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.

Customer targeting using machine learning

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.
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Customer churn prediction using ML

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 very costly for companies.By harnessing the power of big customer data sets, companies can develop predictive models that enable proactive intervention.
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Sales forecasting using machine learning

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.
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Improve wine quality using machine learning

Analysis of market surveys helps companies address customer requirements, increasing their profit and reducing attrition. 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.
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Customer risk assessment using machine learning

Risk assessment helps companies identify and analyze potential problems in business operations. Predictive analytics builds decision support systems to estimate which operations are profitable and which are not.The term "risk assessment" can vary in meaning depending on the context. For example, in banking, it involves evaluating the risk of customers defaulting on loans. By analyzing various factors, the goal is to predict whether a customer is likely to repay a loan, thus mitigating the impact of default risk.
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Conclusions

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.

4 machine learning use cases in banking and insurance 6 machine learning examples in banking & insurance Customer risk assessment using machine learning 2 machine learning examples in finance