Machine Learning and Dynamic Pricing



As data becomes more and more accessible, staying competitive means being able to dynamically price products as conditions change (and in real-time). And, instead of having to constantly monitor and adjust prices manually, dynamic pricing uses machine learning algorithms to do the brunt of the work for you.


We’ve talked about dynamic pricing quite a lot (you could even say that we’re the experts) so this piece will focus specifically on how machine learning leads to the best results.. If you’re new to dynamic pricing, you can find out how it works and how it differs from price optimisation here and here, then come back. Or if this isn’t your first dynamically-priced rodeo, read on.


Machine learning and dynamic pricing


While you know how dynamic pricing works, you might be asking how machine learning comes into play?


Basically, machine learning algorithms process a number of pricing situations and estimate the impact of each possible price on revenue, selecting the price point that is most relevant for a given company’s objectives and the prices that have a minimal impact on revenue. To do this, you’ll first need to collect data, including:


  • Historical sales data for the entire product portfolio

  • Customer behaviour - think purchase data and quantity, and even customer demographics

  • Competitor prices


This data can be used to segment similar customers - whether they purchase a product at given times or purchase certain products together, for example. Machine learning algorithms are then used to predict whether a customer is likely to purchase the product or not, given that they have been correctly grouped and they are willing to pay the price dynamically set for that group, or how other members of the same group acted in the same circumstance.


These algorithms can form part of a broader suite of tools to take into account the latent relationships between products in a product portfolio into account, where the prices generally aim to maximise sales and revenue for the whole portfolio.


For example, let’s consider an online retailer that is using dynamic pricing with a reinforcement learning algorithm. The objective is to increase the number of products sold. Customers are segmented based on their response to a discount, such as a ‘buy two get one free’ offer, where those that don’t opt for the discount tend to be less price sensitive (and may be more willing to pay more) than those who do choose the discount. The reinforcement learning algorithm can recommend a particular price range for each segment, where the price for a single unit is higher when site traffic is higher (which capitalises on the price elasticity of demand). And, if the number of products sold increases as a result, the algorithm is rewarded. If this objective isn’t achieved, the algorithm is penalised and continues to explore different strategies.


If you’re wondering about the risks of an A.I being left in charge of your pricing, rest easy knowing the algorithms involved are extensively trained on your historical data first, and that the dynamic pricing system you choose to utilise will be able to cater to your specific business needs (whether that be a focus on shifting a finite amount of stock nearing a use by date, or trialing different prices dependent on traffic through the site to aim for maximum revenue).


If you want to see more examples of dynamic pricing in action, who uses it, and how ecommerce has implemented it, head here, here and here.


Advantages


To implement dynamic pricing successfully, you need to collect and digest a relatively large amount of data to decide on the optimal price for a given product - in nearly every circumstance, the more the better. Rather than doing this manually for every product in your inventory, machine learning algorithms can use this data to determine prices much more rapidly than you or I could - and with the advantage that it remains accurate and focused on your business’s success constantly. While this example might seem pretty obvious, machine learning has a host of other advantages when it comes to dynamic pricing.


In terms of revenue, integrating machine learning into your dynamic pricing strategy can help pricing teams:


  • Identify previously unrealised revenue and margin opportunities

  • Avoid volume and margin losses

  • Quickly price new products using price segmentation

  • Anticipate early trends


And, machine learning algorithms can also work with limited data and even maintain fairness in pricing while still maximising revenue (and preventing customers from feeling that prices are discriminatory or unfair). Plus, if you’re looking for more than just dynamic pricing, certain platforms also optimise demand forecasting for your inventory - ordering by determining both the optimal amount of stock to reorder and when to reorder it.


Remi AI’s Approach


Our platform uses the aforementioned reinforcement learning to quickly learn about the competitive landscape of your company and how to best price your products against your competitors. Using historical data, the platform can anticipate demand while tailoring prices across your customer base. And, the platform constantly monitors market conditions, meaning that it can adjust its predictions in real-time as things change. Depending on the marketing you’re in, the platform can also ingest other data streams, such as weather or commodity prices to predict impact on your sales. And finally, the platform can be integrated with backends such as Shopify and Magento, meaning that prices will update as soon as a price recommendation is approve - saving you double handling.


Want to know more? Lucky for you, there’s plenty more where this came from on our blog. Have a read through our case studies to see dynamic pricing in action or find some interesting reads with our monthly recommended reading lists. Or, if you want to know more about how to implement dynamic pricing, you can drop us a line here.


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