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


Original post:

13 comentários em “Rule of thumb: Which AI / ML algorithms to apply to business problems

  1. I am the proprietor of JustCBD brand ( and am looking to grow my wholesale side of company. I am hoping anybody at targetdomain share some guidance 🙂 I considered that the most suitable way to accomplish this would be to talk to vape shops and cbd stores. I was hoping if anybody at all could suggest a reliable web-site where I can buy CBD Shops Business Contact List I am currently examining, and Unsure which one would be the very best solution and would appreciate any guidance on this. Or would it be easier for me to scrape my own leads? Suggestions?

  2. I am the co-founder of JustCBD Store company ( and I’m presently planning to broaden my wholesale side of business. It would be great if anybody at targetdomain give me some advice ! I considered that the very best way to do this would be to reach out to vape stores and cbd stores. I was really hoping if anybody at all could suggest a dependable website where I can buy Vape Shop B2B Marketing List I am presently reviewing, and Not sure which one would be the very best option and would appreciate any guidance on this. Or would it be simpler for me to scrape my own leads? Ideas?

  3. I truly love your site.. Great colors & theme. Did you develop this web site yourself? Please reply back as I’m planning to create my own site and would love to find out where you got this from or exactly what the theme is named. Thanks!

  4. After I initially left a comment I appear to have clicked the -Notify me when new comments are added- checkbox and now each time a comment is added I recieve 4 emails with the exact same comment. There has to be a way you are able to remove me from that service? Thanks!

  5. Your style is unique compared to other people I have read stuff from. Many thanks for posting when you have the opportunity, Guess I will just bookmark this site.

  6. Good post. I learn something new and challenging on websites I stumbleupon everyday. It’s always helpful to read through articles from other writers and use a little something from their sites.

  7. I seriously love your site.. Great colors & theme. Did you develop this amazing site yourself? Please reply back as I’m trying to create my very own website and would love to learn where you got this from or just what the theme is named. Cheers!

  8. I blog quite often and I truly thank you for your
    information. The article has truly peaked my interest.
    I am going to take a note of your site and keep checking for new information about once a week.
    I subscribed to your RSS feed as well. adreamoftrains web hosting service

Leave a Reply

Your email address will not be published. Required fields are marked *