Data-driven needs analysis: the next step of financial advisory

You know when you happen to look at a sofa on the Ikea website and then for weeks you have to listen to sofa and furniture banners every time you do a Google search or open Facebook?

These are the giants of the web that try to understand your needs, using the data you provide by browsing to frame you as potential customers of a multitude of products and services. In the era of big data in fact, “Know your customer” has become the mantra of every marketing professional.

In this context, the financial world represents a unique case. On the one hand, customers, with a generally mediocre financial culture, are often not aware of their financial needs, which must first be brought to light. On the other hand, as it is highly regulated, the financial sector has a distinct advantage: regulations such as MiFID and IDD1 require clients by law to provide a range of information about themselves that other sectors can only dream of.


But how to make the most of this data?

The answer is the same MiFID regulation: “Know You Customer, Know Your Products”, that is: punctually identify customers’ needs and objectives and sell them only the products they need. In this, technology, aided by a bit of good data science, can offer an incredible help.

Virtual B’s process

The logic of our work is simple: we start from a database of “raw” data on the customer, coming mostly from the mandatory MiFID questionnaire, partly from the customer’s trivial personal data and history (for example, how and when he moved his positions, how much, when and with whom he interacts when he relates to the bank or insurance company) and partly from ISTAT data.

From this data we extract – with a massive work of cleaning, integration and intelligent synthesis – a series of crucial information to “catalogue” the client in question: age, city of residence, income situation, asset situation, any dependant family, any current financing, risk propensity, time horizon, profession, level of financial education, etc..

Once we have obtained the client’s profile, we feed our two algorithms with it, which work to combine their results.

1) Machine Learning

The algorithm based on Machine Learning observes the profiles of customers to whom a selected group of consultants, considered particularly reliable, has sold certain products. And it concludes that customers with a similar profile may have needs that are satisfied by those products.

2) Expert system based on Business Intelligence

The Business Intelligence algorithm is an expert system that incorporates professional best practice and it replicates it.

The combined use of these two algorithms brings out the financial needs of each individual client – or rather: it assigns each client a “probability” associated with different financial needs.

An example?

Client: Mario Rossi

Need for life insurance cover: 47%.

Need for supplementary pension provision: 82%.

Once in possession of this valuable information, it will be the consultant who will decide how to move – we are talking about tools that can support the work of professionals, not replace it: for example, the consultant could propose a product that responds to the need for supplementary pension only to clients who have a probability associated with that need greater than 60%. Or he could, by talking to the client, discover that a similar product that client has purchased from another intermediary. But, in any case, you focus on the customer, and you promote the relationship, as well as advice in the strict sense.

The important thing, however, is this: starting with standard products, the advisor manages to allocate them individually according to the client’s needs.


The “superpowers” of data don’t stop here

Yes, because the more the “client’s DNA” is deep and rich in information, the more customization possibilities there are – you can customize the service model (directing the experience more to the digital channel or more to the physical channel depending on the person in front of you), or the mode of communication (preferring, depending on the case, newsletters, videos, e-mails, different “tones of voice” in communication, and so on).

Behavioural Profiling, which we have already talked about in several posts3, fits perfectly in this vein: additional information on the client’s psychological profile and cultural background (to be retrieved in this case through special questionnaires, contests, gaming and so on) can come to help the consultant to suggest the best way to “break through” his mind – in other words: once the “obstacles” have been identified, it will be easier to overcome them.


Behavioral Profiling at work

Let’s think about a client with a strong need for social security and insurance coverage for his health, but with a decidedly limited financial culture that makes him convinced that he can deal with unforeseen negative events exclusively through the activation of social support from family and friends. Despite the clear need for insurance coverage, the social conditioning of which he is a victim (unaware) makes him short-sighted, preventing him from making rational and far-sighted assessments. But, if his psychological and cultural profile is known – thanks to Behavioural Profiling – the advisor is prepared to be confronted with a certain reticence and can devise the best way to make the client aware of the issue and make him “digest” the need to subscribe a pension fund or a temporary insurance in case of death.

In short, the areas of application are really wide, but the crucial point is always the same: if the advisor first manages to grasp the perceptions and rules used by clients, he can help them to focus on real needs and opportunities. And he can do this by speaking “their language”, therefore working, more than on the content of the message, on the way it is presented.


Virtual B’s solution

Virtual B has been working for years in the financial sector, in close contact with data and their analysis. Our experience has resulted in numerous solutions that generate value and solve problems for financial and insurance intermediaries.

To understand concretely how a data-driven needs analysis can create value for your business and your daily work, discover SideKYC® at the link below. SideKYC® is our advanced wealth management analytics software that increases productivity with profiling and scoring features.

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