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A PERFECT MATCH: FINANCIAL SERVICES AND CUSTOMER DATA PLATFORMS
Rob Fuller, Matthew de Ganon on November 11th 2020
This is a guest submission by our partner Accenture. Matthew de Ganon is the
Northeast Financial Services Lead at Accenture. Prior to Accenture, Matthew
spent four years at Capital One leading product management for Digital Payments
and Customer Understanding. Rob Fuller is the Customer Data Orchestration
Practice Lead for Accenture Interactive, focusing on how brands collect, manage,
and integrate customer data. In this post, they share how financial services
companies can drive growth by rediscovering how to put their customers first.
--------------------------------------------------------------------------------
Over the last few years and especially today, companies have had to rethink how
they do business. For banks, this means rediscovering their original purpose of
putting customers first.
Having worked with dozens of top financial institutions over the past six years
and delivering digital solutions across industries for over twenty, both Rob and
I have seen that customers and companies alike want the same thing: relevant
experiences that build trust and increase customer loyalty.
A recent Accenture report found that 5% of banks’ revenue is at risk as millions
of customers are persuaded by the transparent and tailored offerings of fintech
entrants. Twenty million customers in the UK alone have opened accounts with
neobanks, putting pressure on traditional financial service companies to defend
their market share by innovating how they engage with their customers.
Goldman Sachs and Citigroup have established funds to invest in or acquire
fintech startups. Others, like Bank of America, have embraced going digital with
robust mobile banking services. A few, like JP Morgan Chase, are investing in
data to build machine learning practices that deliver hyper-personalized
experiences and improve security for their customers.
In this post, we share the infrastructure for good data and how our customers
use that data to launch machine learning programs that earn customer's trust.
LAYING THE FOUNDATION FOR CUSTOMER UNDERSTANDING
Customers have high expectations — they are becoming accustomed to technology
understanding who they are, what they are doing, and how they want to engage.
To meet these high expectations, companies rely on rich data. Traditionally, it
required a fair bit of data wrangling by engineering and data teams to cobble
together systems and clean the data. Fortunately, a new breed of solutions,
Customer Data Platforms (CDPs), have come to market. It provides organizations
with a unified platform to capture and synthesize data across all customer
touchpoints. A CDP plays a crucial role in reliable data capture, identity
resolution, and data activation. It can resolve data quality blockers previously
preventing the data integration and analytics functions from driving
customer-centric and contextualized engagement.
For example, a global media customer wanted unified customer analytics to drive
digital traffic, reader loyalty, and revenue across its portfolio of 36 brands.
The company had built data pipelines internally, but each pipeline served
individual brands resulting in dozens of unique data architectures and siloed
data sets.
The walled-off tech stacks limited its ability for executives to measure
enterprise-wide trends and performance. Instead of untangling its current
in-house infrastructure, the company chose a CDP to speed up the unification
process and scale with the growing needs and requests across its multiple brands
and teams.
Investing in a CDP not only eliminated manual work to build and maintain data,
but it also opened an opportunity for them to go beyond their current
capabilities—unlocking audience segmentation to launch more targeted
experiences.
> — The CDP implementation saved the company over $2m in engineering resources
> and hundreds of thousands of dollars in infrastructure costs.
Key capabilities of a CDP
While there is a standard set of functionality that qualifies a platform as a
CDP, not all CDP solutions are created equal Some CDPs like Segment, for
instance, prioritize collecting clean, consistent, compliant, and consented data
to deliver reliable experiences.
When evaluating a CDP, some key functionality to look for:
* Securely collects user engagements across the customer journey.
* Consistently formats customer data to be used in downstream applications.
* Manages user consent and privacy requirements.
* Creates an accessible view of the customer across all business systems.
* Activates data in preferred channels to create compelling, individualized
user experiences.
Implementing a system that has a robust set of tooling for data governance
ensures the data in your tools is accurate, consistent, and complies with
internal privacy and security policies. By enforcing common data standards at
the point of collection, a CDP like Segment guarantees you are feeding
consistent data to your machine learning models for accurate personalization at
scale.
DELIVERING CUSTOMER-CENTRIC EXPERIENCES AT SCALE
While a CDP can bring lots of functionality on its own, it’s critical to ensure
other systems integrate with your CDP. Other systems can maximize a CDP’s
functionality of real-time collection, identity resolution, and activation to
create incredible customer experiences.
For example, let’s say a customer tries and fails to pay a bill online. Within
five minutes of that event he calls into the Interactive Voice Response (IVR).
The system should receive that data and be aware of that online failure and
adjust accordingly. Instead of, “How can I help you today?,” the greeting is,
“Hi Isiah. I see you tried to pay your bill online. May I help you complete that
task?”
Isaiah then receives a follow-up message encouraging him to set up automatic
payments. Isiah is happy because he no longer has to take the time to explain
why he is calling. The company is happy as they can now deflect volume and
provide a better experience through a self-service channel. Each of these data
points allows the company to build a better understanding of its customer.
> — Customers want a personalized experience to match their context. Sending the
> wrong message at the wrong time, too frequently or not enough, can diminish
> trust in your brand. In an industry like financial services, trust is
> paramount.
LAUNCHING A MACHINE LEARNING PRACTICE
The right data and systems coupled with a strong machine learning practice can
deliver the right messages to your customers in the format and system they
expect. Accenture’s research suggests that machine learning drives competitive
advantage and will add US$1.153 billion in value to the financial services
industry by 2035.
Clean and consistent data is critical for powering accurate machine learning
models. Using a CDP as the key source for models, companies can define relevant
features that drive the most accurate predictions, like how, when, and where a
customer wants to interact with your business.
For example, a new financial services entrant was able to increase onboarding
activation from enrollment to direct deposit by 140% by leveraging Segment’s
capabilities to ingest and activate data. Segment collected and sent hundreds of
different user actions along the customer journey to its machine learning
application. The application trained a lookalike model to forecast user intent
and build cohorts based on propensity to engage in key milestones along the
customer journey. Those cohorts were then sent back to Segment to activate in
its web experimentation and email platforms. So now, when a user reaches a
qualifying step, its marketing automation platform can send them an SMS, email,
or in-app notification depending on propensity score to activate in the coming
week.
> — With rich
While clean and consistent data is key for machine learning, implementing a
successful machine learning initiative requires more. Before kicking off your ML
project, take time to:
* Understand who your user is, what they need, and what delights them.
* Identify ownership across key internal stakeholders.
* Define the metrics and business goals.
* Determine which areas of the user journey you will experiment with first.
* Ensure agility from insight to action. How often will your team refine, stop,
or build new experiences based on insights uncovered?
By first understanding your customer and then defining the metrics and
experiments you want to run, you are ensuring you are optimizing the experience
for your target persona while impacting the most important metrics for your
business.
LOOKING TO THE FUTURE: BUILDING TRUST TO ACCELERATE GROWTH
Pre-pandemic, having the ability to deliver consistent experiences was
differentiating enough. Now, with consumer behavior changing and financial
pressure growing, consistency is considered the bare minimum.
Banking and financial services have the opportunity to drive a new narrative by
building customer empathy into everything it does, or it risks customers moving
their business to those that have adapted to their needs.
Rolling out a CDP such as Segment provides businesses with the flexibility and
capability to support real-time personalized experiences that can build the
trust that drives purchase intent. Doing so requires choosing the right CDP,
applying the right governance, and the willingness to adapt or transform
operational models to capitalize on the data and insights surfaced.
Are you starting a digital transformation project? To get started unifying your
data, request a demo to speak to a Segment expert.
Share
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THE STATE OF PERSONALIZATION 2022
Our annual look at how attitudes, preferences, and experiences with
personalization have evolved over the past year.
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