6 business applications of predictive analytics

By Roberto Lopez, Artelnics.

Predictive analytics extracts information from data sets to discover relationships, recognize patterns, forecast trends, find associations, etc. This allows us to anticipate the future and make the right decisions.

The applications of predictive analytics 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.

1. Customer targeting

Customer targeting

Customer targeting is the practice of dividing a customer base into groups of individuals similar in specific ways relevant to marketing, such as age, gender, interests, and spending habits.

It enables companies to target tailored marketing messages accurately to customers who are most 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:

The advantages for the company here are (i) a better communication with the customer, (ii) a considerable saving of money in marketing, and (iii) a considerable increase in profitability.

An example here is related to the direct marketing campaigns of a banking institution. The goal is to predict which clients will subscribe to a term deposit.

2. Churn prevention

Churn prevention picture

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

The cost of retaining an existing customer is much lower than that of acquiring a new one. Therefore, this phenomenon can be very costly.

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:

The company then analyzes the causes of churn and takes the required actions to retain those customers. For instance, we can offer the customer 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 the prior history, seasonality, market-moving events, etc. to result in a realistic prediction of the demand for a product or service. It can be applied to short-term, medium-term, or long-term forecasting.

In this regard, predictive analytics can anticipate customer response and changing attitudes by looking at all factors.

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

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

An example is to predict power demand in the electric industry accurately. This study's results improve 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 the attrition rate.

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

Once the company has designed the predictive model, it can be exploited to search for those attributes fitting consumer taste.

An example is to model wine quality based on physicochemical tests (e.g., pH values), and the output is based on sensory data (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 aims to build 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 a customer, a company, etc.

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

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

6. Financial modeling

financial graphs

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

These predictive models are used for supporting firms in decision-making processes about investments or returns.

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

Conclusions

Predictive analytics can be used across many industries and are a great way to improve your results and anticipate future events to act accordingly.

Neural Designer is a data science and machine learning platform specialized in easily building predictive models.

This tool has been successfully applied in different industries such as commerce, banking, insurance, telecommunications or energy, to name a few.

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