Predictive analytics extracts information from data sets to discover relationships, recognize patterns, forecast trends, find associations, etc. This technique allows us to anticipate the future and make the right decisions. The applications of machine learning in business intelligence are uncountable. In this post, we describe some of the most common ones:
  1. Customer targeting.
  2. Churn prevention.
  3. Sales forecasting.
  4. Market analysis.
  5. Risk assessment.
  6. Financial modeling.

You can use the data science and machine learning platform Neural Designer to easily build predictive models from your business data.

1. Customer targeting

Machine learning business application in customer targeting.

Customer targeting divides a customer base into groups of individuals similar in specific marketing-related ways, such as age, gender, interests, and spending habits.

It enables companies to target tailored marketing messages to customers who are likely to buy their products.

It has been proved that predictive analytics can identify potential customers much better than traditional techniques.

Some types of factors used in customer targeting are the following:

  • Socio-demographic factors: Age, job, marital status, education, etc.
  • Engagement factors: Recency, frequency, monetary, etc.
  • Past campaign factors: Contact type, day, month, duration, etc.

The advantages for the company here are:

  • a better communication with the customer,
  • a saving of money in marketing, and
  • an increase in profitability for the company.

An example of a business applications in machine learning, is the optimization of direct marketing campaigns in a banking institution. The goal is to predict which clients will subscribe to a term deposit.

2. Churn prevention

Machine learning business application in churn prevention.

Churn prevention aims to predict which customers, when, and why end their relationship with our company.

Retaining an existing customer is much cheaper than acquiring a new one. Therefore, customer churn can be a very costly phenomenon for companies.

By harnessing the power of big customer data sets, companies can develop predictive models that enable proactive intervention before it’s too late.

Some types of attributes used in churn prevention are the following:

  • Socio-demographic variables: Gender, age, education level, job category, marital status, nationality, etc.
  • Products contracted: Credit cards, insurance policies, etc.
  • Engagement variables: Recency, frequency, monetary, time, etc.
  • Product/service usage: Mobile, web, physical, call center, etc.
  • Technical incidents: Customer service calls, etc.
  • Stationary variables: Season, date, time, etc.
  • Competitor variables: Price, quality of services, etc.

The company then analyzes the causes of churn and takes the required actions to retain those customers.
For instance, we can offer customers a discount or an extra feature.

An example is to predict churn of telecom customers based on their account information.

3. Sales forecasting

Sales forecasting picture

Sales forecasting analyzes prior sales, seasonality, market-moving events, etc. It results in a realistic prediction of the demand for a product or service. Sales forecasting can be applied to short-term, medium-term, or long-term forecasting.

In this regard, predictive analytics can anticipate customer responses and attitude changes by looking at all factors.

Some types of variables used in sales forecasting are the following:

  • Calendar data: Season, hour, bank holidays, etc.
  • Weather data: Temperature, humidity, rainfall, etc.
  • Company data: Price, promotions, or marketing campaigns.
  • Social data: Economic and political factors that a country is experimenting with.
  • Demand data: Historical sales.

Sales forecasting is the cornerstone of a company’s planning. Indeed, it allows us to predict revenue and allocate resources optimally.

An example is to predict power demand in the electric industry accurately. The result is an improved forecast accuracy, which means better information to decide the best course of action.

4. Quality improvement

Wine quality test

Analysis of market surveys helps companies address customer requirements, increasing their profit and reducing attrition.

Some types of factors used in quality improvement are the following:

  • Product characteristics: Components, presentation, etc.
  • Customer characteristics: Gender, age, etc.
  • Customer surveys: Tastes, preferences, etc.

Once the company has designed the predictive model, it can search for those attributes that fit consumer tastes.

An example is to model wine quality based on physicochemical tests (e.g., pH values). Here, the output is based on sensory data, for instance, evaluations by wine experts.

5. Risk assessment

Risk assement picture

Risk assessment allows companies to analyze possible problems associated with a given business.

Predictive analytics builds decision support systems that can estimate which operations are profitable for the company and which are not.

Risk assessment is a general term that means different things to different users. Indeed, we might want to evaluate the risk of customers, companies, etc.

In the case of a client, the risk assessment can analyze the following types of data:

  • Socio-demographic factors: Gender, age, education, marital status, etc.
  • Product details: Credit amount, bill statement, etc.
  • Customer behavior: Repayment status, previous payment, etc.
  • Risk metrics: Default, etc.

An example in the banking sector is determining which customers will take over a credit. Here, we use different types of information to select if an applicant is suitable to receive credit. More specifically, we assess the probability that a given customer does not pay a loan, mitigating the impact of default risk.

6. Financial modeling

financial graphs

Financial modeling translates a set of hypotheses about market behavior or agents into numerical predictions.

These predictive models support firms in decision-making processes about investments or returns.

An example is to predict the stock market trend from internal and external variables.


Machine learning doesn’t have only business applications. Many industries use predictive analytics to improve their results and anticipate future events to act accordingly.

You can find successful applications in retail, banking, insurance, telecommunications, energy, etc.

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