top of page

Business Problems AI can solve Effectively

Its important to understand that modern AI is just not a tool for to solve customers problems in a more effective manner. Its not that without modern AI , a you cannot build a good solution to solve a customers problem, but yes it can help the you solve the problem more effectively and may be with even higher speed.

Business Problems AI can solve Effectively
Business Problems AI can solve Effectively

In this lesson, I am going to talk about, which are the problems ML is helping to solve better. Here I am taking an example of a e-commerce website and how AI skilled executive are building for customer success.

Its important to understand, that ML currently following problems

1. Ranking System : Helps the user find the most relevant thing

2. Classification : Figuring out what types of thing/person/being is

3. Prediction : Predict the likely hood of an event

4. Recommender : Giving user things which they may be interested in

5. Clustering : Putting similar things together

6. Anomaly : Finding the uncommon thing

For understanding perspective, I shall be keeping things simple and from a Product Manager standpoint. To solve a problem using ML , two basic things need to be identified

1. Data : Volume and Variety

2. ML Algorithm or Algorithms to be used.

Lets start with e-commerce problem

Ranking System

Problem Statement : User is spending a lot of time on the Product Category page with low conversion rate.

Solution : Ranking of products based on highest purchase probability

Data Sets : Customer Behaviour/Activity, Customer Profile , Catalogue , Product, CTR and more

Learning : Supervised

ML Algorithms : Logistic Regression | Neural Nets | Decision Tree

Metrics : Higher Conversion | Higher Average Order Size


Problem Statement : Manual Cataloging is not scalable and a time taking effort

Solution : Automated Cataloging based on Image Recognition

Data Sets : Product Tags | Style Tags | Product Images and More

Learning : Supervised

ML Algorithms : CNN (Convolution Neural Networks)

Metrics : Reduced Manual Effort - Low Cost , Less Errors

Prediction Event

Problem : Users are dropping out at Checkout Page

Solution : Give Free Shipping to a user with high purchase probability based on product margins

Data Sets : Customer Behaviour/Activity | Customer Profile | Product Data | Logistics Data

Learning : Supervised | Unsupervised

ML Algorithms : Regression | KNN | Bayesian Clustering

Metrics : Higher Conversion


Problem : High Bounce Rate on Product Details Page

Solution : Recommending Similar Items to the user on the product page

Data Sets : Catalog | Sales | Customer Profile

Learning : Supervised | Unsupervised

ML Algorithms : KNN | Matrix Factorisation | Neural Netwok

Metrics : Higher User Engagement | Avg Order Size


Problem : Low click through rate of Email Marketing using traditional segmentation

Solution : Feature Identification beyond traditional segmentation

Data Sets : Customer Profile | Customer Sales Data | Customer Behaviour and More

Learning : Supervised | Unsupervised

ML Algorithms : K-Means | Expectation-Maximisation | Hierarchical Clustering

Metrics : Higher User Engagement | Avg Order Size

Anomaly Detection

Problem : Wrong Product Pricing gets displayed on the website , leading to order cancellations, returns

Solution : Notification to the pricing team when products price shows some anomalies

Data Sets : Catalogs Price | Competitor Price | Store Price | Brand Price | Category

Learning : Supervised | Unsupervised

ML Algorithms : Random Forest | Neural Nets | Gaussian Sb

Metrics : Customer Satisfaction| Loss Order


Machine Learning Fundamentals

Anomaly Detection for an E-commerce Pricing System :

Learning Ranking Assortment of Products

Machine Learning for Recommender systems — Part 1 (algorithms, evaluation and cold start)


Recent Posts

See All


bottom of page