions. This is how price ideas become informed decisions.
Conclusion: Pricing decisions require simulation capabilities
Making pricing decisions blindly is risky – and often expensive. If you want to set prices strategically, you need transparency about their impact.
Simulations help you realistically assess the effects of price changes – based on real sales figures and traceable assumptions.
👉 Tools such as rupio not only make this simulation possible, but also easy to use – even for companies with limited resources.
Would you like to take your pricing strategy to the next level?
We would be happy to set up a free demo system for you – with real examples and your own setup.