How Machine Learning is Changing Pricing Optimization

Photo of Chaz Demera

Chaz Demera

Sep 18, 2020 • 3 min read

Dynamic price optimization is becoming increasingly popular among retailers.

Machine learning has exponentially increased their ability to process data and apply this knowledge to real-time price adjustments. But the necessary software is still relatively expensive. So how can it really help businesses? And, most importantly, what’s the return on investment at the end of the day?

Intelligent price optimization

Among the many innovations that machine learning enables in the retail sector, dynamic price optimization is definitely one of the hottest trends. But not every business following the hype will benefit from it to the full extent, and for some it might not be useful at all.

Intelligent price optimization is possible thanks to the powerful data processing capabilities that machine learning possesses. It gives retailers an opportunity to regularly adjust prices, taking into consideration market needs, customers’ expectations, and many other factors that the algorithm is being “fed”.


Traditionally, price optimization had to be done by humans and as such was prone to errors. Having a system process all the data and set the prices instead obviously saves a lot of time and manpower and makes the whole process more seamless. Employees can thus use their valuable time dealing with other, more creative tasks.

The other benefit: you can follow the market and set prices in a more flexible way. Thanks to machine learning, pricing is adjusted in a data-driven, complex way, not just using fragmented data and uncertain projections, as is the case with more traditional methods, for example with setting a manufacturer’s suggested retail price.

Will customers accept fluctuating prices?

The question remains: is your customer going to accept fluctuating prices? This has to be assessed on a case-by-case basis, but the good news is that algorithms can help businesses in this process. Companies experienced in implementing dynamic pricing algorithms, like Netguru, can help you make this initial appraisal.

Keep in mind that you will need a lot of data for the algorithm to function correctly. But you will only have to gather it once, and then simply update it with the most current information. If done properly, you won’t lose customers because of the fluctuating prices, but maximizing potential profit margins.

All these factors need to be considered before implementing a dynamic pricing algorithm for your retail business. It might seem complicated at first glance, but we’re here to help guide you through the process.

Photo of Chaz Demera

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Chaz Demera

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