Rule of thumb: Which AI / ML algorithms to apply to business problems


How to know which AI/ ML algorithm to apply to which business problem?

This is a common question

I found a good reference for it – Executive’s guide to AI by Mc Kinsey

I summarize the insights below


Firstly, there are three broad categories of algorithms:

  • Supervised learning: You know how to classify the input data and the type of behavior you want to predict, but you need the algorithm to calculate it for you on new data
  • Unsupervised learning: You do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
  • Reinforcement learning: An algorithm which learns by trial and error by interacting with the environment. You use it when you don’t have a lot of training data; you cannot clearly define the ideal end state; or the only way to learn about the environment is to interact with it


So, let us consider which algorithms can apply to business problems


Customer services and supply chain

  • Understand product-sales drivers such as competition prices, distribution, advertisement, etc linear regression
  • Optimize price points and estimate product-price elasticities linear regression
  • Classify customers based on how likely they are to repay a loan logistic regression
  • Predict client churn Linear/quadratic discriminant analysis
  • Predict a sales lead’s likelihood of closing Linear/quadratic discriminant analysis
  • Detect a company logo in social media to better understand joint marketing opportunities (eg, pairing of brands in one product): Convolutional neural networks
  • Understand customer brand perception and usage through images : Convolutional neural networks
  • Detect defective products on a production line through images: Convolutional neural networks
  • Power chatbots that can address more nuanced customer needs and inquiries Recurrent neural networks
  • Assess the likelihood that a credit-card transaction is fraudulent Recurrent neural networks
  • Predict call volume in call centers for staffing decisions Random forest
  • Detect fraudulent activity in credit-card transactions. (Achieves lower accuracy than deep learning) AdaBoost
  • Provide a decision framework for hiring new employees Decision tree
  • Understand product attributes that make a product most likely to be purchased Decision tree
  • Forecast product demand and inventory levels Gradient-boosting trees
  • Predict the price of cars based on their characteristics (eg, age and mileage) Gradient-boosting trees
  • Recommend what movies consumers should view based on preferences of other customers with similar attributes Recommender system
  • Recommend news articles a reader might want to read based on the article she or he is reading Recommender system
  • Stock and pick inventory using robots Reinforcement learning
  • Cluster loyalty-card customers into progressively more microsegmented groups Hierarchical clustering
  • Inform product usage/development by grouping customers mentioning keywords in social-media data Hierarchical clustering
  • Segment customers to better assign marketing campaigns using less-distinct customer characteristics (eg, product preferences) Gaussian mixture model
  • Segment employees based on likelihood of attrition Gaussian mixture model
  • Segment customers into groups by distinct charateristics (eg, age group)— for instance, to better assign marketing campaigns or prevent churn k means clustering
  • Analyze sentiment to assess product perception in the market Naive Bayes
  • Create classifiers to filter spam emails Naive Bayes
  • Predict whether registered users will be willing or not to pay a particular price for a product Simple neural network
  • Predict how likely someone is to click on an online ad Support vector machine




  • Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc) logistic regression
  • Diagnose health diseases from medical scans : Convolutional neural networks
  • Predict the probability that a patient joins a healthcare program Simple neural network
  • Predict how many patients a hospital will need to serve in a time period Support vector machine



  • Optimize the trading strategy for an options-trading portfolio Reinforcement learning
  • Optimize pricing in real time for an online auction of a product with limited supply Reinforcement learning
  • Generate analyst reports for securities traders Recurrent neural networks



  • Simple, low-cost way to classify images (eg, recognize land usage from satellite images for climate-change models). Achieves lower accuracy than deep learning AdaBoost
  • Optimize the driving behavior of self-driving cars Reinforcement learning
  • Balance the load of electricity grids in varying demand cycles Reinforcement learning
  • Predict power usage in an electrical- distribution grid Random forest
  • Provide language translation Recurrent neural networks
  • Track visual changes to an area after a disaster to assess potential damage claims (in conjunction with CNNs) Recurrent neural networks
  • Generate captions for images Recurrent neural networks



Linear/quadratic discriminant analysis: Upgrades a logistic regression to deal with nonlinear problems—those in which changes to the value of input variables do not result in proportional changes to the output variables.

Gaussian mixture model: A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)

Image source: Executive’s guide to AI by Mc Kinsey


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