HYPER PERSONALISATION:

AUTO-ADAPTIVE CUSTOMER BEHAVIOUR PROFILING MODELS

The Possibilities Are Endless

Current situation and trends

 

Banks and financial institutions from all around the world want to offer better and more personalised services to their clients. In doing so, considerable budgets are allocated to create business intelligence areas, while investing in teams and technologies. Historical data is vastly used to create valuable insights and business views; however, the commonly used methods create a static, one-dimensional vision of the company and of customer needs. In addition, companies’ reports are often of a great volume, full of details and hard to distill by the time-deprived executives who are responsible to satisfy continuously rising customer demands and expectations.

 

Digital transformation is the main current trend, which brings an opportunity to organisations to enhance efficiency and stay relevant in a highly competitive environment with modern digital newcomers. Maximising the usage of transactional data leads to identifying gaps and hidden opportunities to improve efficiency: be it discovering a new market segment or a need in an additional product in a short cycle dealing with real-time data analysis.

 

The problem

 

Financial institutions try to introduce automated solutions to improve customer experience while serving their needs. However, those solutions are not customer-centric on individual level and commonly based on untested assumptions which don’t fit the reality. Instead of focusing on data-driven individualised understanding of clients, most of the current technologies create persona-driven categories and put clients into those pre-defined boxes whilst ignoring existing mismatches and inconsistencies. Investment banking is one of the many examples, where instead of building an automated solution to understand their clients individually and offer tailored diversified portfolio for each client, banks try to put clients into a pre-defined risk that matches a pre-defined portfolio, or they offer the freedom to their clients to shape the portfolio by themselves which scares away the majority of clients without financial background.

 

Advantages of solving the problem

 

Understanding the customer behaviour on individual level and detect changes in the behaviour due to changing circumstances in real time is the first and the most important basic action for any company. Every organisation is trying to improve decision-making process and automate actions based on customer data to choose and apply the most efficient initiatives while not increasing the associated costs for increasing human resources. Bringing this solution is offering to close the gap between data interpretation on a deep level via recognising individuals in automated way, create stronger relationships with existing customers or acquiring new ones, and to outline accurate insights for further steps in a very dynamic environment.

 

What we propose

 

London Analytics develops advanced analytical self-learning models which can understand the customer behaviour for financial institutions on a deep level, but with a simple way to demonstrate the results. The flexibility and precision of this model allows to recognise customer behaviour patterns and it’s changes in real time, so that this knowledge can boost the results of the entire ecosystem related to making decisions about customers behaviour, e.g. marketing product lines. London Analytics offer to enhance data processing and analysis with advanced ML and AI technologies in real time and to assist in integration the solution into the organisation. We achieve hyper personalisation, or clusters of one client. 

 

How we Do it

 

To achieve superior results, we use historical and real-time on-line transactional data coming from current accounts, credit cards, loans, investments and from the offers, which were presented and accepted/declined by customers. We offer our services on the premises of organisations or remotely, working with anonymized data in order to provide additional protection for our clients. Any transactional data is not stored at London Analytics. We have differentiated technologies to support the investing market and the financial decision platforms. We are building profiling algorithms, with AI components, that can learn and interpret customers’ behaviour based on their on-line transactional data in real-time, resulting in scoring models, along with reasoning models.

 

Why us: We are Unique

 

Our approach in developing technologies is unique, which makes our models being suitable to serve any bank or financial institution worldwide. It goes beyond the currently used theory of ‘personas’ to define investments strategies, which is not optimised and needs to be manually built and updated at high costs for each country individually based on cultural environment and mentality. Our technology will feed any future offer engine in a very innovative way that currently doesn’t exist in banks and is not described in the investing literature.

 London Analytics Ltd.

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