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

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)

An Introduction to Clustering and different methods of clustering :