There has been a 50% increase in volume of financial search phrases on search engines over the last five years, according to Google Trends.
The upward trend for search phrases such as "commercial loans," "interest checking accounts," "mortgages," and "local banks" shows that consumer and commercial customers looking for financial products and financial institutions are starting their search online. As these customers venture out across the web to find options, they are being met by static bank and credit union websites with generic product and brand marketing. While financial marketers and customer experience managers agree that the future of digital finance is dynamic and personalized, few banks and credit unions have implemented these powerful technologies on their public websites and applications.
Where is personalization innovation happening?
Companies such as Amazon, Pandora and Netflix have demonstrated that dynamic sites with practical personalization outperform static industry peers with:
- Higher customer loyalty and trust
- Higher ratios of converting prospects to customers
- And higher rates of repeat customer business
Amazon, with its well-known personalization engine, brings in over $100 million per day with personalized recommendations. Amazon is constantly researching new applications of personalization including a current program that ships items a customer will likely buy to the nearest distribution center before a customer adds them to their cart. Despite this, financial service brands – with the most sensitive of our data – are still presenting offers on their homepages for products or services we already have.
Why aren’t banks, credit unions and investment service companies implementing personalization?
In Extractable’s web analytics research, visitors that are exposed to personalization on bank and credit union websites have three to eight times the conversion rates of visitors that are only exposed to static messaging. Additionally, Episerver data indicates 25 percent of global shoppers are more likely to purchase from a brand again when their experience is personalized.
The ROI from applications of visitors driven by personalization easily covers the expenses involved in implementing personalization. The ROI for personalization is clear. Forrester Consulting, for example, conducted research on behalf of Episerver and found AI-based personalization yields, on average, 5% incremental conversion improvement and 5.5% incremental basket size improvement, amounting to over 10% possible revenue uplift from personalization alone on that digital experience platform.
However, in Extractable annual reports on the best bank websites and best credit unions websites, few are using personalization to promote products because of the complexities involved with older platforms such as:
Digital Personalization = Increased Technical Complexity + Increased Content & Creative Resources + Increased Digital Overhead
With the introduction of user-friendly platforms such as Episerver, Demandbase and LiveRamp, non-technical marketers can implement personalization targeting anonymous and known website visitors. These mature tools are minimizing the complexities involved in implementing personalization and enabling non-technical team members to implement personalization with minimal effort from engineers.
With the adoption of these new technologies, bank customer experience and marketing teams can implement personalization on their public and authenticated sites (online banking or OLB) without a significant investment of resources.
Financial institutions can excel in personalization and achieve the lift in applications and customer loyalty. It is advisable to select the types of personalization that are applicable to the largest segment of the audiences and provide the highest lift in conversion. Organizations such as Matthews Asia are using Episerver integrated with data sources like CRM to drive new levels of personalization and digital marketing performance. Below is partial list of the types of personalization that can be implemented within the CMS without technology resources:
Behavioral Personalization: This personalization attempts to determine the visitors interest based on their actions.
- Visit count: Simply show a different set of content and images to new visitors (i.e., prospects), return visitors, and visitors on their less-than 10th visit (i.e., customers).
- Search phrase: When a visitor performs a search on the site (i.e., “mortgage rates”), the CMS can use this data to customize the experience on current future visits.
- Content viewed: When a visitor views specific content (i.e., checking account benefits), the CMS can customize the experience on current and future visits.
- Functions performed: When a visitor performs a function such as start a deposit application, use a calculator, create a trouble ticket, or set up bill pay, the CMS tracks these functions and personalizes the experience with support or marketing content.
- Referrers: When a visitor comes to the bank website from a referrer site such as an auto dealerships or real estate agency, the CMS can customize the experience accordingly.
IP Based Personalization: This personalization can gain information about the anonymous visitor from the IP address and DNS record.
- Geo location: Based on the city, county, metro, or state the visitor is coming from, the site can show local images and/or use local vernacular to improve the experience.
- Company attributes: For commercial prospects and customers, the IP address can sometimes inform the CMS of the industry, company size, or individual company name to customize the experience.
Online Banking, CRM, and Loan or Deposit applications: These effective types of personalization use data from other banking platforms to drive personalization. While this type of personalization may seem complex, the implementation is often easier than first perceived.
- Products not owned: This personalization simply markets the products that the customer does not have and is likely to be interested in.
- Propensity to buy: Based on the visitors (prospect or customer) behaviors on the public website, a propensity model can be created and used to drive personalization on promotional areas such as the marquee.
Once implemented, a team monitors the performance of visitors that are exposed to the personalization and interact with it to see which types of personalization drive the highest lift in deposit/loan applications and which drive the highest levels of customer satisfaction.