Pricing Optimization With Machine Learning: Is it Worth it? – Total Retail

  • Lauren
  • July 13, 2020
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In recent years, artificial intelligence (AI) and machine learning (ML) technologies have successfully penetrated many industries, ranging from healthcare to automotive. However, retail is particularly susceptible to adopting various ML applications as data is this industry’s driving force both in the physical and e-commerce realms. For example, an AI-powered recommendation engine has become a must-have among retailers, enabling them to boost customer experience and drive revenue.Nowadays, retailers are keeping their ears to the ground to be the first to adopt the next industry changer. Of course, there are attention-grabbers like computer vision-powered robots that autonomously take care of inventory, or facial recognition cameras that detect customers’ emotions. However, the next big thing, ML-powered pricing optimization, seems to sacrifice flashiness for the sake of a significant competitive advantage and return on investment.
Setting optimal prices is one of the most difficult tasks even for retail veterans. With the continuous digitalization of our world, data is becoming more available and the number of relevant pricing factors is increasing. People, contrary to machines, are slower, prone to making errors, and simply lack capabilities to consider all factors at once. This calls for the next logical step — automated, ML-driven price optimization tools.
Let’s figure out the advantages of dynamic pricing, the roadblocks of its implementation, and the ways to determine if your business can benefit from it.
Dynamic Pricing Advantages
Firstly, ML-powered price optimization can provide complete automation of routine tasks, freeing up valuable time for employees and allowing them to focus on more creative work. Secondly, if set right, these tools can find unconventional relationships between a multitude of parameters, such as competitors’ prices, price elasticity, demand, inventory, seasonality, demographic features, etc. This not only helps set the most optimal price, but also provides retailers with valuable insights.
Thirdly, the digital nature of ML-based pricing optimization eliminates the possibility of human error and allows us to take into consideration a countless number of price forming parameters.
Implementing a Dynamic Pricing Strategy
Perhaps the first roadblock retailers encounter when trying to implement dynamic pricing models is the availability and quality of data. Especially so for smaller retailers, data is often scattered across different resources and, most importantly, can be erroneous. Moreover, gathering new data to feed into price-optimizing algorithms can take several months. However, this process needs to be done only once, and there are machine learning experts to assist retailers with data organization.
The second roadblock is that there’s no one-size-fits-all ML-powered dynamic pricing model suitable for each product or retailer out there. For example, if your business has the same or very similar products to your competitors, consumers’ decision making is likely to come down to which retailer offers the lowest price. Here you simply don’t need machine learning, but can use a common rule-based approach.
On the other hand, some retailers have exclusive brand-defining products, which can’t go below a certain price point. Moreover, some types of products are stocked just to attract in-store traffic, while others can be complementary products that actually drive profit margins.
These are just surface-level intricacies of dynamic pricing that have to be considered. Therefore, it’s highly advisable to hire specialized engineers to ensure automated price optimization strategies and tools are just right.
Further down the road to adopting ML for price optimization, another hurdle can be the unclear reasoning behind some ML-driven price recommendations. Given that ML tools can consider massive amounts of data in an instant, retail managers often can’t wrap their heads around the underlying logic, especially in the beginning. Appropriate training here is a must: the organization’s key decision makers need to trust these systems for them to be effective, and that’s why software vendors need to explain how exactly the system works.
Do You Need ML-Powered Price Optimization?
In short, probably yes. However, as with any other opportunity, you need to carefully examine the feasibility of implementing pricing optimization tools for your business.
First, define whether demand in your particular niche is elastic or not, and how frequently your customers make repeat purchases. If your customers aren’t willing to adapt to price fluctuations, pricing optimization may be a miss. Second, is your data clean and organized? If not, does your business have resources to invest in data management? ML algorithms are only as effective as your data.
All in all, it’s crucial to test the feasibility of dynamic pricing at least on a small group of customers and consider the cost of implementation. In most cases, though, dynamic pricing will come as a no-brainer.
An effective pricing strategy is one of the main pillars of any retailer’s success. Those that keep setting prices based on their gut feeling and few conventional metrics will fall behind pretty quickly. Data-driven decision making is an inevitable future of retail, and ML-based price optimization is one of the key factors of this transformation.
Of course, it’s never a good idea to blindly jump on a hype train. Therefore, a thorough evaluation of this approach is required. Besides the advantages of ML-powered pricing, certain implementation pitfalls need to be carefully addressed, including data quality and customers’ sensibility to frequent price changes.
At the end of the day, machine learning is here to stay. Given its ability to intelligently process massive amounts of data, retailers can not only set the most effective prices, but also drive this optimal pricing using science.
Andrey Koptelov is an innovation analyst at Itransition, a custom software development company.