What levers do insurers have to detect Mass Affluents customers?

What levers do insurers have to detect Mass Affluents customers?


This article follows on from a series of publications on wealthy clients. To learn more about the profile of the Mass Affluent, you can read our article “Banque Assurance: conquering Mass Affluent customers”. Regarding younger clients with high potential, you can consult our second article “Bank Insurance: invest now in the Neo-Mass Affluent”.

Different from one financial institution to another, the definition they use to qualify a Mass Affluent customer depends in part on the data they can rely on. While banks have a great deal of information directly related to the wealth potential of their clients (income, expenses, savings capacity, mortgage, etc.), insurers have more limited visibility. When insurers have less customer data and a partial view of their financial assets, what levers can they use to detect Mass Affluent customers?

Transform by adopting a customer-centric approach

To compete with the banks, the first leverage in the hands of insurers is to adopt a customer-centric approach and not product-oriented, in order to improve their customer knowledge.

In order to support the client in his investment projects, the advisor must therefore adopt a sales process built on the discovery of needs and projects of the latter, resulting, for example, in the boosting of savings, the transmission of financial assets, preparation for retirement or even the optimization of taxation.

This customer-centric approach – which must be carried out in a systematic is a prerequisite to have a 360 ° view of the customer and be able to offer him an answer adapted to his needs. It makes it possible to legitimately capture key information from the customer as soon as he sees an interest in it, making it possible to identify his potential. For example, in a process of supporting the client to build his investment strategy, knowing the extent of financial assets is part of the diagnosis that the advisor must carry out in order to provide personalized recommendations.

Finally, customer surveys carried out internally or by specialized partners also make it possible to retrieve valuable data on customer profiles (CSP, dependent children, ISF / IFI, etc.).

Use contract data to identify Mass Affluent clients in the portfolio

The main analytical bias for insurers is to have only part of the asset data, wealthy customers generally being multi-banked. However, the data at their disposal is sufficient to carry out a scoring of the probability of belonging to the Mass Affluent segment, according to 3 axes of analysis.

Finely valuing the customer’s stock of savings contracts

The nature of the stock of customer credit notes establishes a first measure of its value. Three criteria are fundamental to study : the amount of the outstanding amount, the nature of the contracts and the breakdown of the contracts (€ / UA):

  • The amount of outstanding is the criterion generally used to identify a Mass Affluent. If the outstanding amount exceeds the threshold set by the Insurer, the customer is considered as such.
  • The nature of the contracts: certain SCPI, PER or life insurance contracts with a high entry ticket meet customer needs a priori well-off. They are thus to be managed in a specific way.
  • Distribution of contracts (UA / €) also gives an overview of the level of risk the client wishes to take. The higher the UA rate, the more ambitious the client is in developing its outstandings, and the more likely it is to be part of the Mass Affluent segment.

With these 3 criteria, the stock analysis allows a first estimate of the probability for a customer to belong to the Mass Affluent segment – but supposes a prior financial commitment with the Insurer. In addition to this analysis, other information can be studied: financial behavior in terms of flows.

Valuing financial behavior through an analysis of savings contract flows

Flow analysis is measured over a sufficiently long period of time to identify trends in customer behavior. It will be a question of leading cohort analyzes, over 3, 5 or 10 years for example, in order to study the value at two different times, based on two types of payments: free payments and scheduled payments.

Free payments correspond to one-off payments made by customers. By analysing variations in the amounts or frequencies of payments, customer lessons can be learned: repatriation of assets to competitors, gradual increase in savings capacity, etc.

Scheduled (PV) or recurring payments correspond to payments made automatically according to a frequency defined by the customer. Projecting the amount paid periodically over several years therefore makes it possible to identify potential Mass Affluent customers.

Example: a customer who pays 1000 € / month of PV on his Savings contract has a very high probability of being a Mass Affluent customer, even though his stock would be low to date.

During quantitative studies carried out by VERTONE, the value of the flows appeared to be richer than the analysis of stocks to detect the Mass Affluent, but also the future Mass Affluent in the portfolio.

Rely on data from clients’ non-savings contracts

Finally, additional indicators can refine a first level of scoring resulting from savings contracts, and / or enrich it in the case of clients who share little information on their financial situation.

Like the banks concerning the domiciliation of income, the Insurers have rich data on the perimeter of property insurance, in particular auto and home. Thus, for insurers with non-life insurance products (auto, home, etc.), extracting certain data from these contracts constitutes a strong potential for detecting Mass Affluent customers: geolocation of the insured home, the living area compared to the price per m2, but also the type and number of vehicles insured are all indicators to consider in order to detect potential wealthy customers.

Example: a customer insuring a 200m apartment2 in the heart of Paris but having only € 5K in savings placed with its insurer, it is highly likely that it will be Mass Affluent and hold a large part of its assets in competition.

Building a scoring taking into account these three major levers will make it possible to create a customer segmentation adapted to this type of profile, and to dynamically target the Mass Affluent in the future.

Innovate by offering digital solutions that increase customer knowledge

In a market where insurance and savings contracts tend to standardize, innovations are now focused on services that bring value to customers, like the development of mobility and self-care, the personalization of uses and the facilitation of daily life.

The banking aggregation solutions respond to these challenges, and make it possible, thanks to valuable services and subject to the client’s agreement, to retrieve a certain amount of information to which only banks normally have access. It’s a true virtuous circle for insurers who access banking data, thanks to a digital and relevant value proposition for customers.

For example, Groupama has developed the HUG fund to meet the support needs of French people in preparing for retirement. This customer-centric approach fulfills a key promise: saving for retirement effortlessly. Indeed, HUG allows the customer to benefit from cash back on daily purchases in the form of a pot, through a banking aggregator giving a complete view of the accounts : the value is twofold for the client, who painlessly saves for his retirement and can have a consolidated view of his savings. At the same time, the insurer is refining its knowledge of the customer, in compliance with the regulations in force and with the agreement of the customer. The whole stake is therefore to offer solutions of value to the customer so that he is convinced of the value of the solution … and is ready to share his data.

So the development of digital solutions will in the coming years considerably increase the customer data to which insurers will have access. To take advantage of them, they must now innovate and develop new tools to understand this issue and be able to process this hitherto inaccessible customer data.

Conclusion

To respond to the challenge facing insurers in detecting Mass Affluent customers in their portfolio, a customer knowledge marketing strategy needs to be put in place. This strategy can be broken down into 3 key stages: transforming and adopting a customer-centric approach, identifying and applying the criteria to detect potential Mass Affluent, and proposing innovative digital solutions to improve customer knowledge. So many keys for an Insurer to take control of land predominantly preempted by the Banks.

VERTONE, a strategy and marketing consulting firm, has developed a great deal of expertise on the subject, and can support you both in your segmentation work and in identifying criteria for Mass Affluent customers, but also in the development of solutions. innovative digital technologies.

An article written by Laura Forner and Martin Blondel

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