Predictive analysis is one part of business intelligence that looks to learn from past behaviors to predict the likeliest behavior in the future. A typical example of a predictive model is seen in loan applications. Financial institutions assign credit scores using predictive analytics. The data from previous financial transactions are used to predict future transactions. People that don’t miss out on loan repayment will get higher scores because the model will indicate that they are likely to repay future loans.
In short words, predictive analytics uses information from a set of data to find associations, recognize patterns, discover relationships, forecast trends, etc. This makes it easier to make appropriate decisions in anticipation of what is to come in the future.
There are many ways that you can use predictive analytics in business. We describe some of these in this article.
Customer Targeting
Customer targeting refers to the breaking down of the customer base to smaller groups depending on the similarity between them relevant to marketing. This could be age, interest, spending habits, gender, etc. This way, companies can tailor their marketing messages in a more accurate and personalized manner to increase the customer’s likelihood of buying their products.
Predictive analytics has proven itself as being better than the traditional strategies for identifying potential customers.
Some of the factors that you can use for customer targeting are:
- Socio-demographic factors: this includes age, gender, education, marital status, job, etc.
- Engagement factors: this includes frequency, recency, monetary, etc.
- Past campaign factors: these include contact type, duration, month, day, etc.
Some of the advantages that companies stand to gain are:
- They can communicate with customers better.
- They save a lot more money on marketing.
- They have considerably higher profitability.
A typical example of this is direct marketing campaigns by banks. They have a goal of predicting the clients that will be willing to subscribe for a long term deposit.
Churn Prevention
The goal of churn prevention is to predict the customers that are likely to end their association with the business, why, and when it is expected to happen.
This phenomenon is a costly one because it is relatively cheap to retain an existing customer than when trying to acquire new customers.
With the big data set of customer info available to the company, they can develop a predictive model that allows them to act proactively to prevent the churn before they no longer can.
Some of the attributes that you can consider for churn prevention are:
- Socio-demographic variables include age, gender, education level, nationality, marital status, job category, etc.
- Products contracted: this includes insurance policies, credit cards, etc.
- Engagement variables: this includes frequency, recency, monetary, etc.
- Product/service usage: this includes web, mobile, call center, physical, etc.
- Technical incidents: an example is customer service calls.
- Stationary variables: this includes time, date, season, etc.
- Competitor variables: these include the quality of services, price, etc.
With these, the company can analyze the cause of the churn and take actions necessary to help them retain their customers. For example, they might offer an extra feature or a discount.
Sales Forecasting
Sales forecasting has to do with analyzing the previous seasonality, history, market-shaking events, etc. to get a realistic prediction for the demand for a service or product. You can use this for the long term, medium term, and short term forecasting.
This way, predictive analytics can anticipate the customers’ responses and changes in their attitudes by considering different factors.
Some of the variables that are necessary for sales forecasting are:
- Calendar data: this includes bank holidays, hour, season, etc.
- Weather data: these include rainfall, humidity, temperature, etc.
- Company data: this includes data from marketing campaigns, promotions, prices, etc.
- Social data: this includes the political and economic factors of the country.
- Demand data: an example is historical sales.
Sales forecasting is a significant part of all of the company’s plans. It helps the company predict the revenue so that they’re able to allocate resources optimally.
An example of this is the accurate prediction of power demand within the electric industry. As forecast accuracy gets better, this means that companies have better information which they can use to decide the best thing to do.
Quality Improvement
Companies can use market survey analysis to address customers’ requirements, reduce the attrition rate, and increase their profit margin.
Some of the factors that you can use for quality improvement are:
- Product characteristics: an example of this is a component presentation
- Customer characteristics: this includes age, gender, etc.
- Customer survey: this includes preferences, tastes, etc.
After designing a predictive model, the company can then use it to look for the attributes that fit consumer tastes.
An example of this is modeling the wine’s quality based on physicochemical tests such as pH values with sensory data being the basis for the output.
Risk Assessment
With risk assessment, companies can analyze the likely problems that a business has. According to Tobias Foster, who provides college-paper.org reviews in Australian Writings.
Predictive analytics has just one goal here, and it is in building decision support systems that can estimate the company’s profitable operations with some degree of certainty.
The term risk assessment might have different meanings to different people. It is possible to evaluate a customer or a company’s risk, and so on.
When it comes to the client, some of the data that the risk assessment might analyze are:
- Socio-demographic factors such as age, gender, marital status, education, etc.
- Product details such as bill statements, credit amounts, etc.
- Customer behavior such as previous payment, repayment status, etc.
This is used in banking for determining the customer that can take credit. They combine different information to select an adequate applicant to receive credit.
Conclusion
There are many industries in which predictive analytics works very well. It is a sure way to boost results while anticipating future events and taking the necessary steps.
Predictive analytics have been used in different businesses across different industries such as telecommunications, banking, commerce, energy, insurance, amongst many others.
