Solving challenges in the retail industry with Machine Learning

Digitalization has completely shifted the game of retail, especially during the COVID-19 pandemic. In order to reach customers in today’s fast-paced world, online shops must be equipped with dynamic content such as competitive prices and customer recommendations based on their preferences. However, many retailers are still making decisions based on manual reporting and static planning tools as well as non-intuitive management systems.

Inventory remains a challenge

When looking to sustain scalability and keep costs down, it’s important to consider things such as tracking and maintaining seasonal inventory, overstock or understocked, stocking a new store, and effective management processes.

To keep up with the most recent trends and stay ahead of clients’ expectations, merchandisers must stay on top of new trends and make data-driven decisions using key performance indicators and comprehensive reports. Unfortunately, many businesses make decisions based on intuition rather than data, and that can be a roadblock to growth and staying competitive. This can present challenges to keeping track of inventory and ensuring that you have what your customers need.

The benefits of Machine Learning

Implementing a new management system that meets the needs of merchandise planners could be a good solution for many retailers. Although it is technically integrated, such a program can only provide limited help by producing reports on key performance indicators – human decision-making is still critical.

While human interaction is important, the emergence of machine learning has paved the way for a more efficient means to forecast and make recommendations. Trends are taken into consideration by intelligent systems, which use empirical data from an available management system to aid with decision-making.

Combining a management system with the power of machine learning technologies allows data to be transformed into valuable information, generating smart predictions which will allow managers to find more efficient ways to meet their challenges.

Before launching into a machine learning approach, there are three steps that must be taken to create a personalized digital solution:

  1. Research: It’s important to examine the business to clearly define and understand its needs. One example would be a review of weekly forecasts based on historical sales data to measure inventory management planning and develop with the right method.
  2. Project Analysis: Next up is analysis and validation before choosing which methods and technologies to use for a prototype solution.
  3. Implementation: The solution is tested before it’s brought into production so any bugs or challenges can be addressed before launching the solution. Once it’s validated, it’s ready to be rolled out company-wide.

Transitioning from raw information to a machine learning solution

  • Data availability and validity are key elements that will determine the success of a machine learning project. A comprehensive and structured data analysis can identify sale trends in a few steps:
  • Data is collected and stored in different formats, with no intuitive descriptions.
  • Based on existing historical data, certain categories are selected and summarized for the project purpose (e.g. product types, color, size, price, season, year etc.).
  • The next step includes correlation and resampling, which are essential to data analytics. Mutual influences are analyzed to draw the most relevant conclusions.
  • Determine an appropriate data aggregation method for training the machine learning model. This is one of the most important as well as time-consuming processes because the chosen model must be complex enough to allow the pairing with learning algorithms.

Advanced artificial intelligence solutions

Machine learning solutions make decisions based on existing data, which is the best way to reduce or eliminate biases when training the algorithms. It can use forms of linear difference equations, map linear stochastic processes, or approximate more complex processes. The combined applications can manage a variety of processes, such as allocation of goods between central warehouses and other locations or manage retail stores.

Dynamic pricing is another notable outcome that can be achieved using this solution. The mechanism not only promotes sales at retailers but also optimizes turnover rates. However, there are some things to consider. Customers expect prices to remain stable throughout a specific time frame (for example, one day to another), which means that when determining new price reductions, these changes will be based on data collected by past purchases.

The sales figures can be used to optimize the distribution of stock from the warehouse as well as between stores. This will ensure that stores are always stocked and customers will find what they need.


Machine learning is one of the most exciting technological innovations in the last decade. It can help your business identify trends and provide continuous optimization to increase sales. As promising as this technology is, it does require specialized expertise and careful planning to avoid unintended biases. AscentCore has a deep understanding of artificial intelligence and machine learning and can help you bring a new level of efficiency to your workflows. Get in touch with us today to see how we can help your business.

Share This Post

Share on facebook
Share on linkedin
Share on twitter
Share on email


AscentCore Labs

Using Counterfactuals To Overcome Data Bias & Increase Model Fairness

For businesses across every industry, the customer experience is more important than ever. In a time when a brand’s success is often closely tied to customer loyalty, finding ways to improve and optimize customer service is critical. Keep reading for five AI-powered solutions that can help businesses transform and enhance their customer experience.

‏‏‎ ‎Insights‏‏‎ ‎

The Process Behind Meaningful Visualizations

Creating compelling visualizations through storytelling is the main goal in translating information into a visual context. When working with large data sets, the visualizations expose hidden insights, patterns or relationships in the data and at the same time capture, compress and expose the very essence.