How to preserve placement channels with data enrichment
These are tough times for everyone: the pandemic, our long-distance relationships, more competition between intermediaries, consolidation via acquisitions and mergers. Today, more than ever, there is a need to sharpen up tools in order to do more business with existing clients or attract new ones.
In this regard, an interesting business case is that of financial marketing customization and data enrichment. Good partners when it comes to investments decisions.
The usual situation for many intermediaries is to find in their own databases customers of whom they have only some limited personal and contact details. Usually, these customers only have basic products/services and no profiling surveys, neither MIFID nor IDD. Think of the millions of customers who only have an online account.
Being able to activate these customers, revitalizing them and providing them something that is really useful can be a good way to effectively up-sell and increase business volumes. This activity becomes essential for the client retention of those realities, such as investment and insurance product factories, that due to the intermediaries’ aggregation process suddenly find themselves without distributors: bank A used to place your products, but group B acquires bank A, places its products, and cuts the previous distribution contracts.
Poof, an entire placement channel is gone….
How do you activate those customers whom you know practically nothing?
With the data enrichment. Starting with a few pieces of data, such as age, residence place, gender, profession or educational qualification, it is possible to trace back to more useful information, such as investment objectives, the most appropriate insurance protection, financing needs, sensitivity to ESG issues, propensity to digital channel, real assets’ ownership, spending and saving capacity and more.
To enrich data, at Virtual B, we have consolidated various public and private databases with financial, economic and geographical information. This is not trivial: we need to use probabilistic data fusion techniques to be able to combine heterogeneous data from different sources. But once this is done, the desired information can be extracted from the available data.
It is, in short, the estimation of a conditional probability distribution using a specific “lookalike modelling” for the financial services world. So, they are estimates, in other words, data with an associated probability and not certainty, but… they are very reliable. In fact, the richer the starting data, the more precise the result, meaning the better the salient financial traits of the client will be identified.
With the same kind of starting information, completely different profiles can obviously be revealed.
Example #1: the widow of the Northern Italy
Imagine that our client is a woman about whom we know almost nothing: she is 62 years old, professes to be a housewife, is a widow, and she lives in a 40,000 inhabitants city, in a northern province.
These few pieces of information allow the lookalike model to classify her as belonging to a financial clientele’s specific segment: the segment of mature women, widowed, divorced or single, with a shallow financial culture, often with a considerable real estate, with little or no debt and with a good spending capacity. It turns out that this target persona has a clear preference for the service channel: they prefer the physical channel and do not like to use the intermediary’s website – at most they can phone the call center and talk to a person.
They have specific financial needs: the PIC income type of investment, with medium-low risk but able to produce periodic returns from the proceeds, preferably with capital protection and attention to ESG issues. They are also inclined to purchase insurance products that protect against the self-sufficiency loss risk and are quite sensitive to the issue of succession management.
Through targeted communication on these issues for which she is most sensitive, in a manner and with an appropriate “tone of voice”, it is likely that she will decide – after a more or less long nurturing period – to purchase new products and services. Which wouldn’t be bad at all, right?
Example #2: the 50-year-old manager
Let’s consider a fifty year old business executive with a family who lives in a northern metropolis, has a high propensity to use the digital channel and a medium-high level of financial and economic education, invests in products aimed at accumulating capital for his children and for protecting his family, is fairly sensitive to ESG issues (as long as the investment is not too penalized by them), he is well capitalized and has a loan for which he could consider a subrogation. He is inclined to purchase temporary insurance in case of death and an all-inclusive policy that protects home and family, including pets.
You can attract his curiosity and activate him by mainly exploiting digital channels, with a brilliant communication, directing him to that channel – if it exists.
As you can see, two customer photographs that are sufficiently complete to begin an effective commercial action.
Data enrichment does not require integration and is cost-effective.
The service is actually simple: an anonymous file with the initial data of a customer list is enriched with a set of additional information for each of them, ready to be used in the way you deem most appropriate.
Is it an exact science? No. But instead of activating promotion campaigns that only follow the beat of new product launches, you focus on people’s needs and what they perceive to be their focus points: thus, the probability of success is much higher. Dormant customers can be revitalized, and the personalized approach is a harbinger of greater customer loyalty.
Obviously, data enrichment can be used in the lead generation phase: instead of shooting (basically) in the crowd, or on relatively large clusters (as intermediaries often do in promoting their products and services) it is possible to be much more precise, with lower acquisition costs.
In short, data enrichment allows an excellent balancing of the TCQ triangle – Time, Cost, Quality: in a short time and with low costs you can get reasonably accurate information on the customers’ financial traits otherwise abandoned to themselves, revitalizing the relationship and putting them in a position to do business.
Virtual B’s solution
Virtual B has been working for years in the financial sector, in close contact with data and its analysis. Our experience has produced numerous solutions that generate value and solve problems for financial and insurance intermediaries.
If you are curious about this topic, contact us for a demo at the link below and discover how to apply the Data Enrichment logics to your business processes.