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Tools based on
Data-Driven Analytics are the crucial element characterising services that have revolutionised behaviour models and the use of content across the various online/digital channels for billions of users. The model encompassing the full use of content through digital channels is increasingly driven by algorithms and intelligent tools, which are designed to capture user expectations and transform them into elements of effect and user experience.
Netflix claims that 80% of content streamed on the platform is driven by personalised recommendation tools; for
Amazon, it is estimated that 35% of
e-commerce purchases are driven by automatic suggestions.
But if until now such services were the distinctive features of
Web players and constituted the main key to their success, today they can be developed and implemented, in an agile way and at very low costs, by practically anyone.
All this is now possible thanks to two enablers: on the one hand, the immediate availability of data combined with the computing power of
cluster-based architectures (Big Data) and on the other hand, by the adoption of libraries and computational models based on
advanced heuristics (Machine Learning).
This type of tool is now available as part of
open source solutions, which are easily and seamlessly integrated from a technological point of view.
With the aim of experimenting with an
Advanced Analytics approach, the
Data Analytics laboratory initiative launched by Banca Mediolanum involves a
partnership between the
Marketing Research team and Reply for the development of advanced data analysis mechanisms and the design of
proactive services, tailored to the customer’s needs.
Among the projects currently underway, an early success has been achieved in the development of a platform for the analysis and synthesis of
automated recommendations aimed at providing
personalised commercial offers on the bank’s product catalogue. The initiative facilitated the development of an experimental platform for the creation and evaluation of
Recommendation Engine based on data-driven services.
Recommendation Engine can be described as a mechanism that can summarise a personal recommendation in real time, based on the
behavioural assessment of a set of customers.
The principle of a
Engine is based on the concept of
Collaborative Filtering, in other words, the ability to filter
similar users within a large
customer base. The principle of
similarity can be modelled using
Machine Learning tools based on the specific
business context (Retail, as in the case of the big
e-commerce players, or
Financial Services, as in this case) and on the prompt analysis of
events observed in relation to individual customers (e.g. products purchased, browsing behaviour across the various online/digital channels, searches and actions requested from the
Banking Centre, etc.). On this basis, it is possible to calculate:
Similar customers: users who tend to make the same purchases;
items (products) that tend to be purchased by similar customers.
Collaborative Filtering Principle: missing
items are recommended with reference to the selections made by customers with a similar portfolio
business case implemented for Banca Mediolanum, the
Recommendation Engine works like an analytic filter on the customer base. The basic idea implies that, if you can
identify a number of users carrying out a certain sequence of purchases of the same products
, it becomes possible to anticipate that all customers who so far have shown similarities to that sequence of events, complete their portfolio with the missing products. A very intuitive concept, whose implementation, however, becomes very complex when faced with massive
data streams and relating to the event history of millions of users and thousands of catalogue products.
quantitative measurement of similarities leads to the immediate identification of a list of personalised recommendations, easily applicable to each customer in real time. According to the
Robotics for Customers methodology conceived by Reply, the use of a
Recommendation Engine can be perfectly harmonised as part of an organisation's processes and easily adopted as part of a service platform (e.g. through
web services or an API). From a
Marketing point of view, for example, three main uses can be mentioned. In particular, the service can be used to:
Figure 2 Uses of
Recommendation Engine services in
As an additional element of attention, it is important to note how, unlike
Retail scenarios in which products are typically well-defined and readable, in the
Financial Services sector the product catalogue offers a number of specific features related to the life cycle of products, the customers’ requirements and their current
situation (e.g. a customer needing a mortgage or insurance only in specific situations in his life).
The Recommendation Engine defined for Banca Mediolanum uses two different computational models, implemented according to two distinct sets of logic:
The implementation of the computational model facilitated the
development of a pilot case, which was evaluated using selected customer data observed at a given t0. For each of the selected customers at that date, the analysis was based on a
snapshot of the products in the portfolio and some
demographic data (e.g. age, commercial profile, asset class, etc.). Subsequently, a series of recommendation models based on
Machine Learning algorithms were tested on this customer base.
The solutions were configured with different parameter
settings in order to facilitate a comparison of the solutions and
experimentally verify the effectiveness of the results. The evaluation was obtained through a projection of the results, in other words
by comparing the answers provided by the models with the actual sales data, observed subsequent to t0.
The metrics considered with respect to a
benchmark recommendation showed a marked improvement in
Precision, up to +167%, and in
Recall, up to +974%
 , providing a strong stimulus for the implementation of these models into production.
In contrast to traditional approaches based on segmentation and profiling, which require a costly analytical study to assign each customer to a
Personas cluster, in this case it is the
data already available that drives the
Collaborative Filtering and, therefore, the selection process of the best offer.
dataset of primary events on which the
Recommendation Engine is based can in fact be obtained from sales data, typically extractable from a common
commercial data warehouse.
Two aspects worth noting are the ability to
take data that is readily available and make it actionable , together with the
ease of implementation of the process. The Banca Mediolanum pilot case was created in a totally unconnected manner with respect to the Bank's
Business Intelligence infrastructure and was developed as a
service-based solution on a
Precision refers to the number of purchases among the recommended products, divided by the number of recommended products.
Recall refers to the number of purchases among the recommended products, divided by the total number of purchases.