Your digital customers expect one-to-one interactions with their bank. They seek authenticity, personalization (tailored to me), and consistency across channels. Communications must be informed and based on up-to-date information that reflects previous transaction history and behaviors. To deliver the best customer experience, and inspire acquisition, conversion, and loyalty, it’s important you provide the right message, support, offer, or incentive to the right people at the exact moment that matters. That means being able to leverage detailed interaction data to anticipate, predict, and respond to consumer behavior as well as to personalize experiences in real time.
Banks are increasingly turning to advances in technology such as machine learning, which enables you to quickly contextualize all of your customers’ data and predict their likely next actions. It can also support you in providing advanced recommendations and help customers make decisions faster - which in turn boosts revenue.
First, let’s dive into interaction data, and what it means for financial organizations.
What’s interaction data and how can it drive personalization for banking customers?
Customer interaction data is an exhaustive record of all customer behaviors and experiences taking place on digital channels such as websites, mobile apps, IOT devices, online chat, product reviews, and much more. It’s more than just clicks and pageviews – interaction data also captures behavioral patterns and insights to help build a complete customer profile.
Interaction data provides visibility into the products your customers are interested in at the individual level, offering the opportunity to reach out to each customer with a highly relevant offer or call to action ‘in the moment’ and on the channel they’re interacting with. This is so much more powerful than traditional segment-based, bulk personalization.
Why do banks lack interaction data?
Capturing detailed interaction data from any channel is a significant challenge. Stitching data from multiple channels, devices, and divisions is even more complex, but it’s necessary to deliver real-time personalization. To be of use by decisioning solutions within banking MarTech stacks, this data must also be highly structured, and the most relevant signals extracted to prevent overloading downstream applications. Finally, this lightweight, yet highly detailed interaction data must be connected to decisioning solutions within milliseconds to ensure next-best-actions are available before a page loads. Very few data capture solutions are capable of achieving this.
Every brand has the potential to create wow moments.
Historically, investment in digital channels and digitization means that most banks already have many of the supporting systems and data sources needed in place and ready to be leveraged. So, providing a framework to deliver personalized experiences won’t mean huge IT investments, legacy replacement, or lengthy implementations. In many cases, a software solution and suitable integration can provide all the additional functionality required. Even better, these deployment costs will be quickly offset by increased revenue.
Let’s look at what’s needed to turn banking interactions into moments that matter:
- Capture the right data across channels
Choose a data capture solution that captures behavior and interaction data about every customer once and can then be used many times both in real-time (milliseconds) and as part of wider analysis. It’s important the solution is capable of capturing your customers’ mobile interactions, i.e. gestures, device orientation, and chatbot text - not all do. It’s also important to ensure your solution can capture from Accelerated Mobile Pages (AMP) and browsers with tracking prevention technologies such as Apple’s ITP. Ensure data capture is compliant with GDPR and other privacy laws. Consent must be gained from customers and automated systems must prevent the capture of opted out customers’ data.
- Contextualize the data for deeper insights
Enrich customer behavioral data to add valuable context to customer profiles which can be used to enhance real-time interactions and engagement. Natural Language Processing updates customer profiles with sentiment scores, visitor intent, and preferences in-the-moment across brands and channels. Data mapping links valuable intel with precise situations and individual requirements, providing a foundation for personalization and next-best-actions based on real-time scenarios.
- Personalize, trigger, and deliver content
Automated marketing signals (AMS) linked to enterprise decisioning systems automate the delivery of personalized content based on pre-configured behavior signals. The ability to respond as soon as a customer ‘opportunity trigger’ arises dramatically boosts conversion and improves service with minimal effort. Low latency connectivity of smart data to enterprise decisioning applications then allows highly personalized content to be delivered to the individual to create the best experience for their situation at any given time.
- Optimize engagement and minimize issues
Automated visibility detection boosts marketing ROI by maximizing content, offer, and ad ‘viewing time’. Performance timing can identify and resolve problems with inefficiencies in sales or customer service-related channels. Anomaly detection automates the identification of unusual customer behavior and channel performance issues, facilitating more rapid diagnostics and response.
- Learn and improve
Factor in progressive evolution to accommodate future learning and growth. Customer expectations are always increasing, as is the technology to identify, understand, and respond to those needs with hyper-personalized, moments-based experiences. The enterprises that act on these opportunities the fastest, and most intelligently, will quickly rise to the top. This requires ongoing adaptation and development.
