Data science and machine learning are both sides of a coin. Data science is an idea used to find big data that contains data cleaning and analysis. A data analyst collects data from multiple sources and applies machine learning and produces predictive analytics to extract important information from the collected datasets. Data analyst can understand data from a business point of view and can predict analysis more accurately and that provide business man to make good decisions. Machine learning can be defined algorithms to extract data, learning the data, and then predict future trends and knowledge for that topic. Traditional machine learning software is contains statistical analysis and predictive analysis that is used to catch hidden insights from extract data.
Data Science and Machine Learning use cases in E-Commerce
Recommendation engines are the most important tools in a retailers business. Retailers use these engines to pilot a customer towards buying their products. This engine provides better recommendation for retailers to increase their sales and find or predict new trends.
Market Basket Analysis
This is one of the most traditional methods of data analytics, retailers have been getting profit for years. This concept means that if a customer buys a combo of items that contain more or less likely to buy items. This method works with tooth paste and tooth brush combo.
Warranty data analytics helps both retailers and manufacturers to keep records on their products which contain potential lifetime, problems, and returns of products and also keep a check on any fraudulent activity to their product.
The main problem for retailers and manufacturers are to find right price. Right price means a price that will not affect retailers and manufacturers business and customer satisfaction and also they want to find price for a product that already in market. All of this is calculated with the help of price optimization method.
Location of new stores
Location analysis is playing a big role in Data analysis. Before starting a business, we want to find a place with less competition and customer can easily reach. Location analysis helps us to find better place. This analyzer helps us to find number of shops, number of people and trends in this locality.
Fraud detection and fraud protection are bad dream for all online stores. Machine learning technology can help us to secure processes and make them more efficient and easy. This technology notices every online transaction. Something went wrong it will find location and alert the owner and track the fraud.
Improved customer service
All ecommerce businesses know the importance of customer service because customer is the prime thing. Chatbots is the main use case of ecommerce machine learning. Lots of sites that provide Chabot assistance to their customer. So it can provide 24/7 support to customer.
Data Science tools, libraries, frameworks that are used in e-commerce.
Data Science and machine learning alone can’t do anything. So it require tools, libraries and frameworks for making our job easier. This Section is divided into three.
- Machine Learning algorithms
- Low code tools
- Code intensive tools
Machine Learning algorithms
Mainly, there are 3 types of Machine Learning Algorithms used in e-commerce.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
This algorithm consists of a target or outcome variable and a set of predictors or independent variable. This algorithm will predict dependent variable from a given set of independent variables. Using these set of variables, we generate a function that relate inputs to desired outputs. Eg: – Regression, Decision Tree, KNN, Logistic Regression etc.
In this algorithm, there is no any target or outcome variable to predict / estimate. It is used for gathering population in different groups, which is widely used for dividing customers in different groups for specific interventions.
Using this algorithm, the machine is getting trained for making specific decisions. The machine trains itself continually from previous training, so it learns from past experiences which include error and trial. That why it can capture the best possible knowledge to make accurate business decisions.
List of Common Machine Learning Algorithms
It is used to estimate or calculate the real values based on a continuous variable(s) eg: – cost of houses, number of calls, total sales etc. Here, we establish mapping between independent and dependent variables in a better way or by fitting a best line. This best fit line is called a regression line and it represented by a linear equation Y= a *X + b.
In this equation:
Y – Dependent Variable
a – Slope
X – Independent variable
b – Intercept
These coefficients a and b are derived based on averaging the sum of square difference of distance between data points and regression line.
For Eg:- linear equation y=0.2811x+13.9
It is a classification not a regression algorithm. It is used to approximate discrete values Discrete values means binary values like 0/1, yes/no, true or false based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by setting data to this function. So, it is also called as logit regression. Since, it predicts the probability, and the target values lies between 0 and 1 (as expected).
odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence
ln(odds) = ln(p/(1-p))
logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3….+bkXk
Graphical view of logistic regression
It is a sort of supervised learning algorithm that is mostly used for classification problems. It works for each categorical and continuous dependent variables. In this algorithm, we divide the population into two or more homogeneous (same type) sets of values. This is often done based on attributes/ independent variables to make as distinct groups as possible.
SVM (Support Vector Machine)
It is a classification method. In this algorithm, we plot each data value as a point in n-dimensional space where n is number of attributes with the value of each attribute being the value of a particular coordinate.
It is a classification technique based on Bayes’ theorem with a guess of independence values between predictors. In other words, a Naive Bayes classifier guess that the presence of a particular attribute in a class is unrelated or independent to the presence of any other attributes.
Bayes theorem provides an equation that calculate posterior probability P(c|x) from P(c), P(x) and P(x|c). Equation shown below:
P(c|x) is the posterior probability of class with from predictor (attribute).
P(c) is the prior probability of class.
P(x|c) is the probability of predictor from given class.
P(x) is the prior probability of predictor.
Low code tools to implement Machine Learning in your e-commerce
Google Analytics 360 provides the tools and supports for enterprise teams need to get detailed understanding from their data. Using Google Analytics 360, enterprise team can access advanced or complex tools, like Unsampled Reports, BigQuery Export, and Data Driven Attribution; apart from this enterprise team can all the standard Analytics features and reports. Google Analytics 360 also includes a service level agreement that includes data gathering, data cleanness, and reporting, a higher processing limit, and access to dedicated support specialists. Google Analytics 360 provide a Segmentation model, so enterprise team can build a Recommendation Engine driven by clubbing GA360 with BigQuery.
BigQueryML is one of Google’s best low-code Machine Learning approaches and used to build complex Machine Learning models. BigQueryML is easy to create and execute models because it uses standard SQL queries. BigQueryML is also help as to use existing SQL tools and skills. BigQuery ML increases development speed by eliminating the data move.
BigQuery ML functionality is available by using:
- The Google Cloud Console
- The bq command-line tool
- The BigQuery REST API
- An external tool such as business intelligence platform
Using this approach, we don’t want to about the complex implementation required to build recommendation engine. BigQuery ML does everything for us except type of Matrix Factorization needed to run. So we want to specify the type of Matrix Factorization.
Code intensive tools to implement Machine Learning in your e-commerce
TensorFlow Garden NeuMF
TensorFlow is an Open-sourced library based on Python used to create Machine Learning models. Using this library, we can implement or create Neural Matrix Factorization in TensorFlow Model Garden. This model can able to implement a worldly Collaborative Filtering Recommendation Engine by far better and fastest way. TensorFlow is open-sourced, we can implement or integrates quite easily with modern infrastructures and it is free to use. But setup Neural Matrix Factorization in production is difficult because we required a strong Python-developer and an ML Engineer for deploy.
Python is the commonly used programming languages for creating Machine Learning. Now Python is used to create applications based on Artificial Intelligence. Python has itself frameworks to help build an E-commerce website. TurboGears was full stack python framework and also an open source. TurboGears allows developers to build data-driven web applications very fast and easily. Python is generally used to build, train and deploy models using libraries such as SKLearn, Pytorch, etc.
What is different from other industries?
The E-commerce industry heavily links on the interactions made by a customer and the customers are at the center of business.
The Internet allows people from all over the world to get connected easily with inexpensively and reliably. It allows businesses to grow by selling their products and services online and gives potential customers, find new trends, and prospects. They can find competitor using the internet so they can provide better services that would lead to an increase in their business.
Personalization is referred to online retailers that define the practice of creating personalized communications and experiences on e-commerce sites. Personalization is done by dynamically showing content, media, or product recommendations based on browsing behavior and purchase history data of customers.
Search Engine Ranking
Every website has a rank. Ranking shows that website position in search engine. Page ranks in the results for a search query is higher, and then the higher the chance is that the searcher will click on this page. This shows the direct connection between rankings and traffic. Higher the Rank for a website then traffic also higher. So Ranking plays a big role in ecommerce industry. Ranking of a webpage is calculated by number of backlinks, URL structure, page load time (site speed) etc.
E-commerce is the one of the best business method that can continuously progressing and is becoming more and more important to businesses as technology continues to advance. Data science and machine learning are connected to each other and also connected to E-commerce. Using these methods, we can build a recommendation engine and that help us to find customers and new trends. There are a lot of use cases for Data Science and Machine Learning in E-Commerce which help us to grow our business smooth and safely.