Machine Learning – 9 Most Common Usecases for Higher Business Growth

This article represents some of the most common use cases of machine learning algorithms which has been found to impact business growth (in terms of revenues) in a positive manner. These usecases could be most commonly seen with all businesses which are running some or the other form of ecommerce site to support one or more aspects of their business. I have tried and provide information regarding which algorithm (or class of algorithm) could be used to come up with a solution for these usecases. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

• Sales & Marketing
• Product Pricing
• Product Roadmap
• Financial usecases

Most Common Usecases for Machine Learning

Following are some of the top/most common use cases for machine learning:

• Sales & Marketing: Following are some of the areas where machine learning (ML) models could help/impact business in a positive manner by increasing revenues, preventing customer churns, better sales targeting etc.
  • Customer Lead Scoring: ML algorithm could be used to identify the leads that will engage better, in order of priority. This looks to be classification problem.
  • Market Segmentation: ML models which can be used to discover customer of different types. This piece of information could be further used for achieving objectives such as sales targeting, product offerings etc. The clustering algorithm such as K-means could be used in cases like these.
  • Personalized Recommendation: This is very common with ecommerce websites. The ML models are created to identify the association rules in the pruchasing behavior of the customer and use the information to recommend the products that could be bought along with the chosen products (in other words, cross-sell, up-sell)
  • Churn prevention: To prevent the customer churn or, in simpler words, customer leaving is one of the top most concerns of every business. ML models based on classification algorithms (classification trees, K-nearest neighbours, ANN) could be used to handle problems such as customer churn prevention. The discrete variable in this kind of problem for which estimation is done is “whether a customer will churn or not”.
• Product Pricing: ML algorithms could be used to achieve targeted/optimum pricing based on offer feedbacks and in turn, optimize revenue. This is a regression problem and could be solved using regression algorithms.
• Product Roadmap: Deciding optimal product features to be developed/released could help business achieve greater customer satisfaction, and, in turn, achieve higher revenues. The clustering models could be used to identify products features from customer feedbacks, emails etc. Clustering models could help identify top N phrases representing N clusters that could be associated with N product features.
• Financial Aspects: Following are some of the most common financial aspects/problems that could be dealt using machine learning:
  • Credit/loan risk scoring: ML algorithms could be used to identiy potential cases where loan can be defaulted. This is a classification problem and could be solved using classification algorithms such as ANN, SVM, K-NN etc.
  • Fraud detection: More instances of frauds could be identifed using ML algorithms. In cases like these, clustering algorithms may fit the best.
  • Fraud discovery: This is a classification problem where new types of fraud instances need to be identifed.