Throughout most of the history of the retail industry, pricing was an artform. It wasn’t an elegant artform either: it was frantic, confusing, and often inaccurate. No matter what strategy the retailer used or how many excel sheets they made, at the end of the day they had to pick a number, cross their fingers and hope that it wouldn’t completely break their profit margins. Today, pricing is a science. It is backed by immense amounts of data that is digestible, reliable, and instantly available. It leads to pricing decisions that maximize profits no matter how hectic the market is, and retailers have machine learning to thank for it.
Optimization of everything from pricing to store layout was always a core focus for retailers, so data has always played a role in these decisions even before machine learning came into play. However, the quality of data and the way in which it was collected was far from ideal. Up until the ’90s, obtaining competitor prices required hiring someone to walk around their stores and write them down. Inevitably, some products couldn’t be found, and by the time all of the pricing data was manually collected and analyzed, many of them had changed. This meant that the majority of pricing decisions came down to experienced managers making their best guess with the information they had, and the results were far from perfect.
When online stores became commonplace, web crawlers became the preferred way to collect data, but they also encountered issues of their own. They were not easy to create and were often blocked by web protection measures. Even as they became more advanced, it was extremely difficult to verify any of the data they collected and fill in the gaps they missed. Trying to analyze this messy data was a struggle of its own as well – you needed several very long spreadsheets, multiple calculations, and someone who was very good at math. However, in those cases errors were also frequent. Due to how difficult it was to go through all of this data, they were nearly impossible to find, let alone fix. So, retailers required new solutions able to combine a scientific background with an easy-to-use attitude.
Machine learning came in and completely changed the pricing data game. Its first big revolution in retail pricing was its ability to collect and analyze data. ML auto-crawlers use computer vision and instance-based learning to bypass web protection measures and collect data 1200% faster than before. They are 70% less expensive than manually created crawlers, and anomaly algorithms can detect any deviations in the way these crawlers work. For retailers, this meant the prices of thousands of products from all of their competitors are collected errorlessly and near-instantly. You can easily interpret that the need for Data Scientists grew exponentially in the retail market and more started enrolling for Data Science training to grab the opportunistic market.
What’s more, ML-based pricing software can visualize the obtained data for the retailer so they can get an accurate idea of their price positioning in real-time. This allows retailers to use dynamic pricing, which is an extremely popular pricing strategy for eCommerce retailers especially, as it allows them to change prices several times a day if needed to stay competitive.
Machine learning has even more applications in retail pricing as time goes by. When given one year’s worth of historical pricing data for supervised learning, ML-based price optimization software can be trained to detect patterns in sales, revenue, or other target functions to forecast optimal prices based on whichever pricing strategy a retailer chooses. All of this data and analyses are presented to the retailer in a readable manner through graphs, charts, and tables. This ML-based pricing software continues to learn over time, becoming more refined as it continues to analyze pricing data.
ML optimized both data collection and data analysis in retail pricing. It turned retail pricing from haphazard educated guessing into a data-driven science that can be predicted with eerie accuracy. Thanks to ML, we already see retailers like Amazon change their prices for millions of products each day to stay competitive, and it’s data processing capabilities are being applied to other roles within retail-like advertising. It has made retailers value data more than ever before, completely revolutionized the retail industry as we know it, and it is here to stay.
