Sears, Toys ‘R’ Us, JC Penney’s. What do these companies all have in common?
They failed to adapt properly to the increasingly data-driven retail market.
90% of the world’s data has been generated in the last two years. The data being created and collected is becoming more and more complex, and is re-shaping how the retail industry does business.
But as the amount of data businesses need to analyze grows and the markets they are competing in become more dynamic, retailers have been looking for a solution to their big data problems. Machine learning has proven to be a valuable asset in multiple areas of retail, handling more data than ever while providing consistently better results compared to human labor. Let’s take a deeper look into how companies are using machine learning to stay ahead in the market.
Traditionally, the balance of maintaining inventory according to demand was a process of trial and error. A lot of guesswork had to be done in an attempt to predict what products may go in and out of style or how many new customers may purchase products each month.
Machine learning takes the guessing game out of supply chain planning by optimizing root cause analysis. This gives retailers a more complete view of faults within an existing system, helps identify incomplete data, and avoids human bias or error. Over time, machine-learning can optimize supply chain planning even further as it identifies various patterns and relationships, becoming more reliable in the process.
The presence of machine learning in marketing campaigns is nothing new, but the extent to which it can be used in marketing has yet to be fully realized by retailers. Currently, machine learning helps to personalize advertising and promotions to customers by analyzing customer behavior and identifying high-value customers. ML can also make email marketing campaigns more effective by determining the best time to send emails to consumers, and give context to retailers regarding factors that traditional analysis may overlook such as weather.
AI and machine learning also play a huge role in the success of omnichannel retail. Customers likely use more than one platform to engage with a brand, which makes consistency difficult in terms of marketing. Machine learning can help identify which platforms are most effective in engaging different customer personas, and help retailers understand how a customers wants and needs change over time.
For example, machine learning is already being used by companies to determine whether a customer may prefer to purchase products in-store with a cashier or self-service checkouts. This information can help retailers maintain the delicate balance between automation for improved efficiency while avoiding the negative image that automating previously human encounters can cast on a brand.
Optimizing prices in a dynamic market is traditionally expensive, requiring extensive resources and an impressive IT budget. Like inventory planning, pricing is also a trial and error process for the most part. Considering the extensive amount of data that needs to be analyzed to change the price of just one product, price optimization used to be time-consuming and often lacked context that matched the scale of a company's product portfolio.
Machine learning helps companies optimize their prices in multiple ways. ML can combine historical and real-time market data for highly accurate price predictions, which like supply chain planning, become more fine-tuned as relationships between products and prices are observed for a longer period of time. Unlike traditional pricing methods, machine learning can give a much broader perspective of a company’s product portfolio, which allows companies to set prices that consider pricing relationships such as the halo effect and price cannibalization.
Not only are prices more accurate using machine learning, but price recommendations are also given significantly faster, allowing companies to keep up with market changes. This is how companies like Amazon change their prices multiple times per day, helping them increase sales and revenue in the process.
We’ve only scratched the surface of how machine learning is re-shaping how we do retail. However, one thing remains clear: if retailers wish to stay afloat in the modern market, they need to embrace machine learning in multiple aspects of their business strategy. From marketing campaigns to pricing and much more, ML is here to revolutionize retail, and it is here to stay. And as we’ve seen with Amazon, Walmart and others, the success of a retailer in the future will be directly related to how they use machine learning to optimize how they do business.
