Banks have long pondered the untapped value of the commercial segment but
often lack the means to identify the precise needs of individual companies in
this large and diverse population. This is changing, however. By mining huge
reserves of customer data, banking analytics leaders are meeting the needs of
hundreds of thousands of commercial customers—from small businesses to
medium-size corporations—with new levels of convenience and cost efficiency.
Several banks have achieved a ten-fold increase in the success rate of product
recommendations, thus delivering highly relevant offers with clear economic
benefit. This article highlights recent examples of how “next-product-to-buy”
(NPtB) recommendation engines are identifying time-critical needs for their
small- and medium-size enterprise (SME) and mid-corporate clients.
The problem: Most businesses are “invisible”
Many banks currently use rules-based models
to generate recommendations for SMEs and
mid-corporate companies (with annual sales
up to $100 million), but with limited success.
Relationship managers often view these recommendations
with skepticism, as conversion
rates typically range between three and
five percent. They resort to general propositions
designed for the consumer segment and
devote most of their energy to those clients
whose businesses they already know well and
whose needs they can anticipate reliably. The
result is that 25 percent of a bank’s commercial
customers usually account for 85 percent
of the revenues, and the remaining 75 percent
represents the “long tail” of untapped potential.
These companies are effectively invisible
to the bank’s sales force (Exhibit 1).
Exhibit 1
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The solution: Anticipate the client’s next step
Banks are investing in building up predictive
models globally: US Bank and TD Bank
in North America; Itau and Banco do Brasil
in Latin America; Barclays Bank and Lloyds
Bank in the UK; ING, Banco Santander, and
BBVA in Europe are just some examples of
banks improving their commercial performance
by leveraging machine learning . These
advanced techniques have proven effective
in diverse customer segments, from self-employed
individuals to large corporate customers.
SMEs and mid-corps are the sweet-spot
for NPtB, as they generate massive amounts
of data, which are typically underused. With
the help of advanced analytics decisioning
engines, banks have demonstrated that it is
now practical to mine vast (and often messy)
amounts of data, separating signal from
noise, to arrive at precise recommendations
for a client’s next action. In addition, by
broadening the types of data collected for the
commercial segment, banks are also analyzing
customer behaviors, transactions, and
customer preferences across more extensive
databases.
Successful implementation of NPtB engines
has boosted new sales upwards of 30 percent
and increased commercial segment revenues
by between two to three percent. The impact
on sales efficiency has been radical in some cases, with an increase of more than 50 percent
in the number of leads offered per client and as
many as six out of ten customers purchasing a
new product in response to a sales call.
Leveraging data for NPTB recommendations
More than a decade ago, Amazon and Netflix
began leveraging data and analytics to
improve their cross-selling efforts. They
started with simple analytics, dividing huge
customer populations into several dozens of
microsegments according to key behaviors
(inputs). In order to achieve this new level of
precision, they used singular value decomposition
(SVD) to classify customers according
to patterns in their purchase histories, each
pattern culminating in a target output, that
is, the “next product to buy .” The number of
inputs and the complexity of the algorithms
used to analyze these inputs have been increasing
in recent years, achieving outputs
that have much greater precision than was
possible with next-best-action (NBA) models.
(See sidebar “From NBA to NPtB” for a summary of the
evolution of NPtB from NBA.)
The NBA engines employed by Amazon (with
more than 300 million customers reported
in 2016) and Netflix (125 million subscribers
reported in the first quarter of 2018) are not entirely suited to banks serving SMEs and
large corporations with products/services
that address a relatively narrow range of
business activity—domestic and cross-border
payments, financing, documentary credit,
investments, and insurance. By building NBA
recommendation engines designed specifically
for transaction banking, banks have
increased service levels and profitability,
improving their responsiveness to SMEs and
helping large corporate clients cut through
complex banking relationships and account
structures to optimize liquidity.
To maximize the impact of each recommendation,
decision engines should identify both
customer needs and the preferred channel(s)
for delivering the proposal and related communication.
In some markets, companies
tend to rely more heavily on direct communication
with relationship managers, who play a
key role in following up on recommendations.
In other markets, such as the Nordics and
the UK, the digital channel is the primary
means both for alerting a customer to a recommended
action and for delivering more
detailed information about the opportunity.
Consolidate data for analysis in three waves
The data reserves required to power an NPtB
engine are consolidated in three waves. As
the volume and complexity of data increase
across the three waves, analytical algorithms
become progressively more sophisticated and
accurate in predicting precise, time-critical
needs of individual customers.
The first wave starts with the aggregation and
analysis of internal structured data of various
formats, including customer demographics,
product usage, profitability, and transaction
history. For example, one bank in Europe
started by consolidating the information it
had for 1.3 million SME customers, ranging
from beauty salons, doctor’s offices, and
family-owned stores to small manufacturing
companies and technology start-ups. This
data set yielded 1,200 variables for analysis.
Continuing the focus on internal data, the
second wave introduces algorithms capable
of digesting unstructured data (e.g., call records,
email communication), as well as a
broader range of structured data from CRM
systems (e.g., share of wallet, historical risk
scoring, maturities, customer relationship
lifecycle, company value chain, and suppliers).
Fast-evolving algorithms augment the
value of data already at hand by learning to
recognize unanticipated clusters and associations
in increasingly complex data sets. The
algorithms generate actionable insights into
a company’s current needs, from payables
management to financing for new equipment,
based on information coming from transactions
and payments along the customer value
chain.
The third wave analyzes a broad range of data
from point-of-sale transactions to industry
news and comments on social media to generate
ever more precise recommendations. As
machine learning algorithms become more
sophisticated, it is possible to produce recommendations
from increasingly diverse types
of unstructured data (including, voice, image,
and video files) extracted from industry and
company websites, as well as news and social
media (Exhibit 2).
Exhibit 2
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How to build an NPtB engine: Design, develop, deploy
The preparation of an NPtB model moves
through three phases: design, development,
and deployment. Clear milestones mark the advance from analytics to proof-of-concept to
implementation in the front line.
Design the NPtB engine
This first phase is the preparation and design
of the NPtB engine—adjusting the scope,
mapping pitfalls, and elaborating the business
case to convince stakeholders throughout
the organization of the value of the effort.
This phase has five key activities:
Prepare the data. Looking across the three
waves of data consolidation, the first step
is to identify the types and sources of data
and map the variables that can be analyzed.
The goal is to locate data that can be combined
with unique customer identifiers
and provide sufficient record history (at
least two years of data). It is also necessary
during this phase to check data quality
(that is, data consistency, format validation)
to identify points for improvement
and to restructure data ingestion (for example,
defining treatment of unavailable
values and primary keys, synchronizing
time periods).Ensure the IT infrastructure meets the
processing requirements to run the
model. It is important to design the recommendation
engine so that it runs efficiently
on the current IT infrastructure. Most
banks have adequate processing and storage
capacity to run basic NBA models that
will generate actionable recommendations.
For example, a CPU system with expanded
memory capacity is adequate to run induction
algorithms based on decision trees.
However, deep learning algorithms require
a GPU system to support complex artificial neural networks.Identify business sponsor and form multidisciplinary
team. The sponsor should
be a business-line owner authorized to
make binding decisions. The team should
include data scientists, data engineers,
business translators, UX designers, and
data architects. The team identifies the
variables to be analyzed and builds the
analytical model based on precise understanding
of business goals, including
familiarity with the market segment to be
addressed and the products to be targeted.Scope the effort and build the business
case. The executive team must reach a
shared understanding of the problem to
be solved and agree on a “back of the envelope”
business case for the NPtB effort. It
should also identify the main elements for
evaluation (model performance, adoption
rate, conversion rate improvement, etc.).Identify potential roadblocks. It is crucial
to follow proper legal and compliance
procedures to ensure that the bank has the
necessary consent/permission to use and
merge the targeted data. In our experience,
most of the data available for the corporate
segment can be leveraged for NPtB analysis;
however, it is important to identify
early in the design phase any business limitations
that may delay implementation.
Such limitations may include, for example,
business involvement, change management
challenges, and workers’ council policies.
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Develop the analytical solution
In the development phase, data scientists,
data engineers, and business translators collaborate
to build the analytical solution of the
NPtB model. The goal is to identify the products
a customer is likely to buy, prioritize
recommendations, and determine the most
effective channels for delivering an offer. The
team works toward these goals by building algorithms
to answer three main questions:
Which products does a particular company
need or is willing to acquire? The
NPtB analytical engine identifies opportunities
for cross-selling. For each specific
company doing business with the bank, the
engine ranks commercial leads for each
product according to two criteria: probability
(which leads are most likely to result
in a transaction?) and value (which will be
most profitable?).Which companies need a particular
product? Next, the engine also prioritizes
clients according to their potential value/
business priorities, propensity to buy, and
more. (This step is particularly helpful for
relationship managers, who must decide
how to prioritize follow-up calls and visits.)Which channels should be used to optimize
the success rate of the commercial
opportunities? Leads are distributed to
digital and traditional channels based on
company behavior and preferences, contact
policies, and relationship managers’
commercial activities.
To answer these three questions precisely,
banks can analyze customer data, such as
payments transactions and digital interactions.
Machine learning algorithms can identify
patterns in past customer behavior to
predict future customer purchases. Data scientists
build NPtB engines leveraging modeling
environments such as R, Python, or Spark.
While deep-learning algorithms generate the
most accurate predictions if data sources are complex and unstructured, gradient boosting
machines, random forest algorithms, and even
logistic regressions provide valuable commercial
opportunities for the NPtB engine. In
addition, gradient boosting machines have the
advantage of generating reliable recommendations
with smaller data sets or data sets where
there are gaps. These models are trained with
past data and statistically back-tested on an
out-of-sample and out-of-time customer base
to quantify model performance.
Deploy and pilot NPtB engine
The last phase is to embed recommendations
in digital channels and relationship managers’
interactions with clients. Banks test and
refine the NPtB pilot in the field before rolling
it out to the full market.
A European bank recently tested an NPtB engine
in five branches to evaluate the precision
of sales leads. A team of relationship managers,
product specialists, and branch managers
participated in the pilot, which included
training on how to follow up on recommendations
and testing the effectiveness of leads
with clients. Over three months, the team
tested the leads with companies and provided feedback. A skeptical relationship manager
selected an offer for a letter of guarantee
recommended for a particular client, and
commented, “I don’t believe the customer
will buy this, I know the company.” When
the relationship manager asked the company
owner if he needed a letter of guarantee this
month, he answered, “How did you know? I
am currently negotiating this product with
another bank.”
In the course of a similar pilot with another
financial institution, the model predicted
that 4,500 companies, for which there was
no indication in the data of previous international
trade activity, would purchase international
trade products in the coming month. As it happened one in five of these companies
purchased an international trade product for
the first time within 30 days. Based on the
performance of the pilot, the analytics team
updated the model before implementing it
across the entire organization to target more
than one million customers. The full launch
included four weeks of coaching for more
than 600 relationship managers. Within five
months of starting the project, the NPtB was
fully up and running, with the predictive
model stabilized and relationship managers
fully trained. Ultimately, this bank increased
new sales by more than 30 percent, and relationship
managers increased their interactions
with commercial customers by more
than 50 percent.
Smarter analytics for banks
The pilot is an important opportunity to secure
the endorsement of team members participating
in the pilot, who then share information
about the model with other colleagues. The
pilot is also an opportunity to test metrics for
evaluating the sales process, such as number
of visits, percent of leads used by relationship
managers, conversion rates, and the level of
satisfaction among relationship managers participating
in the pilot (versus control group).
In addition, the pilot phase is the time to begin
testing long-term performance metrics (in
order to ensure sustainability in the front line),
for example, hit rates for branch staff and relationship
managers, customer profitability, and
customer satisfaction.
In the transition from pilot to full roll-out, it
is crucial to ensure that the organization is
aligned around the NPtB use case and that a
support team is assigned for the deployment.
Adapting an NPtB engine to serve large corporate clients
Banks have also been able to improve the
relevance and timeliness of their recommendations
to large corporate clients. At
many institutions, relationship managers are
thoroughly familiar with the general needs
of their corporate customers, but sometimes
they are at a loss to anticipate changes in
these needs. This was once the case for a large
European bank operating in diverse regions.
It now draws on a broad range of data to understand
general market trends and specific
company behaviors. This requires not only applying
advanced analytics to traditional types
of information (annual reports, market conditions,
competitor news) but also collecting
publicly available data on social media (company-
managed pages, customer comments,
etc.). An NPtB engine extracts insights from
available data to alert corporate treasurers to
new opportunities, for example, to leverage
complex banking relationships to improve cash
flow and lower the cost of short-term financing.
Identified leads include opportunities in
various currencies, possibly triggering a change
in cross-border pooling arrangements; letters
of credit, domestic and international guarantees; or even investment banking products, e.g.,
debt capital management. NPtB engines can
boost new sales among large corporate clients
by as much as 15 percent.
Implementing NPtB for SMEs/midcorporates
The lessons learned from banks that have
implemented an NPtB engine can be summarized
in five points:
Design the NPtB engine according to the
characteristics of the market segments
served. Consider first internal data, including
company profiles, relationship
characteristics, product granularity, and
opportunities. Expand the data set to include
external data, testing the relevance
of the new variables in generating useful
recommendations. In developing algorithms
to generate predictions for the large
corporate segment, it is important to test
a broad variety of external data in order
to build a robust data set that can produce
insights with a new level of accuracy.Build the model around customer needs
and interests. One of the biggest impacts
is shifting from a “product push” approach
to interactions that address specific customer
needs, as reflected in current transaction
activity and financial performance.
This shift enables relationship managers,
service representatives, and product specialists
to help customers weigh their options
and choose the path that best serves
the company’s financial interests.Pilot the outcome of the NPtB engine to
build confidence and secure buy-in. Relationship
managers must be confident in
the opportunities identified by the NPtB
engine; at the end of the process they will
leverage leads to improve their sales effectiveness,
but change management and
internal buy-in are key for successful implementation.Focus on prototypes that create excitement.
Don’t let IT and the complexity of
legacy systems become the bottleneck, but
start with a pragmatic “proof-of-concept”
to demonstrate the model’s potential.
Quick test-and-learn prototypes have
multiple purposes, including learning and
improving but above all showing prompt
impact to create enthusiasm.Ensure impact from multiple levers. Better
targeting based on analytics is crucial,
but there are additional levers, including
the timing of recommendations, framing
recommendations within a broader value
proposition, measuring the impact of recommendations
(including the performance
of relationship managers), which can also
improve performance.