How Can Machine Learning Help Ecommerce

Nisha Oommen | Updated 09 Sep 2021 | 4 min read

Developments in machine learning have a great influence on the e-commerce sector. Online purchase is steadily on the rise, allowing companies to capture all kinds of data on the customer experience, including

what are customers interested in
geographical location of visitors
what time it the sales most or least
when does traffic peak on the site
for how long they are on any given product page
what is the preference of the items
what are the items sold most on any particular given day of the week
what other products are they looking at
what do they buy
what are the triggers of the decision from the last window browsed before the purchase
how are they affected by rates, reviews and coupon
what is the optimal discount rate that triggers the purchase

Data received thus helps to make informed decisions based on the knowledge acquired through analysis. It makes it easier to collect and process the data, helps to see factors that went previously unforeseen.

What is Machine Learning?

Machine learning is defined as the science of getting computers to act without being explicitly programmed¹. Based on pattern recognition and computational learning theories in artificial intelligence, machine learning uses the study and construction of algorithms to learn from and make predictions on data².

Data-driven predictions or decisions can be made using algorithms and building the right model for data inputs. Machine learning is used for computing from a variety of data sets where designing and programming with the least error and highest accuracy is not possible.

Applications of Machine Learning in the E-Commerce Industry

Cross Selling/ Recommender Systems

' You may also like ' Section: Using a customer's past browsing and purchase history, what will they likely want to purchase in the future? This refers to making predictions based on the previously available data i.e what kind of product can I recommend for you?

Query Expansion

While the customer types in on the search, this feature allows autocompletion suggestions using the most likely search terms. The algorithms take into account frequencies of specific search terms as well as the particular customer profile (e.g. previous product views, age range, previous search terms).

Personalized Discount

Using prediction, you can try and figure out the optimal discount at which will a customer succumb. Minimize the number and rate of discounts you give while maximizing the number of conversions you can get.

Sentiment and Trend Analysis

Evaluate the public perception of a product based on sources like social media.

Churn Prediction

Prediction can be made on when customers’ interest in a particular product or feature will cease to be profitable allowing for countermeasures like cross-selling. Deciphering the characteristics of churners allows a company to reach out to churners, update the product or even discontinue a particular product line.

Inbound and Outbound Logistics Optimization

How many of what things do you need and where will we need them?

Fraud Detection

Match the data received against different data points and extract fraud scenarios. One instance can be matching the shipping address against the address on the credit card: it can raise a red flag if it does not match.

Product Classification

Products can be sorted automatically to speed up inventory management and improve customer navigation.

Wallet Share Estimation

Information emerging from the data analysis can give the proportion of a customer's usual spending in a category allowing the business to identify upsell and cross-sell opportunities.

Dynamic Pricing

Automatically adapt price as a function of supply and demand. Uber is a classic example of the usage of dynamic pricing. The surcharge applies when demand surpasses supply like during rush hour. It is a major way to optimize revenue.

Supply and Demand Analysis and Forecast/ Inventory Forecast

Make production and distribution more efficient by predicting market demands. Demand forecasting is all about understanding consumer demand for goods or services. Knowing how much stock to keep means avoiding unnecessary spending on overstocking or surplus on the one hand and avoiding the other extreme i.e sales lost due to lack of inventory or supply of goods. This enables lean inventory and prevents out-of-stock situations.

Chatbots and Automatic Answering of Phone Calls

This feature in e-commerce has been developed through speech recognition/synthesis and natural language processing via deep learning.

Target Market

Understanding the target helps you determine exactly what your products or services will be, and what kind of customer service tactics work best.

Merchandizing and Inventory Management

When to start stocking and how many. This is particularly applicable in the case of perishable goods.

Product Launch

From the data, prediction of customers most likely to buy can be targeted for product launches.

Channel Optimization

Optimal channels can be targeted based on behavioral characteristics.

Discount Targeting

What is the probability of inducing the desired behavior with a discount?

Market Basket Analysis

Market basket analysis helps in determining what products customers purchase together. For instance, market basket analysis might tell a retailer that customers often purchase bacon and egg together or shampoo and conditioner together, or beachwear and suntan lotion so putting both items on promotion at the same time would not create a significant increase in revenue, while a promotion involving just one of the items would likely drive sales of the other.

The right algorithms in machine learning help in the above processes.

Major Challenges of Machine Learning are