7 ways predictive analytics can improve customer experience

Data analytics is increasingly being adopted by small and large business to improve the efficiency of their working as well as to improve the experience that their customers have with their brand. Data analytics has been proven to improve the relationship of the customers with the brand and also to increase the loyalty of the customer.

The use of predictive analytics presents a win-win situation for both the enterprises as well as the customer gets a better experience and this in turn leads to the business having better revenues.

Here we have a look at the 7 major ways in which predictive analytics can improve the customers experience:

1.The personalised experience:

Data analytics help in providing the customers with personalised experiences as per their interests. This in a way means that you provide customers with products, services or advertisements that will cater to their personality and choices rather than to the audience as a whole. With the increasing amount of data that is being made available through social media, it is becoming easier to predict the customers choices and provide them with personalised experiences.

2.Reducing the customer fallout:

One of the highest revenue generators for any businesses are the customers who are loyal to the brand and thus have a long-term relationship with the business. Predictive analytics can help to increase the number of customers that become these types of long-term clients this can be done by taking decisions that will prevent the customers from looking out for the competitors.

3.Optimised use of resources:

This is a major benefit that use of analytics brings in to a business. The main goals of business are to improve the revenue that they generate and this can be done by optimum use of their resource. Predictive analytics can help manage the resources in a much better way based on the data that they obtain from the customers. Consider an example of a particular style of clothing item which is being sold to customers, by making the use of predictive analysis it is possible to decide on the showroom locations where this item shall be most liked by the customers. This also means that the resources of time and money are best utilised by predicting the chances of some events occurrences in the future.

4.Better data for decision making

The insights that are obtained using predictive analytics help the management and the employees of an enterprise to make better decisions. When the management is equipped with more insights and also better information on the choices that they have, it can take better decisions.

5.Shipping

Analysis helps in improving the shipping experience as a whole because it helps the business take steps to ensure that the delivery that the customer expects can be done on its specified time. This can be done by predicting the optimal transportation issues and also by forecasting the issues that could arise in the future and thus could be mitigated.
6.Demand forecasting
Predictive analytics helps in predicting the choices that the customers are likely to make, much well in advance and also accurate to a very large extent. This data helps the organisations in providing the customers with much better service.

7.Real time feedback

The real time feedbacks of customers can be taken and analysed to instantly improve the quality of the services of the organisation. This means that the customers can help the enterprise provide them with what they require in much shorter spans of time.
Revenue generation and customer satisfaction are the two main goals of any business, with the help of predictive analytics both of these goals can be achieved and even improved upon.

BI Consultant

About the Author

BI Consultant

DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.

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