January 12, 2018 9:20 AM /
The Financial Brand
“Financial marketers must
understand the latest artificial intelligence and machine-learning marketing
applications to succeed. Not only do consumers expect a new level of
personalized communication and engagement, but revenue and cost pressures
require a more efficient marketing mix with improved results”. By Jim Marous, Co-Publisher of The Financial
Brand and Owner/Publisher of the Digital Banking Report
who have selected your organization as their primary financial institution, or
are considering doing so, see more offers and content in a day than ever
before. They see marketing messages everywhere they look and on every channel
they engage with. Making matters worse, the vast majority of these consumers
engage with one of your well-trained customer service representatives less than
they ever have in the past. Bottom line, financial marketers have their work
cut out for them.
viable and potentially scalable solution is content that is so personalized and
relevant that it’s impossible to ignore. We need to look for ways to
communicate to an ‘audience of one,’ using artificial intelligent (AI) systems
that constantly work in the background to enhance every step of the customer
journey. We need to leverage new tools that were previously only available to
the very largest companies with huge support staffs.
personalization at scale requires advanced analytics, which is why banks and
credit unions of all sizes are using AI and machine learning to customize all
components of the marketing mix. Your marketing team can no longer postpone
using AI-powered solutions in your content development, offer selection,
segmentation and targeting, website integration, customer service/support,
product pricing and churn management.
Here are some
ways artificial intelligence and machine learning can improve both the
marketing process and the customer experience.
1. Content Development/Offer Recommendations
analytics can assist financial organizations develop messaging and make offer
recommendations. Similar to how Amazon and other retail organizations fine-tune
messages and offers in real-time based on purchases and digital shopping
behavior, financial services organizations can test communication/channels and
offers to find the ‘perfect mix’.
together consumer insights from diverse data sets is a common use of AI. These
can be insights from multiple internal data sources as well as third-party
insights from credit bureaus, social sites, etc. The result is the ability for
the your organization to create contextual and personalized communication and
advice based on aggregated insights … potentially in real-time.
an IDC white paper, Can Machines be Creative? How Technology is Transforming
Marketing Personalization and Relevance, the most
commonly personalized element was images, with 58% of marketing execs
automating the personalization of images in marketing communications. More than
half said that their teams were personalizing taglines (57%), naming (57%),
formatting (55%) and color palette (51%).
calls to action (46%) led to the greatest satisfaction among respondents.
generation also has the potential to lighten the load of content creators. By
2018, Gartner predicts, 20% of all business content will
be authored by machines. Over time, this capability will be used more and more
for B2C communication as well. Many organizations are already using AI tools to
automatically generate personalized email content, text messages and to curate
content for social media.
2. Consumer Targeting and
Lifetime Value Enhancement
internal and external data into a clustering algorithm, then using the results
in a CRM system, is a great use of machine learning. Even though advanced
analytics can ultimately process millions/billions of data points more
efficiently than ever thought possible, it still does not make sense to focus
on your entire user base without regard to the potential value of the consumer.
Brian Solis, “Customer Lifetime Value (CLV) tied to artificial intelligence
(AI) and machine learning focuses marketers and developers on targeted
engagement and growth. The idea is to drive profit by investing in more
value-added user experiences and personalized offers. Doing so intentionally
cultivates meaningful relationships with key customers.”
In a banking
industry study by Bain, it was found that it costs banks $4 every time a
customer calls or visits compared to only $.10 when consumers use a digital
app. Therefore, to reach potential high-value customers, AI/machine learning
can use data from existing high-value customers to move other similar consumers
to more efficient interactions.
The goal is
to use the advanced analytic tools to find and engage the ‘right’ customers and
members and to maximize the experience while optimizing the revenue. What’s
nice about AI and machine learning is that, the more the system learns, the
more it improves and optimizes.
Website Experience and Sales Conversion
While the design of your website can’t be done
entirely by a robot yet, AI can help improve your visitor experience with
intelligent personalization. According to the Content Marketing Institute (CMI), intelligent
algorithms can help personalize:
experience – By
analyzing hundreds of data points about a customer or member (internal product
use, location, demographics, device, interaction with the website, etc.), AI
can display the best-fitting offers and content.
Using behavioral personalization, push notifications can be specific to
individual users, delivering them the right message at the right time. AI can
also assist with intelligent re-targeting.
According to the 2017 Real-Time Personalization
Survey by Evergage, 33% of
marketers use AI to deliver personalized web experiences. When asked about the
benefits of AI-powered personalization, 63% of respondents mentioned increased
conversion rates and 61% noted improved customer experiences.
According to CMI, “At a time when
customers expect increasingly meaningful experiences, you can use AI to automate
a huge part of personalization. As a result, your website visitors can see the
most relevant content, notifications, and offers based on their current
relationship, location, device, demographics, and browsing history.”
4. Chatbots, Digital Assistants and Messengers
Chatbots are thought by many to be the
future of user input on mobile, replacing apps. Talking or typing to a chatbot
can allow a service to be delivered through the analysis of natural language
combined with understanding your organization’s data sets. AI-powered chatbots
can replace many of the current customer support processes. In fact, in some
cases, chatbots are better at creating personalized content than humans.
As Techcrunch points out, Facebook’s platform could soon
lead to chatbots replacing ‘1-800 numbers, offering more comfortable
customer support experiences without the hassle of synchronous phone
conversations, hold times and annoying phone trees.’ Chatbots can aggregate
location-specific requests to detect patterns, spot repetitive issues, and
predict what’s causing challenges for a particular user.
5. Product Pricing
Should products and services be priced
exactly the same for every customer or member in your database? Optimally,
product and service pricing should reflect the profitability of the
relationship and the overall impact a specific pricing decision would make on
the relationship (similar to pricing done in commercial business
relationships). With dozens of factors products impacting the sales model, an
estimate of the price to sales ratio or price elasticity would also be
Dynamic price optimization using machine
learning can help correlating pricing trends with sales trends by using an
algorithm, then aligning with other factors such as product management
goals and cost to deliver/service the product or service.
6. Predict Churn and Promote Engagement
AI and machine learning also can help
identify segments of your customer/member database that are about to churn or
leave for a competitor. Using internal and external data sets, a predictive
model can be built, tested and validated that on real customers. The resulting
insights can intelligently predict what stage of disintermediation the person
is in. While users who abandon your organization very shortly after opening a
new account or applying for a service are difficult to impact positively,
customers or members with a longer-lasting relationship can be intercepted as
they are contemplating a move and incented to stay with your organization.
According to the CMI, “When combined
with personalized content creation, AI-powered churn prediction helps keep more
of your customers/members engaged, leading to higher lifetime value and
profits. As churn prediction is unique to every product and company, the
machine-learning algorithms need to be adjusted for your company or built from
the ground. With that information, you can create more effective content to be
delivered to disengaged users.”
The Future Intersection of AI, Machine Learning and
Today, the majority of marketing execs
who use AI or machine learning do so to drive personalization of content and/or
offers. In the future, machine learning will be used more extensively for media
planning and execution, multichannel campaigns and highly contextual ads. In
other words, advanced analytic tools will extend beyond a point solution to a
broad customer journey solution that informs every aspect of marketing.
The increasing accessibility of data and
the lower cost of advanced analytic capabilities means that making the most of
AI and machine learning is going to be a necessity for financial industry
marketers in 2018 and beyond. While the full extent of AI and machine
learning’s potential is yet to be realized, consumers are already expecting
their primary financial institution to know them, understand them and reward
them on a highly personalized basis.
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