Offer the right price at the right time with our machine learning powered dynamic pricing

Redefining the dynamic in dynamic pricing

Forget the old complex, rule-based dynamic pricing models. While they let you tap into revenues hidden by fixed pricing, their accuracy is suboptimal and maintenance heavy. Fourkind’s truly dynamic demand-based pricing solution improves continuously by learning from the feedback it gets from the booking data. It’s the ultimate way to get your pricing right.

Offer the right price, at the right time without increasing manual labor

Supports growth and business functions

Increases turnover and revenue

Optimizes the use of available resources

Saves valuable work hours with automation

EXAMPLE OF RESULTS FROM OUR CUSTOMERS:
PARKING PRICING

4%

revenue increase on average
(2.4% daily revenue increase)

VS

a traditional commercial dynamic
pricing solution.

HOTEL ROOM PRICING

13%

revenue increase per available rooms on average in selected locations

VS

a manually managed dynamic pricing model. In some categories up to 2 or 3 times more revenue.

A dynamic pricing model that improves continuously

Our dynamic pricing is a reinforcement learning based model. The system learns based on real-time data using myriads of significant data points. The system continuously learns and improves from this feedback. Thanks to our simplified architecture design, the dynamic pricing model is integrated into existing your existing booking system or online store.

IN A NUTSHELL, THE MODEL IS BASED ON:

Online reinforcement learning

Each action (such as booking) helps the model to learn the optimal price.

Exploration of new price points

The model continuously tests higher and lower prices to arrive at the optimal revenue.

Immediate learning from feedback

The model implements price changes automatically, in real-time without the need for human supervision.

Witness superior results in just two months

Typically, we begin with a Proof of Concept (PoC), which we can set up within two months from agreeing on a project. In the PoC phase, we compare the model against your current pricing model with A/B testing to demonstrate its superiority before integrating the dynamic model to your existing systems to enable real-time pricing.

The reinforcement learning based model is customized to the needs of your business and our pricing is typically based on revenue sharing.

A TRIED-AND-TESTED MODEL FROM IDEA TO DEPLOYMENT IN 4 MONTHS

Stakeholder interviews define pricing strategy goals

1.
2.

Selecting specifics for PoC, such as location and sales channel

Dynamic pricing fine-tuning

3.
4.

Connecting the model to existing systems

A/B testing against current pricing strategy

5.
6.

Go/no-go decision for the project

Plan for deployment

7.
8.

Deployment

At the end of deployment, we will train you or your partner to manage the model in the future. There’s no fear of a vendor-lock.

Delivers results in all industries

Our dynamic pricing works for any industry where supply and demand need to meet dynamically in real-time. In other words, where capacity is relatively fixed, but demand varies, dynamic pricing is the solution. Events, airlines, car rentals and other mobility solutions, hotels and hospitality industry, are good examples.

Whatever your key metric is, we can customize the model to match it.

SOME OF OUR LATEST DYNAMIC PRICING CLIENTS:
Our cooperation has been effortless and professional. Fourkind's expertise in dynamic pricing proved incredibly valuable."
Fourkind were not just tech vendors, they helped us understand the role and possibilities that data has in our entire business."
One of the greatest benefits of our cooperation was that manual pricing work decreased."

Antero Saarnio
Director Of Revenue Management
Forenom

Convinced?

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