Do you want to predict items and their revenue using machine learning? Here is the Solution!!!

To solve a business problem using machine learning, we must first understand the business context of the problem and then deep dive into the solution. Here, we will take an example of a clothing business, its pain points, and ways to solve the problem.

The main goal of the following example and solution is to get data scientists and business analysts to think like business people. So, rather than detailing the coding part, the key areas that are needed to ask the right set of questions to the business to solve the problem are detailed.

Business Scenario:

A major clothing business is having trouble boosting consumer pull and attracting new customers. They have a wide range of clothes and accessories, but they don't do enough to stand out from other stores and online marketplaces. This is hurting sales and profits, so they need to come up with ways to bring in more customers.

The above business scenario gives the fashion retailer a chance to improve customer pull in their stores and attract more people to their brand, which will lead to more sales and more money.

Solution:

To solve this problem, the fashion retailer can leverage machine learning algorithms to analyze customer behavior data and identify patterns and trends in customer preferences and purchasing behavior. They can also use predictive analytics to make their marketing and sales strategies more personal and to send relevant offers and suggestions to specific groups of customers. They can also use natural language processing (NLP) techniques to look at customer comments and reviews and find ways to make their products and services better.

From a marketing and sales standpoint, one way to improve sales is to target the right set of customers to purchase. In order to do this, analyzing the available data is a must.

We will keep this article limited to simple ML solutions, and in the future, we can try out solutions using NLP techniques in the case of Text data, and Computer Vision in the case of Image data.

Data Required:

The quality of the data we use to model in any ML project is critical to its success. According to data scientists, the only mantra is "garbage in, garbage out."

We need high-quality data that can be turned into features while we explore the data and do some preliminary tests of feasibility.

To implement this solution, the possible data sources are obtaining customer behavior data, including purchase history, demographics, and preferences, as well as customer feedback and reviews. They will also need outside sources of data, like social media and weather data, to find trends and patterns in how customers act and what they like.

1. Retail Customer Purchase History: This dataset contains transactional data of customers’ purchase history, including items purchased, quantity, price, and date. It can be used to train machine learning models to predict customer purchase behavior and recommend new items to customers based on their past purchase history.
2. Customer Reviews and Ratings: This dataset contains customer reviews and ratings of clothing items, such as dresses, tops, and skirts. It can be used to train machine learning models to predict customer satisfaction and sentiment towards different clothing items based on their reviews and ratings.
3. Dress Features: This dataset contains features of dresses, such as size, color, material, price, and style. It can be used to train machine learning models to predict the affinity of customers towards different dresses based on their features.
4. Demographic Features: This dataset contains features like customer age, gender, etc. It can be used to understand customer segmentation in case of implementingthe promotions at a segment level.

Response Variable Definition:

As we understand the business needs and analyze the available data. We can model the problem by defining a target variable.

To define the target variable to predict the affinity of a particular dress among your customers, you would need to start by determining what you mean by “affinity”. Affinity could be defined in different ways depending on your business goals and the data that you have available. Here are a few potential ways to define the target variable:

1. Purchase likelihood: One way to define affinity is as the likelihood that a customer will purchase the dress. In this case, the target variable would be a binary variable indicating whether or not the customer purchased the dress.
2. Rating or review score: Another way to define affinity is as the customer’s rating or review score for the dress. In this case, the target variable would be a continuous variable indicating the score on a scale of 1–5 or 1–10.
3. Engagement: A third way to define affinity is as the customer’s engagement with the dress, such as how often they view the dress online or how long they spend looking at it. In this case, the target variable would be a continuous variable indicating the level of engagement.

Outcome:

By making it easier for customers to buy from them and getting more people to come to their stores, the fashion retailer can boost sales, make more money, and get a bigger share of the market. They can also make customers more loyal and happy by giving them personalized and relevant experiences and figuring out where their products and services could be better. Ultimately, this will help them stand out in a competitive market and position themselves as a leading brand in the fashion industry.

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