Bankers from all around the globe are wondering how to secure a better engagement with their customers to retain their existing clients. Acquiring a new business is not enough to power business growth. Instead, it is more important to retain existing customers. Just like any other business, banks are finding it equally competitive. Hence, their focus is on retaining the customers that consolidate their business, taking them to the next level of success and achievement.
To downsize the customer churning, Text Analytics solutions can make a significant contribution. A recently conducted research accounted for the fact that extensive data analytics can help a business retain 75% of its existing clients. Likewise, it helps a business in optimizing its profit by more than 120%. Moreover, Text analytics helps a business precisely comprehend its customer’s needs and choices, eventually offering the most relevant solutions to their needs. Motivated by these outcomes, banks worldwide is emphasizing on text analytics. They are doing it intending to secure a permanent bonding with their customers.
An Overview of the Text Analytics Process
Before exploring how bankers can secure better customer retention through Text Analytics, it is essential to get a precise idea about the concept. It is an automated process for translating unstructured data in massive volumes in quantitative data to gain better insight, ongoing and emerging patterns, and business trends. This technique gets combined with Data Visualization tools to support businesses to evaluate the inside story beyond numbers, eventually helping in better decision making.
How Data Analytics contributes to better customer retention?
- You must create a Data Roadmap, and adhere to it
Around 30% of Banking professionals feel they are working in their companies in the absence of a precise strategy about data embedding and data analytics solutions. It is the point that demands the first and foremost consideration. A Bank must make the maximum utilization of their data. Secondly, the operational framework should be flexible enough to make the necessary changes, based on the data suggests. Hence, the first thing to do is to prepare a detailed Data Roadmap. Subsequently, it would help if you stuck to the
- As Text Analytics is all about better informed-decision making, it is crucial to identify the most critical data for a bank.
- The Data Road Map should have a clear direction about the category of data that a bank will emphasize, the possible ways to collect the data, the data processing approach, and the probable outcomes to derive.
- As the daily work process goes about producing tons of data, a bank needs to be very specific about the most crucial data.
- Once you prepare a clear strategy in this regard, the text analytics process gains more impetus, helping you comprehend your customers’ needs and choices with optimum efficiency.
- Most importantly, it would help if you stuck to the strategy you prepare to serve this objective.
- Banks must focus on the best quality leads
The percentage of Customer churning drops down significantly if you target your customers efficiently. Suppose a bank holds a comprehensive listing for its existing and prospective customers. In that case, they get to the position to focus on customers who are more likely to retain.
A suitable algorithm to compare the target customers’ characteristics and features makes it easier to identify the customers who are less likely to churn. At this point, Customer segmentation becomes the most crucial task.
- Each category of customers holds some exclusive features that aid in the easy identification of potential customers.
- Moreover, the comprehensive knowledge about the special train of these segments helps a bank plan activities that can drive better engagement between the brand and the customer.
- Eventually, Banks can retain the maximum of its customers, who, in turn, will keep bringing new clients for the bank from their respective contacts.
- Robust Text Analytics aids in better Machine Learning that helps in developing comprehensive predictive models
High customer retention is the reward for the most accurate foresightedness for the upcoming changes in customer behavior. To materialize these plans, Banks must develop accurate and robust predictive models that will enable them to foresee the possible changes and plan suitable solutions to match the changing demands.
- Comprehensive text analytics will prepare a bank for better machine learning that, in turn, facilitates accurate and efficient predictive framework.
- Consequently, banks can offer an instant solution to the changing patterns and orientations in customer behavior. It will enable them to secure better engagement with customers by offering more relevant solutions to their needs.
- The retrospective outcome is that their customers will not consider churning away from the bank and look for other service providers to find the most suitable answers to their needs. It is one of the critical reasons Text Analytics is becoming more mainstream across the Banking sector.
- To drive better Business insight
Accurate business insight is not something that happens by chance, or by the time of a good night. Instead, it has to be based on real-time and accurate facts and figures. It is where advanced analytics solutions can help Banks to consolidate their relationship with their customers. The purpose is to seamlessly monitor the customers’ approaches, orientations, and practices and plan the convenient services and solutions that will come relevant to changes in customer behavior.
- Text Analytics help Banks to identify the customers who will be the most beneficial to retain
Even all customers are essential to a bank, the fact is, not all customers come equally advantageous to retain. Some customer groups are more impactful and the potential to retain. Text Analytics is the most effective way to identify the customer segments who can prove to be the most potential bank. It helps banks to plan the appropriate measures to retain these customer segments. On the other hand, banks can identify customers who are more likely to churn-away. Consequently, banks can plan their strategies to reduce the churning of these customers.
