Machine Learning For Crm

If you’re looking for ways to boost your CRM efficiency, it doesn’t get much more useful than machine learning. Using this powerful subset of artificial intelligence, computers can learn specific tasks and skills from large amounts of data. Here are some common uses for machine learning in CRM systems and what they can do for your company.

In this post, we review the aspects of Machine Learning For Crm, artificial intelligence in customer relationship management, accuracy of different machine learning algorithms, and which machine learning model to use.

Machine Learning For Crm

Machine learning is a subset of artificial intelligence (AI). It’s a set of algorithms that allows computers to teach themselves how to do things using large amounts of data. In this article, we’ll go over some common uses for machine learning in CRM systems and what they can do for your company.

The science of learning from data is called machine learning.

Machine learning is a subset of artificial intelligence (AI). Machine learning is the science of learning from data and it’s a way to develop computer programs that can change and improve with experience.

Machine learning algorithms can be used for a wide range of applications, including search engines, fraud detection, robotics, and healthcare.

Let’s look at some examples where machine learning has been successful:

Machine learning is a subset of artificial intelligence (AI).

Machine learning is a subset of artificial intelligence (AI). AI is the science of making computers do things that require intelligence when performed by humans. AI encompasses a broad field, and machine learning is just one branch of it.

Machine learning is the use of algorithms to let computers learn more effectively. It’s often used in conjunction with other types of programming, including data mining and natural language processing (NLP). Machine learning can also be used for pattern recognition, computer vision, deriving meaning from text or speech, as well as decision-making under uncertain conditions like those found in game playing with imperfect information or financial trading where there are no fixed rules about which action should be taken next.

Machine Learning For Crm

CRM refers to the customer relationship management software that helps businesses manage their customers’ data and interactions. In a nutshell, it’s a platform that allows you to track your customers’ actions and behaviors in order to improve your business processes.

Machine learning is the idea that computers can automatically learn from data without being programmed how to do so. This means that with machine learning, you don’t need an expert programmer or developer on hand if you want your CRM software to be able to learn from its own data—you simply input the data into the system, and let it take care of the rest!

The benefits of using machine learning for CRM include:

  • Increased efficiency – You’ll be able to spend less time on manual tasks like reporting because your system will be doing much of this work for you instead. This means having more time to focus on growing your business while still staying competitive!
  • Improved accuracy – Machine learning algorithms are based on historical data which can help reduce errors when making predictions about future behavior (such as which customers might leave).

artificial intelligence in customer relationship management

Clear C2

Artificial Intelligence for years has been the symbol of what the will future bring…whether it be robots in the workplace or smart appliances in your home. Well, the future is now. AI is commonly described as intelligence displayed by machines, as opposed to “natural intelligence” which is displayed by humans and other animals. The long-range plan by numerous technology vendors is to have AI evolve to include machine learning, reasoning, knowledge, planning, voice/speech recognition, text analysis, perception, and the ability to move and manipulate objects.

AI technologies are becoming more important to the world of business applications each year. And with more integration, data sets, and machine learning capabilities, the technology should continuously get more advanced. When you couple the massive data explosion that is currently happening with the “internet of things”, there is a plethora of personal data we’re putting out there to be mined. The need for AI technologies is being required more and more to sort it all out.

Past and present trends have been to move away from legacy CRMs that historically were mostly on-premises and operated as Excel replacements and static data-entry systems. There’s been a shift towards deploying more cloud-based CRM systems that act as digital assistants, rather than basic data input tools. Software as a service, mobile and social are all becoming more prominent. With all the available information across many devices and platforms, companies had to have a way to integrate this “big data” into their cloud-based CRM in a way that produces results that are more predictive in nature. AI for CRM, powered by machine-based learning, is optimized for these large data sets.

AI and machine learning are still in their infancy stage when used with a CRM. However, in the next few years businesses will/should be able to deliver more predictive and personalized customer experiences across sales, service, marketing, and commerce resulting in accelerated sales cycles, improved lead generation/qualification, personalized marketing campaigns, and lower costs of support calls.

accuracy of different machine learning algorithms

Evaluating your machine learning algorithm is an essential part of any project. Your model may give you satisfying results when evaluated using a metric say accuracy_score but may give poor results when evaluated against other metrics such as logarithmic_loss or any other such metric. Most of the times we use classification accuracy to measure the performance of our model, however it is not enough to truly judge our model. In this post, we will cover different types of evaluation metrics available.

Classification Accuracy

Classification Accuracy is what we usually mean, when we use the term accuracy. It is the ratio of number of correct predictions to the total number of input samples.

It works well only if there are equal number of samples belonging to each class.

For example, consider that there are 98% samples of class A and 2% samples of class B in our training set. Then our model can easily get 98% training accuracy by simply predicting every training sample belonging to class A.

When the same model is tested on a test set with 60% samples of class A and 40% samples of class B, then the test accuracy would drop down to 60%. Classification Accuracy is great, but gives us the false sense of achieving high accuracy.

The real problem arises, when the cost of misclassification of the minor class samples are very high. If we deal with a rare but fatal disease, the cost of failing to diagnose the disease of a sick person is much higher than the cost of sending a healthy person to more tests.

Logarithmic Loss

Logarithmic Loss or Log Loss, works by penalising the false classifications. It works well for multi-class classification. When working with Log Loss, the classifier must assign probability to each class for all the samples. Suppose, there are N samples belonging to M classes, then the Log Loss is calculated as below :

y_ij, indicates whether sample i belongs to class j or not

p_ij, indicates the probability of sample i belonging to class j

Log Loss has no upper bound and it exists on the range [0, ∞). Log Loss nearer to 0 indicates higher accuracy, whereas if the Log Loss is away from 0 then it indicates lower accuracy.

In general, minimising Log Loss gives greater accuracy for the classifier.

Confusion Matrix

Confusion Matrix as the name suggests gives us a matrix as output and describes the complete performance of the model.

Lets assume we have a binary classification problem. We have some samples belonging to two classes : YES or NO. Also, we have our own classifier which predicts a class for a given input sample. On testing our model on 165 samples ,we get the following result.

There are 4 important terms :

Accuracy for the matrix can be calculated by taking average of the values lying across the “main diagonal” i.e

Confusion Matrix forms the basis for the other types of metrics.

Area Under Curve

Area Under Curve(AUC) is one of the most widely used metrics for evaluation. It is used for binary classification problem. AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. Before defining AUC, let us understand two basic terms :

False Positive Rate and True Positive Rate both have values in the range [0, 1]. FPR and TPR both are computed at varying threshold values such as (0.00, 0.02, 0.04, …., 1.00) and a graph is drawn. AUC is the area under the curve of plot False Positive Rate vs True Positive Rate at different points in [0, 1].

As evident, AUC has a range of [0, 1]. The greater the value, the better is the performance of our model.

F1 Score

F1 Score is used to measure a test’s accuracy

F1 Score is the Harmonic Mean between precision and recall. The range for F1 Score is [0, 1]. It tells you how precise your classifier is (how many instances it classifies correctly), as well as how robust it is (it does not miss a significant number of instances).

High precision but lower recall, gives you an extremely accurate, but it then misses a large number of instances that are difficult to classify. The greater the F1 Score, the better is the performance of our model. Mathematically, it can be expressed as :

F1 Score tries to find the balance between precision and recall.

Mean Absolute Error

Mean Absolute Error is the average of the difference between the Original Values and the Predicted Values. It gives us the measure of how far the predictions were from the actual output. However, they don’t gives us any idea of the direction of the error i.e. whether we are under predicting the data or over predicting the data. Mathematically, it is represented as :

Mean Squared Error

Mean Squared Error(MSE) is quite similar to Mean Absolute Error, the only difference being that MSE takes the average of the square of the difference between the original values and the predicted values. The advantage of MSE being that it is easier to compute the gradient, whereas Mean Absolute Error requires complicated linear programming tools to compute the gradient. As, we take square of the error, the effect of larger errors become more pronounced then smaller error, hence the model can now focus more on the larger errors.

which machine learning model to use

Why are there so many machine learning techniques? The thing is that different algorithms solve various problems. The results that you get directly depend on the model you choose. That is why it is so important to know how to match a machine learning algorithm to a particular problem.

In this post, we are going to talk about just that. Let’s get started.

Variety of machine learning techniques

First of all, to choose an algorithm for your project, you need to know about what kinds of them exist. Let’s brush up your knowledge of different classifications.

Algorithms grouped by learning style

It’s possible to group the algorithms by their learning style.

Supervised learning

In the case of supervised learning, machines need a “teacher” who “educates” them. In this case, a machine learning specialist collects a set of data and labels it. Then, they need to communicate the training set and the rules to the machine. The next step is to watch how the machine manages to process the testing data. If there are some mistakes made, the programmer corrects them and repeats the action until the algorithm works accurately.

Unsupervised learning

This type of machine learning doesn’t require an educator. A computer is given a set of unlabeled data. It is supposed to find the patterns and come up with insights by itself. People can slightly guide the machine along the process by providing a set of labeled training data as well. In this case, it is called semi-supervised learning.

Reinforcement learning

Reinforcement learning happens in an environment where the computer needs to operate. The environment acts as the teacher providing the machine with positive or negative feedback that is called reinforcement.

Machine learning techniques grouped by problem type

Another way to divide the techniques into groups is based on the issues they solve.

In this section, we will talk about classification, regression, optimization, and other groups of algorithms. We are also going to have a look at their use in industry. For more detailed information about every common machine learning algorithm, check out our post about machine learning algorithm classification.

Common algorithms

Here are the most popular ML algorithms. Sometimes they belong to more than one group because they are effective at solving more than one problem.


Classification helps us to deal with a wide range of problems. It allows us to make more informed decisions, sort out spam, predict whether the borrower will return the loan, or tag friends in a Facebook picture.

These algorithms predict discrete variable labels. A discrete variable has a countable number of possible values and can be classified. The accuracy of the prediction depends on the model that you choose.

Imagine that you develop an algorithm that predicts whether a person has or does not have cancer. In this case, the model that you choose should be very precise in predicting the result.

Typical classification algorithms are logistic regression, Naive Bayes, and SVM. More information about them and other algorithms you can find in our blog.


Sometimes you need to divide things into categories but you don’t know what these categories are. Classification uses predefined classes to assign to objects. On the other hand, clustering allows to identify similarities between objects, and then group them according to the characteristics they have in common. This is the mechanics that lays behind detecting fraud, analyzing documents, grouping clients, and more. Clustering is widely used in sales and marketing for customer segmentation and personalized communication.

K-NN, k-means clustering, Decision trees, Random forest can all be used for clustering tasks.


Trying to find out the relationship between two or more continuous variables is a typical regression task.

Note: If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable.

An example of such a task is predicting housing prices based on their size and location. The price of the house in this case is a continuous numerical variable.

Linear regression is the most common algorithm in this field. Multivariate regression algorithms, Ridge Regression, and LASSO regression are used when you need to model a relationship between more than two variables.


Machine learning software enables you to provide a data-driven approach to continuous improvement in practically any field. You can apply product usage analytics in order to discover how the new product features affect demand. Sophisticated software equipped with empirical data helps to uncover ineffective measures, allowing you to avoid unsuccessful decisions.

For example, it is possible to use a heterarchical manufacturing control system in order to improve the capacity for a dynamic manufacturing system to adapt and self-manage. Machine learning techniques uncover the best behavior in various situations in real-time – which leads to continuous improvement of the system.

Anomaly detection

Financial institutions lose about 5% of revenue each year to fraud. By building models based on historical transactions, social network information, and other sources of data, it is possible to spot anomalies before it’s too late. This helps detect and prevent fraudulent transactions in real-time, even for previously unknown types of fraud.

Typical anomaly detection algorithms are SVM, LOF, k-NN, k-means.


You can apply machine learning to build ranking models. Machine learning ranking (MLR) usually involves the application of supervised, semi-supervised, or reinforcement algorithms. An example of a ranking task is search engine systems like SearchWiki by Google.

Examples of ranking algorithms are RankNet, RankBoost, RankSVM, and others.


Recommender systems offer valuable suggestions to users. This method brings utility to users and also benefits the companies because it motivates their clients to buy more or explore more content.

Items are ranked according to their relevance. The most relevant ones are displayed to the user. The relevancy is determined based on historical data. You know how it works if you’ve ever watched anything on Youtube or Netflix. The systems offer you similar videos to what you have already watched.

The main algorithms used for recommender systems are collaborative filtering algorithms and content-based systems.

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