A tutorial on predicting the next trend in a fashion e-Commerce context
Image by James Wheeler
Experience with the specific topic: Novice
Professional experience: No industry experience
Knowledge of machine learning is not required, it would help if the reader is familiar with basic data analysis. All concepts in the article are explained in detail from scratch for beginners. To follow along, download the sample datasethere. This tutorial explains a business application of Natural Language Processing for actionable insights.
Introduction to Natural Language Processing and Business use cases
A very simple definition that is commonly found online for Natural Language Processing (NLP) is that it is the application of computation techniques on language used in the natural form, written text or speech, to analyse and derive certain insights from it. These insights could vary quite widely, from understanding the sentiment in a piece of text to identifying the primary subject in a sentence.
The complication of the analysis can also range from simple to very complex. For instance you may want to categorise pieces of text, say from Twitter data, as having positive sentiment or negative sentiment. On the other hand you may want to extract product suggestions/complaints from product review data from a large dataset to figure out new product launch strategies. The work being done on chatbots, to understand text queries and respond to them; spam detection, etc. falls in the ambit of NLP.
This field can encompass many applications and thus many techniques, which makes it a fairly large topic. For this particular tutorial we will be discussing in detail a business case application of NLP, to derive practical and actionable insights in the e-commerce domain.
This dataset seems fairly typical of most e commerce marketplaces, and so it is a good representation. It is a dataset that has numerical rating data as well as text reviews of several items of clothing for women. The items have a “Clothing ID” which is a unique identifier. There are two kinds of ratings — a numerical 1 to 5 for product rating and another rating of 1 or 0 for recommend the product or not, a binomial attribute i.e it has only two possible values. There are also other attributes that describe the category of the item, the age of the reviewer etc. If you think about it, these attributes provided in this dataset are very typical of most such online shopping portals. In fact, this is not restricted only to the fashion domain and so the techniques applied here should be extendable to other domains as well.
With this dataset, it would be interesting to explore if the text reviews can predict whether the item would trend or not. That would be pretty useful information for a fashion business. If such insight was accurately available about a product the product or category manager can make sound decisions on the product’s inventory strategy, promotions etc. This is our business problem.
So the aim is to have an automated system, that can analyse the initial few reviews by customers of a new product and predict if the newly launched product will succeed or not. With this information the products inventory can be built up and it can be pushed through the supply chain. This way one can implement soft product launches and respond to the market’s acceptance of the product.
The two tools used for this experiment are Excel and Rapidminer. There are two parts to this workflow. First some feature engineering, carried out in excel, then the actual model training and testing, carried out in Rapidminer.
Part 1: Feature Engineering in Excel
With the data given, we need to first arrive at a mathematical definition for “trending product”. At this stage is where domain knowledge of the business comes in handy. We know that if a product is doing well and if customers are happy with it, they take the effort to write positive reviews about it. People generally tend to write detailed reviews either if they are very happy or very dissatisfied with the item. If they are happy, they tend to write good reviews and rate it highly. Also, products that do well get many reviews, and most reviewers would have a similar opinion of the product with high ratings.
To translate this mathematically; the product’s success has a direct relation between number of reviews, average rating of the product, and has an inverse relation with the standard deviation of the ratings for the product. That is if the market likes it, a lot of customers tend to rate it highly without much variation. We can create a new feature based on this understanding.