The Power of Recommendations in Financial Services
An industry-specific recommendation approach can bring a new dimension of personalization-based growth.
Many retailers start their personalization journeys with recommendations—digitally translating the in-store sales associate’s best practice of aiding a customer in their product discovery—and seemingly instantly generate meaningful results. Yet, financial institutions (FIs) haven’t been able to achieve the same degree of success with one of the most straightforward types of personalization.
This partially has to do with the industry’s long consideration phase, as it’s much easier to say yes to new home decor than it is to a new home loan. However, FIs exacerbate the problem by limiting their recommendations strategy to only include products when they could be serving educational resources, offers, and content to inform and help customers feel understood.
In this post, we’ll touch on why financial services should leverage recommendations and what they need to do so, with examples of where and how to use them.
Product and resource recommendations as a decision-making accelerator
Many FIs have prioritized personalization as a majority of customers have already moved to primarily digital relationships, with 45% using mobile and 27% managing their everyday banking needs from the web.
However, this adoption of digital banking and its concurrent growth has elongated product research and decision-making cycles. With more options than ever across a growing arena of financial services players, customers need education and guidance to aid in the selection process.
Additionally, according to a 2023 Forrester report:
- 42% of banking customers believe that product offers are more valuable when tailored to their personal needs
- 38% of banking customers believe banks should make it easier to discover financial products
- 31% of banking customers wish their banks were more proactive about giving relevant information
And while many FIs have created high-quality content, few have effectively used personalization to facilitate learning and discovery – recommending the right information, advice, or products to customers based on the digital signals they leave behind. In eCommerce, “guided selling” is a concept that online retailers have adopted to help, educate, and streamline the process through which information and products are discovered. Starting in the early stages of a customer relationship, this establishes an understanding of the user and helps set customers on the right track of their journey.
Though not a one-to-one translation of guided selling, FIs can take a page from eCommerce’s book and pave the way for more personalized, relevant, and seamless customer engagement across channels, offering up product and resource recommendations based on cues from the user.
A framework for choosing the right FI recommendation strategy
To ensure value is being derived from recommendations, FIs should consider the following factors:
- What are the goals I’d like to achieve?
- Who are the audiences I’d like to target?
- Is my data feed optimized for results?
- Which recommendation strategy should I use?
- Where should I place these recommendations?
With these in mind, they can begin to piece together an approach, which I’ll dive deeper into below.
An easy way to set goals is to focus on which KPIs need improving.
Some examples of personalization KPIs for financial services include:
- Open accounts click / apply click / get started click
- Application start or complete
- Form submission
- Mobile app downloads
Knowing which action you’d like your audience to take will help guide you towards which recommendation strategy will be most effective, and where to place the recommendations on your digital property.
Teams can take an FI-specific audience strategy approach, identifying impactful segments to build recommendation experiences around. A good way to get started is by identifying 3-4 primary audiences to consistently target, analyze, and optimize towards. These audiences should be based on a single segmentation principle and comprise 100% of the site’s traffic for maximum efficiency.
Some examples of segmentation principles for financial services include:
- Engagement level: Are they logging in to pay their bills, signed up for automatic payments, or engaged in other telling activities?
- Lifecycle phase: From prospect to early month on book, mature or declining – where are they in the customer lifecycle?
- Product attainment: How many products or services do they use, and are they in different categories (cross-sell opportunities)?
Each of these audience segments has its own set of questions and needs, making them more (or less) conducive to the various types of recommendations. For example, serving a high-engagement customer a recommendation for a boat loan based on their recent browsing history makes sense – but not a low-engagement prospect who may benefit more from educational content at this point in their customer journey.
A data feed is the source of truth for all things recommendations, responsible for powering the different types of products, offers, resources, or content showcased. And while more data is better, if each asset has not been tagged with the proper metadata based on its attributes, it won’t be able to generate valuable results.
A good place to get started is by creating a data feed that correctly tags product metadata based on the product category it is associated with (e.g. Checking, Saving, Credit Card, Lending, Wealth Management). Some product types may also have additional attributes, so it’s important to account for these when tagging as well. For example, credit cards also offer various categories of perks (e.g. Travel, Dining, or General Cashback).
Certain products can also be tagged by target audience segments, such as customer status, so an FI can showcase recommendations that would appeal to a new customer as well as those that speak to customers with a long history at the bank. And finally, products can be tagged by engagement. For example, checking, savings, and credit cards would most likely appeal to users with low or no engagement, whereas lending and wealth management would make sense for highly engaged users.
To recap product feed tagging best practices for FI:
- Product category (e.g. checking, savings, credit card)
- Product attributes (e.g. travel perks)
- Customer status (e.g. prospect, new client, loyal customer)
- Level of engagement (e.g. low, medium, or high)
Setting up the feed with organized metadata before launching recommendations will position FIs to succeed. However, they must make time for ongoing development and maintenance, as products shift and the personalization program grows.
The overall success of a recommendation campaign hinges on an understanding of which strategies align with each audience segment and the available information on each user.
Different recommendation strategies include:
- Affinity-Based: Recommendations are personalized to each individual based on their user affinity profile, which is great for high-engagement users with enough data collected. FIs might also use affinity-based recommendations for credit cards with travel, dining, or general cash-back rewards to a user who has an affinity to those categories based on their spending data.
- Recently Viewed: Recommends the last items viewed by the current user, with the most recently viewed items appearing first, typically based on data from the last 30 days. This is a great strategy for increasing prospect engagement levels by allowing them to pick up where they left off in the previous session.
- Similarity: Products that resemble the item (or group of items) currently in view, factoring in the item’s popularity. This can help promote product discovery for high-engagement prospects, like those comparing credit cards. Alternatively, a bank may use this strategy to suggest products like checking, savings, and credit card options to an existing wealth management customer.
- Viewed Together: Items that are frequently purchased together with the item currently in view are displayed using this strategy, which can present upselling and cross-selling opportunities for high-engagement customers or prospects.
Different pages on an FI’s site serve different purposes and indicate different levels of intent from visitors. The customer expectations for each on-site location make certain types of recommendations more or less relevant, and tailoring recommendations accordingly will improve their relevance.
Typically, FIs should consider three main areas:
- Homepage (before login): A large mix of customers and prospects with varying intent levels visit the homepage, making it a strong candidate for recommendations designed to spur engagement.
- Blogs page (before login): Users typically visit a blog page to find information, making it a great location for content recommendations designed to educate and increase engagement.
- Account overview page (post login): The account overview page can showcase recommended products or offers to existing customers to encourage usage and increase overall spending.
Examples of recommendations in financial services
Let’s look at three ways FI can effectively use recommendations. We’ll walk through each example using the framework above:
- Chatbot recommendations
- Article recommendations
- Offer recommendations
1. Chatbot recommendations
Chatbots are a great way to recreate an in-person consultative experience, which can be integrated with the product catalog to provide personalized recommendations right away. Helpful for influencing conversion rate (e.g. apply click, application start and complete, etc.) among new or unknown visitors who are unfamiliar with your products and services, an FI might consider placing a chatbot recommendation experience on multiple pages early in the customer journey, such as the homepage or category page. Results could then be populated through an affinity recommendation strategy that leverages direct input from the visitor collected throughout the conversation, matching them with the right educational resources, offers, or content.
2. Article recommendations
Blogs and articles are crucial for the education of prospects as well as cross selling and upselling, and can be recommended to help users advance along their journey. The goal of article recommendations is to increase engagement and pageviews, particularly for those who have not shown much activity and likely require more information. An FI could take advantage of its existing blogs page to recommend additional content based on a ‘Viewed Together’ or ‘Similarity’ strategy – running an experiment to determine the best performing one for this particular audience.
3. Offer recommendations
Especially crucial for current cardholders given they can make or break an FI’s top of wallet position, if able to recommend exciting offers, cardholders will reach for that card first. To make this a reality, we’ll set to influence ‘offer engagement click’ as the main KPI of an offer recommendations experience, which makes sense to target an audience of ‘cardholders or one product attainment.’ A prime area for placement would be on the homepage (before login), where an FI could test between an affinity-based recommendation strategy and one that highlights popular offers that are ending soon.
Recommendations within financial services: recommended to win big in customer acquisition and lifetime value
Financial institutions are well aware of what it takes to acquire, engage, and retain customers, but increased competition from new market entrants and higher than ever consumer expectations has made it impossible to go on without incorporating personalization across the customer lifecycle. Recommendations are a great place to start, and with the right methodology (which incorporates not just products but also offers and educational resources), teams can help customers gain confidence that they’re making the right financial decisions – leading to improved business metrics in the form of reduced acquisition costs and greater lifetime value.