Get help now

Customer Relationship Management (Crm) Strategy for Banking



  • Pages 5
  • Words 1190
  • Views 494
  • Can’t find relevant credible information

    Let our experts help you

    Get help now

    Promotion Individual Assignment

    As the product manager, I propose to employ the data mining techniques, as an important implementation of our Customer Relationship Management (CRM) strategy, to better understand the clients of our third party products and increase our profitability. Our bank has various sorts of third party products ranging from mutual funds, insurance products to bonds. The commission is earned on selling other companies’ products.

    Although the fee amounts are small, they are a valuable contribution to diversifying revenue streams, increasing the mix of non-interest income and also improve profits. However, it is a competitive market because almost all banks and other financial institutions are increasingly turning to these Non-Fund Income products. As a result, the customer relationship management strategy and the data mining tactic are highly necessary to be imbedded.

    Actually in the USA and Europe, CRM strategy has been implemented among larger companies in financial services and has achieved significant business success. Taking the Spanish Insurer AXA Seguros e Inversiones(AXA) as an example, it is a member of the global giant The AXA Group, with revenues of over €1. 8 billion and two million customers. The company once wanted to better understanding its customers in order to be able to make more personalized offers and implement customer loyalty campaigns.

    So AXA used data mining solution to build a predictive policy cancellation model. The solution thus created profiles and predictive models from customer data which enables more finely targeted campaign management, call center management, sales-force automation and so forth. The model was applied to current and cancelled policies in various offices, and the result was that auto insurance policy cancellation rate was cut by up to 9% than before. The same discipline should be embedded to our NFI products.

    By data mining the clients who have purchased the products in the last two years, our bank can first of all have better and further insight of the customer portfolio and thus better identify and acquire new customer. Through cross-analyze the data of financial data (income level, payment history, risk level) with the demography data (age, gender, occupation), data mining technique can provide essential information such as: which the preferred products of different customer segments, what are the characteristics of these customer clusters respectively, and who are the most profitable and valuable clients. Therefore, the salesperson with this information can identify the potential customers precisely and timely. Also since our bank have multiple transactional channels including branches, ebanking, telebanking, direct sales, some customer segments may be unique to or prefer to a single channels which can be unveiled by the data. Special sales force, accordingly, may be allocated to those specific channels.

    What’s more, the intelligent interrogation of data allows marketing department staff to use these customer-related data in order to develop and execute targeted communication. While a mega advertising campaign may create noise among the audience, the message is not necessary reach our target customers. By nature, financial product is high-involvement product that customer spends time to learn and compare alternatives. Adverting campaign, limited by the budgets, time and space and media, is not powerful to persuade people.

    Rather, basing on the previous survey, people tend to consult their professional friends or families or do some research on the internet when they are interested in financing. Therefore the relationship marketing management for retention of customers is essential to create a word of mouth (WOM) effect. To retain and build relationship with the past customers, database can firstly tell what the customers’ needs and expectations exactly. People’s financing purposes are varying: make a promising future of their children, have a better livelihoods, make a big fortune, or simple keep money inflation-protected.

    It is very difficult to build long-term relationship with customers if their needs are not understood and well met. An aggressive client expecting high yield in a short time, for example, cannot be satisfied with a long-term bond product. Hence, we can show our commitment to the relationship via understanding the fundamental expectation of the customers, then satisfying and delighting the customer in a consistent way. Commitment builds trust. Trust begets relationship longevity.

    This insight into customers can also improve our bank cross-selling performance. The salesperson can suggest several relevant products to the customer to achieve their need well, even the loan department or the private banking department could be benefit as well. In the process, product designing department can cooperate with the product suppliers, to help design and the suitable product package from the third parties. Secondly, we can study the pattern of customers——life cycle, career development, even children growth, to predict their future needs.

    Providing financing advice and product at the right time, advancing our competitor, can show our concern to the customers, thus wining their trust. Thirdly, data mining technology help front-tier staff to deal with event-based marketing issue. For instance, a call to account manager, enquiring about the current yield rate of a fund product, can be taken as indication that the customer is comparing alternatives and may switch to different bank. This simple event may trigger the manager to provide an offer designed to retain the customers.

    The intelligent technology can also inform account managers the birthday or important anniversary date of the important customers, so that personal greeting is sent and customer feel treasured and delight. These above practices of customer relationship management to retain the customer generate two benefits: reduced marketing costs and better customer insight, which cannot be achieved by mage advertising campaign. Advertising campaign cost tremendous money, and it can take several years to recover from it. By contract, improving customer retention reduces our bank’s money, because fewer dollars need to be spent replacing lost customers.

    And as time goes by, as customer tenure lengthens, we better understand customer requirements and expectation, and customers also come to understand what we can do for them. Trust are built, risk and uncertainty are reduced. Consequently, customers commit more of their saving and assets to our banks. Also, because we develop deeper customer intimacy over time, we can enjoy better yields from the cross-selling effort. As it has been mentioned before, a good relationship with retained customers lead to word-of-month effects.

    A committed and satisfied customer is more like to utter positive WOM and influence others people. This WOM influence is more effective than advertising, become people tend to trust their friends and family with real purchasing experience. All these conditions mean that relationship marketing management for retention of customers is more profitable than advertising: a 5% increase in customer retention rate leads to an increase in the net present value of customers by at least 25% as it is estimated. In short, customer retention drives up customer lifetime value.

    To conclude, this database and selection method is more effective and worthwhile than an advertising campaign to increase our third party product value.


    1. Francis Buttle (2009).
    2. Customer relationship management : concepts and technologies(2nd Edition). Amsterdam, London. Butterworth-Heinemann.
    3. Fitzsimmons, J. A. , Fitzsimmons, M. J. (2008).
    4. Service Management: Operations, Strategy, Information Technology (6th Edition). New York: McGraw-Hill
    5. V. Kumar, Werner J. Reinartz. Kumar, V. Hoboken, N. J. (2006): Customer relationship management : a data based approach. Wiley

    Customer Relationship Management (Crm) Strategy for Banking. (2016, Aug 30). Retrieved from

    Hi, my name is Amy 👋

    In case you can't find a relevant example, our professional writers are ready to help you write a unique paper. Just talk to our smart assistant Amy and she'll connect you with the best match.

    Get help with your paper