With industry examples, case studies, and best practices.
From airline tickets going through the roof during holiday seasons to Uber and other ride-sharing services charging higher prices in rush hour, we have become accustomed to paying different prices for the same services.
Traditionally, dynamic pricing was a tool reserved for industry giants like Amazon because of its implementation complexity and price tag.
But with the advent of dynamic pricing tools, small and medium sized businesses can implement a dynamic pricing strategy that helps them optimize revenue management.
In this article, we’ll answer the burning questions:
A dynamic pricing model (also called a surge pricing model) is a pricing method that changes product prices to optimize a business objective in an ever-changing market. The changes can be in real-time or they can happen over days.
For example, a pricing engine on an online retailer’s e-commerce site adjusts prices every minute to reflect fluctuations in customer demand.
Dynamic pricing can be used to chase multiple business objectives:
Dynamic pricing is often contrasted with fixed pricing. Unlike variable pricing, fixed pricing offers set prices for products or services, irrespective of market conditions.
There are many different types of dynamic pricing that differ based on the implementation approach and implementation goal of your dynamic pricing strategy.
In general, we can classify dynamic pricing implementation strategies into:
The rule-based approach is usually easier to implement, but it reaps fewer benefits. On the other hand, the machine learning approach is harder to implement but it’s more cost effective.
So what business outcomes can you improve with dynamic pricing?
A successful dynamic pricing strategy can help you:
Revenue and profit margins are often hard to grow side-by-side. Lower your product price point and you get a higher market demand, more purchases, and increased revenue while also cutting into your margins. And vice versa for higher price points. Dynamic pricing helps you identify that sweet spot where both are maximized.
Example: Olfin Car’s data-driven price adjustments drive higher revenue
Olfin Car is a leading seller of new and used cars in the Czech Republic with additional services in the field of financing, authorized car service, and insurance.
With the change in the global supply chains, Olfin Car struggled to understand how to price its product. Both market demand and supply were fluctuating drastically, shifting the price point of the same car model by disproportionate margins.
Olfin Car decided to use Keboola to automate the process of price determination. With Keboola, Olfin Car collected supply data from manufacturers (how many cars of model X were produced), competitors (pricing of model X on their site), and other resellers, and joined them into a single pricing engine that outputs near real-time pricing information based on market conditions.
The result? Olfin Car increased its sales by 760%.
Reduce the business warehousing and management costs by lowering the price of your stale inventory.
Rohlik is a data-driven e-commerce business that sells fresh food, vegetables, fruit, dairy products, and other expirable food products.
A big obstacle for any e-grocery is food waste. If the food doesn’t get bought, it ends up in the bin.
To tackle this issue, Rohlik uses Keboola and AWS to build machine-learning models that can propose real-time discounts for any given product.
The dynamically adjusted price takes into account information about the current stock, historical sales of the product (aka market demand), expected remaining time to sell the product and other parameters.
This way, price-conscious customers can acquire items at a discounted rate, while at the same time Rohlik can drastically cut food waste (and move stale inventory) while increasing its revenue and surpassing the $1 billion valuation mark.
With a dynamic pricing strategy, you can monitor your competition and adjust your offering to win over their clients with competitive prices. With a penetration pricing strategy, you can even set your prices to automatically pick the lowest price on the market and outbid your competitors, making it especially useful when entering new markets.
Mall Group, the leading e-commerce group in Central Europe, has been expanding its business throughout the old continent, conquering one country at a time.
Each market in Europe is specific - from different languages to distinct purchasing powers - conquering a new market can pose a challenge for the expanding business.
Mall Group decided to gain a larger share in new and existing markets by employing competitor pricing. For each of its 300.000 products, they decided they will price them at the same price as the lowest price offered by competitors.
With Keboola, the data team at Mall Group built data pipelines that collect information daily on product prices on competitor sites, match the prices with their own product offerings, and automatically adjust Mall Group’s prices to match the competitors’ lowest prices.
With Keboola’s automation, Mall Group was able to implement an aggressive growth strategy without the engineering overhead.
Let’s look at how you can achieve the same results with the right dynamic pricing strategy.
There is no one-size-fits-all dynamic pricing strategy - market conditions and approaches vary widely from industry to industry.
But all successfully implemented dynamic pricing strategies go through the same steps.
Start your dynamic pricing strategy by picking the commercial outcome your dynamic pricing model is trying to achieve. Here are some examples:
The business goal needs to be high-level enough to encompass various strategies while also specific enough to understand the vision of your dynamic pricing.
Without measuring the success of your dynamic pricing model, you won’t know whether your dynamic pricing strategy is on the right track.
You need to establish two sets of metrics: success metrics and guardrail metrics.
Success metrics help you measure directly the impact of your dynamic pricing model on the business outcome you chose in step 1.
Let’s look at a couple of example success metrics for different goals:
Alongside success metrics, you also need to establish guardrail metrics.
Guardrail metrics help you understand whether your dynamic pricing model has accidentally caused problems outside your revenue management.
Typical guardrail metrics are:
Once you know your business goal and how to measure it, it is time to set up your pricing method.
You need to decide whether to develop rule-based pricing or machine learning algorithms that will guide price-point determination.
As a reminder: Rule-based approaches are usually easier to implement but reap fewer benefits. On the other hand, machine learning approaches are usually harder to implement but find more optimal price points.
Dynamic pricing models only work when they’re based on quality data. Make sure to collect all the data you need.
For starters, you will need to collect data on your product costs and profit margins, to understand the range in which your prices can fluctuate dynamically.
But you might also need other data. For example, if you opted for competitive pricing in step 1, you’ll need to collect data on how products are priced by competitors.
Beware: data collection can be a black hole eating up a lot of engineering resources.
Instead of collecting all your data yourself, rely on software solutions that automate the data collection for you.
Keboola can help you speed up data collection in a couple of clicks. Simply choose your data source > click-click > save data to your database.
The best part? Once you configure the data collection in Keboola, you can set it on autopilot and let Keboola regularly collect fresh data for you with no extra effort.
Once you have the data and have decided on the approach, push it into production.
You have three implementation options:
Pick option three if you want to get the best result out of your dynamic pricing strategy.
Unlike manual collection it avoids human errors and unlike home-brewn solutions, you can test, deploy, and scale the model in weeks instead of wasting months of your own engineering resources.
We’re biased here and obviously think Keboola is the best all-in-one solution for dynamic pricing out there. Not just because of its stellar track record (implemented large scale dynamic pricing models for Olfin Car, Rohlik, and Mall Group), but also because with Keboola you get much more than just a dynamic pricing tool.
Keboola allows you to build up many data products, such as a customer 360 view, automated ETL pipelines, fraud detections, and many more.
Pick Keboola as your dynamic pricing tool and you’ll also get a platform that empowers you to build dozens of use cases.
Keep a finger on your dynamic pricing model performance.
Regularly check the success and guardrail metrics to see how the dynamic pricing model is affecting your business.
This step helps you evaluate whether your dynamic pricing strategy has been successful and you should continue or whether you should stop the model and revisit it.
Dynamic pricing can always be improved. By using the success and guardrail metrics as baselines, you can set up more ambitious goals, tweak your rules/machine learning algorithm, and improve the performance of the dynamic pricing model.
Now let's look at the best practices to follow and common pitfalls to avoid when running the 7 steps outlined above.
There are many best practices you can follow and potential pitfalls you need to avoid when implementing your dynamic pricing strategy. Here’s the recap of the most important ones:
Keboola is a data platform as a service that helps you automate all your data operations.
It helped Mall Group, Rohlik, Olfin Cars, Revolt BI, and dozens of other companies set up and implement their dynamic pricing strategy.
Keboola offers support for every step of the dynamic pricing strategy implementation:
Curious about how Keboola can increase the revenue of your business?
Get in touch and let’s find out together.