Winning the battle of bank customer retention by valuing the customer base

18 February 2016

Winning the battle of bank customer retention by valuing the customer base

During this period marked by declining revenue linked to historically low rates and by the emergence of new competition from non-banking players, banks must continue, if not accelerate, their digital transformation and strengthen their customer focus.

The retention battle depends on the ability to include the actions taken in a global strategy to develop the customer base, in the intelligent use of Big Data technologies and in the involvement of all of the bank’s players.

Retention: an essential lever in ensuring the sustainability of the bank’s activities

Consumers are now wooed by new entrants, powerful brands such as “GAFA” (Google, Apple, Facebook, Amazon) or start-ups specialising in financial services (“FinTechs”), who propose an alternative offer to those of the large banking networks, principally in terms of payment. Other activities are also targeted such as lending, with the development of crowdfunding platforms.

Banks still do not really consider these players to be a serious threat. It is true that the motivation of a giant such as Google is data capture, and that the first generation of “FinTechs” is not currently competing head-to-head with traditional banks, notably due to the regulatory barriers. Most of the major banks are implementing a great number of initiatives to integrate this innovative ecosystem: Crédit Agricole has started to host start-ups, Société Générale is involved in Linxo (Personal Finance Management application creator) and BNP Paribas is opening innovation hubs to support innovative companies. In fact, these new players are one step ahead in terms of customer relations and data exploitation.
Traditional banks therefore have every interest in taking the opportunity to align themselves with this competitive advantage to better cope with the unprecedented volatility of their customers, especially as there is one constant throughout the strategic objectives: to increase the value of the customer base.

It is well known that it is cheaper to retain an existing customer than it is to acquire new customers. Numerous studies, including the work carried out by Frederick Reichheld in collaboration with the Harvard Business School in 1996 entitled “Loyalty effect: the hidden force behind growth, profits and lasting value”, have established that the cost of retaining a customer is considerably less than the cost of acquiring a new customer. There is often talk of a ratio of at least 1 to 5. In this changing context, retention is more than ever a major lever in ensuring the sustainability of activities. However, all too often, it is still viewed as a defensive strategy, and the preserve of a few employees in the company.

At the same time, technological innovations offer real opportunities to improve customer knowledge and the way in which attrition can be prevented. The sharp rise in the use of digital channels makes it possible to develop interactivity, collaboration and to collect a greater wealth of information on customer use. The storage, processing and analysis of increasingly large volumes of data, made possible thanks to Big Data technologies, makes action plans more responsive and relevant, notably with the possibility to act in real time and to tailor the requests to individual requirements.

Deploying a retention strategy from the very beginning of the relationship

The objective of retention strategies is generally to keep customers at risk of leaving. However are banks interested in retaining all customers, including those who present a high risk of default or those who prove to be unprofitable after several years? Retention forms part of a more global strategy to increase the value of the customer base, with the following question put forward as a starting point: “which customers should be retained as a priority? “.

The first few months of the banking relationship are often the most sensitive; this is when the duration and intensity of the relationship is decided. The ability of the bank to come up to the mark during moments of truth is crucial. In fact, non detection or mismanagement of these situations are potential factors of disengagement on the part of customers.

A proactive strategy to develop the customer base relies on anticipating or identifying the drop in customer activity in order to put in place appropriate actions as early as possible. In fact, at the time the risk of attrition is detected, the customer is already often inactive, which considerably impairs the effectiveness of the retention actions. There is a strong case for preventing attrition from the very beginning of the relationship and at least up until the “break-even point” in order to make a return on the acquisition costs incurred. Retention must therefore be an ongoing concern throughout the life cycle of the banking relationship, with particular attention paid to the first years of the relationship, and then on key stages, whether these involve specific defining events in the customer’s life or business development opportunities for the bank.

The quality of onboarding, customer activation, and the collection and exploitation of information to support them optimally during moments of truth are determining factors to retain customers and turn them into profitable and sustainable assets.

Exploiting data: a mirage or a real opportunity to improve the value of the customer base

Today, technological innovations related to data open up a wide range of possibilities to improve customer knowledge and strengthen the relationship. All of the banks have embarked on experiments in order to make the most of Big Data technologies.

They are equipping themselves with the necessary tools, transforming their IS and developing skills in data analysis and Data Science. The question each bank should ask itself is: what meaning should be given to these initiatives to enhance the value of the customer base and ensure a ROI?

In order to enrich the customer relationship strategy, Big Data innovations must make data intelligent and operational. They must also make it possible to improve the definition of the basics of the strategy and its application.

This means producing processed and aggregate information, which relies on a higher volume and a greater variety of data, and on using signals (strong and weak) and context data (time, localisation etc.) that are easy to activate by the strategic and operational divisions.

Thanks to Big Data technologies and Data Science, complex processing can be carried out on data which is structured, unstructured, internal or external, or data taken from channels or transactions. The models created feed the customer strategy or its operational application, through the production of alerts, recommendations or predictions.

The development of Data Science, within banks and elsewhere, has brought artificial intelligence to the forefront through the term “Machine Learning”. “Machine Learning” depends on the power of computers to create auto-learning algorithms. It supplements traditional statistical approaches, well suited to describe and explain phenomena and which have enabled banks to have full control of the “banking” data related to their customers (signs, possession, movements, net banking income, etc.). Artificial intelligence opens up a more predictive dimension to customer knowledge and enriches “banking” data with behaviour-related data (consumption of products and services, Internet browsing, omni-channel experience, etc.) in order to make action plans more relevant.

Weak signals, paradoxical single and large events, can be detected more quickly. An array of causes explaining these phenomena can be defined and makes it possible to improve pro-activity. For example: a few customers who have been regularly using a service stop using it. Detection of this phenomenon can be explained by the poor functioning of the service, cannibalisation by another service, start of customer disengagement, etc.

In the context of valuing the customer base, all of this knowledge is used to define or optimise the foundations on which the strategy is based, namely:

  • Identifying the most profitable customers with the greatest potential by, among other things, modelling the Life Time Value or combining traditional segmentation with micro-segmentation recalculated in real time.
  • Defining the relationship typologies or links sought by the customer. The banking relationship on the one hand (main or fringe bank) and, on the other hand, the emotional relationship (transactional, partnership, confidant relationship, etc.) to adapt the channels, the frequency of contact and discussions.
  • Understanding and exploiting reasons for leaving, to improve the process.
  • Anticipating or detecting moments of truth for the customer, to offer tailored support.
  • Understanding what fosters customer engagement, to implement best practices.

This knowledge is used to support the operational services in ownership of the strategy by disseminating “Next Best Offers” and “Next Best Actions” to all channels and to back offices. It is used to customise the channels and offers.

Finally, the use of real-time makes it possible to capitalise on the opportunities to generate revenue and also to intervene immediately in the event of an emergency in order to establish itself as a reliable partner for the customer.

Retention has several dimensions involving the entire company

At the time of “Data driven” management, the strategy to value the customer base concerns both the executive management, the ISD and all of the Core businesses of the bank. To achieve its objectives, it involves transforming the organisation in order to promote the end of silos and agility. The clear identification of individual roles and the decision-making circuit is key in order to ensure the fluidity and consistency of actions within the chain of players involved.

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