Artificial intelligence (AI) can help optimize prices at any given moment to satisfy customers and maintain profit margins.
Price matters. It matters so much that according to a PwC report 60% of shoppers base their decisions on retailers because "their price is right". The challenge of setting the right price has never been so important, nor more complex. This complexity increases with the rise of the informed consumer, thanks to online catalogues, search tools and collaborative platforms, and is further compounded by the increasing amount of variables to consider in pricing decisions, like customer purchase histories, seasonality, inventory levels and competitors’ pricing just to name a few.
So what is optimal pricing? Optimal prices are prices that do not irritate or act as a significant pain-point for your customers, do not undercut margins and do not cannibalize sales of other items in the product portfolio.
To set optimal prices for a large product portfolio at a weekly or daily cadence, managers would need analytical and computational superpowers and the near in-human ability to make fast (and accurate) decisions - enter Artificial Intelligence (AI). According to PwC's industry report, IBM learned that as many as 73% of retailers are planning to use intelligent automation to enhance their pricing and promotion by 2021.
AI can help with price optimization at any given moment to satisfy customers and maintain profit margins. So, what makes AI so effective? Self-learning algorithms can scan vast amounts of data, endless pricing scenarios and suggest the most relevant price or evaluate the potential impact of sales promotions.
The speed at which an AI capability can be developed and applied to pricing in the past was dependant on access to a large technical team and budget. This is where The AI & Analytics Engine can assist in making AI for pricing more accessible to more businesses. As an easy to use end-to-end AI and machine learning development platform, The Engine provides a simplified end-to-end process and AI-powered guided recommendations to make sure the best decision is made at every stage of AI development.
The Engine can be used to develop ML models to automate time-and-labour-consuming pricing tasks, giving retail teams more time to focus on strategic customer-centric decision making. Its power is grounded in the fact that the models can learn patterns from data and can continuously integrate new information and detect new demands or emerging trends.
A common scenario where The AI & Analytics Engine can assist is where retailers alter their prices based on market demand. For example, an eCommerce store wants to estimate the best prices for new products for the upcoming season. Competition is high, so competitive price and promotion is a key consideration, as such, they adopt a competitive pricing strategy. The process might be as follows:
1. Gather input data: The AI & Analytics Engine could use the following;
- Product descriptions: information like the product size, colour, brand, style, manufacturing and purchase costs.
- Transactional data: sales history including product and customers who purchased.
- Data on past promotions and marketing campaigns.
- Data on competitors pricing on similar products and,
- Inventory and supply data
The AI & Analytics Engine can assist in smartly cleaning and preparing the data for modelling. And, produce a repeatable recipe to conform the new incoming data.
2. Define constraints and goals.
Retailers often pursue objectives like profit maximisation, however increasingly customer loyalty or new customer acquisition is of interest.
3. Select model and train: Data gathered previously is used to train the models.
With The AI & Analytics Engine, this step is accelerated with the help of AI-powered recommendations - so a clear understanding of the best model for your data and its performance criteria is understood prior to training.
4. Execute and adjust prices.
Once the model is trained prices can be estimated for the new products and tested. At this step, the Engine will also automatically monitor the model against new incoming data to make sure that it is performing optimally.
Machine Learning provides retailers with the ability to develop more complex pricing strategies that work harder and more efficiently to achieve business goals. Machine Learning techniques can be used in many ways to optimize prices, across many different pricing strategies. Price optimization is not limited to traditional retail models and has been used, with significant success, in industries such as hospitality, airline, car rental, and e-commerce to name a few.
Most importantly, the use of AI in retail continues to expand, and all signs indicate this will continue. If you are interested in how you could leverage price optimization at your company? Get in touch with us to find out how.