2018: The ‘Year of AI and Machine Learning’ for Financial Marketers

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.

Subscribe TodayThe people 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.

The only 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.

True 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

Predictive 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’.

Bringing 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.

According to an IDC white paper, , 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%).

Personalized calls to action (46%) led to the greatest satisfaction among respondents.

Natural-language generation also has the potential to lighten the load of content creators. By 2018, 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

Combining 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.

According to 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 , 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.

3. Improved 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 (CMI), intelligent algorithms can help personalize:

  • Website 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.
  • Push notifications – 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 by , 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 , 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 preferred.

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 Marketing

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.

Jim MarousJim Marous is co-publisher of The Financial Brand and publisher of the , a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital available to rs. You can follow Jim on and , or visit his .

This article was originally published on January 10, 2018. All content © 2018 by The Financial Brand and may not be reproduced by any means without permission.

Comments

  1. Surely it is true, but from what I see it’s very much quite slow and difficult journey forward, getting the insights from vast amount of data is one thing but embed it into seamless customer journey is hugely complex…

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