By definition, Machine Learning is the design of systems that can learn from and make decisions and predictions based on data. Machine learning allows computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task. Machine Learning programs are also designed to learn and improve over time when exposed to new data. Machine learning has been at the center of many technological advancements in recent years such as self-driving cars, computer vision and speech recognition systems.
The trends section is at the bottom, we wanted to give a little overview of Machine Learning for context.
Supervised vs Unsupervised Learning
Supervised Learning is when a program is “trained” on a pre-defined dataset. Based off this training data, the program can make accurate decisions when given new data. An example would be building a business rule to mark all even numbers with a “positive” of “favorable” sentiment vs. all odd numbers with a “negative” or “unfavorable” sentiment. When new numbers are presented the program, the program will check to see if the number is positive or negative and assign sentiment.
Unsupervised Learning is when a program, given a set of data can automatically find patterns and relationships in that dataset. An example of this would be for a program to collect all tweets from Twitter and group them based on keyword or topic. Another name for this is called Clustering.
Classification generally falls under Supervised Learning, and is the process taking in data, analyzing it, and classifying it. Classification systems are usually used when predictions are of a discrete, or “yes or no” nature. An example of this would be taking in all stocks on the Russell 2000 and determining if each stock’s Relative Strength is greater than 92. If yes, then the stock will be classified with a “Yes”; otherwise, a “No”.
Regression is another sub-category of Supervised Learning and is used when the value being predicted has the possibility of multiple values, more of a continuous spectrum or value curve. An example of this would be to take the same Russell 2000 dataset, and determine if a correlation exists on Relative Strength (dependent variable) when an independent variable, such as Investor Sentiment is introduced. This is frequently used to determine correlation, predictions or trends.
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences. Probability is the core metric used with decision trees.
In probability and statistics, a Generative Model is a model used to generate data values when some parameters are hidden. Generative models are used in Machine Learning for either modeling data directly or as an intermediate step to forming a conditional probability density function. An example of this would be collecting a large amount of data in some domain, let’s say Twitter Tweets, and then train a model to generate data like it – Tweets with a similar sentiment automatically.
Discriminative Models or conditional models, are a class of models used in Machine Learning to model the dependence of a variable y on a variable x. As these models try to calculate conditional probabilities, they are often used in Supervised Learning. An example of this would be Logistic Regression used to predict whether a patient has a given disease based on observed characteristics of the patient (age, sex, body mass index, results of various blood tests, etc.).
Deep Learning refers to a category of machine learning algorithms that often use Artificial Neural Networks to generate models. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics.
Neural Networks or Artificial Neural Networks
Inspired by biological neural networks, artificial neural networks are a network of interconnected nodes that make up a model. They can be defined as statistical learning models that are used to estimate or approximate functions that depend on a large number of inputs. Neural networks are usually used when the volume of inputs is far too large for standard machine learning approaches previously discussed.
9 Best Machine Learning (ML) Trends in E-commerce
Here a list of some of the current trends we are seeing in E-commerce usage of Machine Learning.
1. Pricing Optimization
Price is, unsurprisingly, one of the most important factors when considering a purchase. In fact, price is one of the major drivers for close to 50% of customers. With ML, these factors could be assessed in a fraction of a second and your site will display dynamic pricing: showing the customer the most up-to-date price for them based on the aforementioned factors.
2. Segmenting, Personalization and Targeting Customers
In traditional brick-and-mortar stores, segmenting was done solely by sales associates, who approached the customers, observed, asking a few questions, and in doing so, gathered enough information about demographics, their needs and doubts, and their overall mood based on non-verbal communication. Much harder to do this digitally without ML. With ML, when searching, shoppers use natural language, which gives clues about their background and native language. Based on the results they click on, an algorithm can determine what they are looking for and offer more relevant results, which encourages them to make a purchase.
3. Search Results Optimization
Providing search results based on keywords is just the most basic step in on-site search. In order to provide shoppers with the best experience possible, your search should go much deeper than that. Given a large enough data set, you could determine which results are better for people in certain locations, how to optimize search result filters, and which products best match their needs based on their previous behavior. With ML, by analyzing the data and figuring out what items go together, you can also recommend similar products, and even cross-sell items that are frequently bought together by your users.
4. Product Recommendations
Effective automatic product recommendations can really drive growth. Take Netflix for example, according to McKinsey, 75% of what people watch on the streaming platform is suggested to them via an algorithm that analyzes user behavior. (The same is true for 35% of purchases at Amazon.). ML can help with offering ultra-targeted products to your customer, as long as you have a very large number of variables, each weighed differently.
5. Predictions About Your Customers
Machine learning can tell you a lot of things about the people who visit your site and make a purchase – even things like how likely they are to buy from you again or what they might be interested in. With ML, your brand could determine which shoppers are the most likely to abandon your site – based on things behavior such as: returning to your store less frequently, smaller purchases, etc.
6. Chatbots for Automated Customer Support
Customer service is extremely important and extremely inefficient and costly – with significant trade-offs between quality vs. cost. With ML, chatbots are able to maintain a conversation with the customer. Not only by using previously defined answers, but also via AI – they’re able to learn about natural language from every conversation.
7. Inventory Management
Internet of Things (IoTs), like your smart fridge reminds you when you’re running low on pineapple juice, and put’s it into your connected grocery list. With ML, you can do that with your inventory management when connecting your front-end (E-commerce site) to your back-end (ERP type of system) to monitor all your inventory, and even predict future trends in supply, demand and cash flow.
8. Omni-channel Marketing Boost with ML
Omni-channel marketing brings you higher retention and conversion rates and boosts your revenue. But only if you use the available channels wisely. With ML, learning algorithms can analyze your messages, review ads that performed well, understand frequently used keywords and popular content and display content and ads in a way that ensures every customer gets the perfect one (re-marketing concepts).
9. Image Processing and Recognition
Image recognition can be a great tool for an online store with thousands of products in their inventory. With ML, a customer can upload a photo of a given product at home or at the store. The system then processes it on the store’s servers, and instantly displays the product, availability, current price, shipping info and so on.
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