How predictive analytics helps business?

Predictive analytics is the use of data using multiple tools and technology such as statical analytics, business analytics and machine learning to assess the probability of future based on the results obtained from the past data. These models use observed and calculated results to develop algorithms that predict the future from the past results. These data are displayed using tables, graphs, numerical values that are likely to be understood by the company employee and it displays the results about the customer preference and choice on company website such as clicking an ad, making a purchase, adding products in the cart, thus informing the company about the customer preferences. It is the discovery and communication of optimised data that result in better growth and performance of the business. Predictive analytics provides a company with highly precise forecast allowing two or more important decisions making and better investment in different fields.

The Business importance of Predictive Analytics

1. Maximisation on Return of Investment (ROI)

Predictive analytics is widely used by the companies to increase the return on investment (ROI) through various schemes such as strategic marketing, reduced risk assessment, optimised operational costs, predicting better results and advanced technical decisions. By using predictive analytics, a company can easily access the present stage of their business, improve their marketing and operational strategy and compete more effectively by gaining customer relations.

By assessing the predicted outcomes of the company’s future events and using that information for their growth, expansion and performance as a whole, predictive analytics is the best tool for forecasting the future of the company. In fact, predictive analytics can help companies to minimise the risk and increase overall revenue and profit across all the sectors of the business.

Increasing the number of optimised business decisions will ultimately lead to better business performance of the company. Informed decision backup help to increase the probability of making better decisions for the business organisation. This further increases the chances of deploying predictive analytics models within the organisation for multiple purposes. Hence, deploying predictive analytics model helps to make better decisions on the basis of accurate information.

The more and better use of these predictive analytics tools deployed and used within the organisation, the contribution towards the team and overall organisation success increases. Successful predictive models help the organisation to make a better and longer return on investment.

2. Predicting Customer Behaviour

Knowing your customer (KYC) is the biggest challenge in every marketing field. The better you understand the requirements of your customer, the better you deploy the tools and technology to reach to them via multiple channels with the right interaction of time. Using predictive technology tools, developed by advance Machine Learning and Artificial Intelligence, predictive modelling helps the employee to possess customer demand by identifying patterns and structures in the data. For example- regression analysis can be used to calculate the probability of future purchases. Predictive models also help to identify the dissatisfied customers whom the organisation are facing larger chances of losing them.

In this competitive era, everything depends on customer satisfaction and businesses are working hard to attract customers by giving different rewards, redeemed points and reducing customer problems. Thus, a small increase in customer retention would increase the business revenue about two to three times. According to one of the survey, it concluded that a 15 per cent increase in customer retention would increase company profit to two to three folds. Proper application and better use of predictive analytics can help to achieve more customer retention strategy and better customer experience.

By proper examination of customer past demand and usage, service performance and other activities, predictive analytics can predict the needs and demands of the customer. Predictive analytics can also predict what is right and what is wrong for the customer so that the organisation can take the necessary steps to increase customer activity.

3. Lead Generation

Good quality of leads is to required efforts and cost of operation. Unqualified leads lead to unnecessary spendings and loss of time which causes many delays or the organisation. With the help of predictive analytics, data analyst professionals can avoid such issues and focus on marketing efforts to promote leads. By using advance algorithms, the company can optimise leads to increase the sales and revenue.

Furthermost, predictive analytics can help sales and operation team to focus and concentrate on the promising customer by exploring how to show the maximum efforts and providing their sales teams with valuable data about what should be their next target and which prospect will most likely be helpful to close the deal. Also, predictive analytics provide guidelines on how to use the available resources currently available in the company without making any expenses. predictive analytics also forecast a high-value customer with the highest probability of spending and buying with the available resources.

4. Minimise the churn

Churn is the process of the customer selecting other companies over a particular entity and this way they stop doing business with an entity. Every year it is estimated that churn rates increase at the rate of 30-40 per cent. This effect is widely visible in global markets, small business etc. Thus preventing churns has become a top priority of any business. As the competition increases and the market becomes saturated, unhappy customer withdraws themselves from the companies. This practice is becoming more and more common and currently, it is largely impacting many of the businesses.

In order to overcome the high volume of churns, increasing optimises tools and technology are being assigned to the organisation to analyse why customer churn and which customer is planning to churn from the current situation. Such information will widely help to monitor the customer base and predict the possibility of a customer converting to churn.

One of the best tools widely used by the data analyst to predict the customer converting into churn is IBM SPSS. SPSS is the advance tool which works on the concept of data mining, predictive analytics and statical mathematics, which is recognised as the best tool to predict the churn and integrated data mining technology. By applying various concepts and segments of algorithms, you can easily predict the unusual pattern in your data. Once the main defect is found customers converting to churns are arranged in the sequence of their probability to leave the business.?And then the company can easily identify the loyal customers and churn customer.

5. Improve transaction

?Predictive analytics allow users to analyse and optimise the problems attached to the business. The aim of predictive analytics is to provide a strong support system that can easily predict the next risk possibility and provide a better prediction about the good and bad plans on the company.

The best example can be seen in the financial sector where a large number of the customer will take credit cards and most likely to make default. Using the application of predictive analytics, the company can predict whether the individual is applicable to purchase a credit card. More specifically, predictive analytics uses statistics and probability to predict if the customer can pay his loan and reduce defaults. Predictive analytics also uses different models to study data and customer behaviour based on their personal details.