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Precision-targeted marketing through big data enabled machine learning

Precision-targeted marketing through big data enabled machine learning

In today’s era of information-based competition, banking
executives are increasingly looking towards big data to maintain their
competitive edge.

In the retail banking area, there has been weak recovery
following the 2008 financial crisis. General market growth has been modest,
while the low interest-rate environment and new online market entrants have
further compressed margins. It was always important to retain and expand existing
customer relationships. But now it is critical.

Just when getting to know your customers better has become
an imperative, face-to-face customer interaction via traditional branch networks
is going down, as customers migrate to online banking.  

Hyper Personalised

Big Data analytics can help by enabling “Hyper Personalised
Experience” at an unprecedented scale. Building on the investment in CRM over
the last decade, newer technologies are available today to capture, analyse,
and scale customer information, and predict cross-selling opportunities for
banking products.

For this, the customer information footprint has to expanded
from across internal transaction systems (i.e. “360 degree” view) to include
data from the web, mobile devices, chat and messaging services, and social
media to provide a “720 degree” view of the customer. Though the full “720
degree” view may still be a while away, incremental benefits can be obtained
from increasing the depth of information available about the customer.

The goal is to determine the best possible product/pricing
combinations to offer to customers, through the right channels, at the right time,
so as to optimise both the value provided to the customer and the value of the
relationship to the bank.

Analysing ever
increasing data volumes

New York headquartered Novantas
is a leading provider of analytic advisory services and technology solutions
for financial institutions, including banks, brokerages and Fintech companies.
It provides information, analyses, and automated solutions that improve revenue
generation — across pricing, product development, treasury and risk management,
distribution, marketing, and sales management.

In the deposits space alone, it helps optimise returns for
over US$3.2
trillion of deposits
on its client’s balance sheets. Novantas focuses on
“customer science” and helps clients develop a deeper understanding of their
customer needs and behaviours. It helps translate those insights into rapid and
sustainable improvement in revenue, growth, credit quality, and profitability
with proprietary tools, analytics, technology solutions, and scoring models.

Hank Israel, director of Marketing, Propositions, and
Products at Novantas, said, “We help our clients solve practical, pragmatic
business problems, such as identifying pricing and prospecting opportunities
that can improve customer acquisition. As the market evolved, we had to expand
the customer attributes we use and leverage AI [artificial intelligence] to
find ways to bring raw data together and derive new insights.”

However, to take its analytics to the next level, Novantas
had to modernise its data platform. Traditional systems could no longer be used
with the increased volumes of data coming in. They also needed to process and
analyse a greater variety of data, such as audio from call center recordings
and unstructured text in payments transactions data. For example, by using
natural language processing (NLP) to analyse call center recordings, Novantas could
gain insights into customer sentiment on products and promotions.

There were requirements for real-time or near real-time
analyses and also, simultaneous evaluation of many periods.

Building a
self-service customer journey analytics solution

To address these issues, Novantas built a self-service
customer journey analytics solution, called MetricScape, on a
Cloudera-based Hadoop and Spark cluster.  

The platform integrates customer accounts and transactions
data from more than 30 institutions with third party data, and applies machine
models to operationalise customer scores, such as customer
potential value (CPV), for a variety of use cases, including offer optimisation,
customer retention targeting, and cross-sell and upsell activities.

More than 1,000 business metrics per customer can be analysed
with sub-second response time. The system can look at five years of data for
six million customers and obtain insights within minutes.   

Novantas deployed Cloudera on both Amazon
Web Services
 and on-premise, to cost-effectively manage variable
workloads as required. The organisation was able to monetise its new solution
in only six months. This was 18 months earlier than originally projected.

MetricScape acts as a librarian for customer and financial
data metrics, providing a business metadata governance layer that tracks things
such as data lineage, definitions and dependencies. Its analytical workflow
helped a team of data scientists pull together relevant data sets for analysis.
It allows banks, in general, to look at customer account histories, the results
of previous marketing campaigns and other data to segment millions of bank
customers based on their likely responsiveness to planned promotional offers.

Analytical workflow/ Source: Novantas
analyst case study on

While all machine learning models and metrics are ultimately
stored in Novantas’ MetricScape solution, IT staff wanted to give their data
scientists the flexibility to choose the tools they used to create the models.

So data scientists at Novantas use Cloudera
Data Science Workbench
 as their core development environment. This
enables them to use whatever language they want to develop metrics and models
and plug in their own libraries into this secure environment. Once the models
are production-ready, they can then easily move them into MetricScape for use
by clients.

More precise
deployment of initiatives

The MetricScape solution provides a new level of
precision-targeted pricing, to reach different groups of customers, such as
those for whom price will drive incremental volume, and also those for whom the
persistence of the deposit and/or utilisation of the credit will yield positive
economics over the estimated lifetime of the balance (including the incremental
cost of the incentive).

The solution has provided new opportunities for customer
acquisition, through digitally refined targeting to find prospects with similar
profiles. This has reduced the reliance on broad campaigns that erode value by
paying incentives where they are either not required or where the customer’s
behaviour will likely not deliver the intended economics.

Having identified target populations based on the
appropriate offer (price or otherwise) for balance consolidation, the system’s
machine learning capabilities equip the bank to rapidly test multiple
dimensions of offers (price level, channel, message, conditions) against target
populations, not only to optimise who should get an offer, but also to pinpoint
which type of offer is the most economic over time.

At one major US bank with 10 million+ customers where this
solution has been implemented, marketing execution costs were reduced by 50% by
focusing on high potential customers. Focus on incremental accounts went down,
lowering promo expense by as much as 60% and 12-month deposit growth by less
than 25%. Reduced requirement for retention offers reduced promotion expense by
10% and balance retention by only 3% with nominal change in customer retention.

The Novantas solution has thus enabled banks to make their
deposit business more profitable by leveraging big data and analytics
technologies and techniques. Their models combined with the Cloudera Hadoop and
Spark’s ability to store and process massive amounts of information at a low cost,
have enabled banks to granularly target customers based on their responsiveness
to price and the likely retained value from that offer.

These capabilities open up new opportunities for banks and might
prove critical in an increasingly competitive environment.

All content from customer
success story
 and case


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