How Luxury Brands Can Use Machine Learning to Drive Business Decisions

Nearly every luxury brand either utilizes the power of machine learning or is interested in doing so, but creating models is only one step when it comes to achieving value.

Unlike general programming where one has to manually formulate or code rules, machine learning (ML) models can automatically make sense of newly entered data. It is important to note that this requires proper data quality, cross-departmental collaboration, and business acumen to deliver any type of value. However, if the right conditions are set in place, this type of AI can make a significant impact for those working in the luxury industry who wish to create lasting relationships with clients. 

Start by defining a pain point or goal

It is important to start with an understanding of exactly what type of value you would like it to provide for your brand. While there are numerous applications for ML, the following are just a few examples.

One application involves creating segments of your consumers via clustering. A cluster refers to a collection of data points aggregated together because of certain similarities. Using ML, it is possible to categorize your consumers based on when they made a recent purchase, their frequency of purchases, how much money they spend on purchases, a combination of these three, or other factors. This helps a brand better understand the types of consumers it has on a macro-level and is particularly useful when analyzing hundreds of thousands of data points or more. Clustering allows for the differentiation between clients with upsell potential and the affluent consumers that drive the success of your brand.  

ML can also be used to predict CLV or customer lifetime value- the entire net worth generated from a consumer during their relationship with the brand. With the proper type of transactional data, you can answer the question: How much will client X be worth given that she has all of these characteristic purchasing habits?

The good news is that these applications do not need to be developed from scratch as many CDPs (Customer Data Platforms) today incorporate clustering and predictive analytics into their solutions. Key players, particularly ones interested in entering or optimizing performance in fast-changing markets such as China, are either utilizing or seeking out local CDP solutions that fit within a diverse digital ecosystem and capture the behavior of young, digitally active Gen Z consumers. The benefit of creating these models on your own vs. using an existing solution is a higher level of customization that makes it possible to factor in the removal of certain features when it comes to creating predictions. For example, how would your CLV change while taking into account a new promotional offer or loyalty program?

In addition to having a full understanding of consumers, proper workforce and inventory management should not be ignored. It is possible to predict how many staff members should be scheduled for a particular day and even shift and to generate inventory-related predictions. Predictive weather analytics paired with current and planned stock data for individual retail locations can help luxury retailers make better strategic decisions regarding supply and optimize the client experience to increase sales per square meter in prime real-estate locations.

Ensure model quality and deployment

“Trash in, trash out” is a popular saying which signifies that if poor quality data is input into your machine learning model, your output will have absolutely no value. This means that it will become increasingly important for IT teams to focus on maintaining quality data.

Also, building a model is not the last step of the process. Once the model has been created, how can it be made accessible via other systems so that stakeholders can interact with it and create predictions using up-to-date data? Model deployment simply means integrating a machine learning model into an existing production environment. 

However, it is a crucial step that presents major challenges. There is often a discrepancy between the programming language in which a model is written and the languages your production system can understand – re-coding can extend the project timeline by weeks or months. While a data scientist knows how to build a model, a DevOps engineer has operational knowledge of the software development lifecycle, but each operates using different workflows. In an ideal situation, a proper MLops workflow would be implemented to ensure continuous delivery and automation. In addition, large brands often need to deal with regulatory requirements and in an agile way to attain cross-functional alignment for deployment. These are a few of the major reasons why nearly half of models created are never deployed. 

It is important to not only start by using business acumen to target specific goals before putting into place ML, but also to ensure that these models are deployed and end up being used by key decision-makers within your organization. However, despite the investment needed to set them up or to find a proper platform, as more and more brands begin to enter the scene and make use of their big data, applications for ML cannot be ignored by anyone who wishes to remain a player in the luxury industry.  

This piece is written by Mariam Ammar, Data Analyst Manager, Fabernovel China

Mariam, Data Analyst Manager at Fabernovel China, is passionate about technology and quantitative analysis, she supports companies in their innovation. She is expert in creating machine learning models to fit structured data and also in designing deep learning models for unstructured data which focus on image and natural language processing.

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