Using AI to Identify Emerging Colors
Business Problem:
A leading clothing retailer, with a well-established brand and decades of market presence, was facing a steady decline in its market share among Gen Z shoppers over the past several quarters. This younger demographic, critical to the retailer's long-term viability, was increasingly drifting towards other brands, despite the company's numerous efforts to regain their interest and loyalty.
The company had attempted to introduce contemporary clothing items featuring trendy colors and limited-edition color schemes to appeal to Gen Z shoppers' diverse preferences. However, their efforts often hit a snag due to their inability to identify and predict emerging color trends early in the lifecycle.
To make matters more complex, the company had adopted a marketing strategy of using inventive, catchy color names for their products, like "morning burst" or "volcanic lava". While this approach was originally designed to create a more engaging shopping experience, it ended up obscuring useful data about which traditional colors (such as blue or purple) were actually in highest demand among Gen Z consumers.
Without a clear understanding of the color trends, the brand was losing its appeal to its target demographic, especially for those lines specifically designed with Gen Z shoppers in mind. Consequently, the company was losing its foothold among Gen Z customers, significantly impacting the retailer's internal clothing lines and overall business performance.
Solution:
Steven immediately recognized the necessity of more accurate and universal product labeling to unlock crucial insights from the sales data. Steven proposed a solution to analyze each product's online images, and using machine learning, assign primary and secondary Red-Green-Blue (RGB) color codes based on the percentage each color occupied in the product. The RGB color model, widely used in digital imaging, was chosen to ensure that the broadest possible range of colors could be accurately represented and identified.
Steven suggested utilizing these identified primary and secondary RGB color codes to assign labels based on universally recognized color names, such as "dark blue," "blue," or "light blue". This process, known as color quantization, would effectively map a broad spectrum of colors into a smaller, more manageable set of color categories that the company's data science and analytics teams could more easily analyze.
By implementing this data-driven approach to their product labeling, the retailer could create a comprehensive dataset where colors were consistently and accurately represented. This would allow the data science and analytics teams to discern the correlation between different color trends and consumer segments more effectively.
More importantly, the newly implemented system would enable the company to identify emerging color trends earlier in their lifecycle. By getting ahead of the color curve, the company could better respond to changing market demands, creating products that Gen Z shoppers would find more appealing. As a result, the company would be poised to reclaim its lost market share among this critical demographic, ensuring the continued success and growth of the retailer's internal clothing lines.
Using A.I. to Identify Color Representation
Recognizing the critical need for a data-driven solution, Steven began by amassing a vast dataset of the company's product images. This comprehensive dataset incorporated a myriad of color combinations, patterns, and styles representative of the retailer's diverse clothing lines. This step was crucial in ensuring the model would be trained on a realistic representation of the company's product range, thus enhancing the accuracy and relevancy of its predictions.
The initial data collection was followed by an essential pre-processing stage. During this phase, each image was resized to a uniform dimension, ensuring consistent input to the machine learning model. This step is vital in neural network-based models, as they require uniform input sizes. Normalization of pixel values was carried out next to bring all pixel intensities into a standard range, usually between 0 and 1, which assists in the model's training by providing numerical stability.
Data augmentation techniques were subsequently applied to the normalized images to create artificially altered versions, including rotations, translations, and zoom. This step helps increase the diversity of data available for training models, without actually collecting new data, leading to a model that's more robust and less prone to overfitting.
With the dataset suitably prepared, Steven turned to the core of the solution – a pre-trained convolutional neural network (CNN). CNNs, due to their unique architecture, are exceptionally good at image recognition tasks. Steven chose a pre-trained model, which had already learned to recognize many common visual features from vast amounts of data. This approach significantly reduced the computational resources and time required for training.
However, to make this model suitable for the specific task of color recognition, Steven fine-tuned the CNN on the collected dataset. This involved a process of training the CNN further, allowing it to learn to recognize color patterns and identify the most dominant colors within each image.
Following the fine-tuning, Steven set out to identify primary and secondary colors in each image. To do so, he turned to clustering algorithms, such as K-means, renowned for their ability to group similar data points together. Applying K-means on the color features extracted by the CNN, Steven was able to group these features into clusters. The largest cluster was taken as the primary color, and the second-largest as the secondary color.
To facilitate effective interpretation and further analysis, these color clusters were then converted into RGB color codes. The RGB color model is a practical way to represent color in digital systems, as it can mimic a wide spectrum of colors by combining red, green, and blue in varying degrees. By translating the identified color clusters into RGB codes, Steven enabled the model to recognize and interpret the colors more effectively, thereby paving the way for an insightful analysis of color trends among the company's product range.
Insights for Future Color Decisions
Following the identification and translation of primary and secondary color clusters into RGB color codes, Steven proceeded to the next crucial step— mapping these codes to universally recognized color labels, such as "dark blue," "blue," and "light blue." The technique he employed for this process was color mapping, a methodology that connects RGB values to a predefined set of color labels. This was a critical step in ensuring the output from the model was interpretable and actionable for the business team.
Steven then set out to evaluate and validate the performance of the machine learning models he had developed. He compared the models' color label predictions against labels generated by human annotators. To quantify this performance, he used several robust metrics that are commonly employed in machine learning.
Accuracy, which measures the proportion of correct predictions among total predictions, was one of these metrics. Precision and recall were also utilized; precision measures the proportion of correct positive predictions out of all positive predictions made by the model, while recall evaluates the proportion of correct positive predictions out of all actual positives. Together, these metrics provided a comprehensive view of the models' performance.
This evaluation phase was not just a one-time activity but part of an iterative process of fine-tuning the models. By analyzing the discrepancies between human and machine-generated labels, Steven could identify areas of improvement and adjust the models' parameters accordingly, leading to more accurate and reliable predictions over time.
This rigorous, multi-stage approach resulted in the successful creation of machine learning models that were not only capable of analyzing historical product images but could also be applied to future collections. The models returned each product's primary and secondary colors, providing a granular, data-driven understanding of the company's color palette.
With this newfound capability, the company was better equipped to understand and respond to emerging color trends in the market. Rather than relying on gut feeling or anecdotal evidence, the company could now base its decisions on robust data analysis, enhancing its ability to cater to the ever-changing preferences of the Gen-Z demographic. This use case exemplifies how Steven's expertise in data science and business strategy can translate into tangible, high-impact solutions for his clients.