Just about every company has a strategy to provide personalized banking, and making interactions more personal and tailored to their current account holders, as well as prospective ones. The logic is that by creating a more personalized appeal, there’s a higher likelihood of converting at a higher rate.
That’s ONE Way To Think About Personalization
For example, by inserting the account holder’s name, i.e., “Hey [Jane Thompson] We Have Great Deals for You!”, the expectation is that the offer will feel like it’s more special, or more customized to that individual.
How The Science of Digital User Behavior Offers a NEW Way to Personalize
What we’ve been learning based on our research at Glia is that using ideas like the above example is the wrong way to approach personalized banking when it comes to actual customer service interactions–situations in which a user needs something or is trying to resolve a problem. These constitute the overwhelming percentage of interactions your company is having with your account holders.
Now let’s explore the obvious question: What IS the right interaction? There are so many ways to interact, including via the following channels:
Given all those choices…which one is best for any given user at a given moment?
And after a career of studying this question, the research points to a single answer: It depends. The most unsatisfactory answer of all, right? It depends. But now we know what it depends on, and this knowledge can automatically trigger interactions that are consistently more efficient for companies and–as it turns out–create a higher quality, lower-effort experience for account holders.
Let’s explore four different scenarios, and what type of interaction would be the best choice:
And we’ve now learned that accurately “matching” the right interaction to each user situation is surprisingly easy to execute, user after user. If your organization could do that on a regular basis, you’d be far more efficient, and also building strong account holder loyalty.
In the past, the focus of account holder interactions has been centered on which channel the user is using to contact you. If you were to picture a typical contact center: There’s a bunch of people on the phone over here, and there’s some digital or chat people over there, and there’s a bunch of other IT people working on self-service and artificial intelligence (AI) solutions a few floors away.
Prior to the adoption of a ChannelLess® option, companies have been forced to operate in a channel-centric environment. There really wasn’t a choice. But now that any interaction can be seamlessly transitioned to/from any form of communication and collaboration within moments of the initiation of the contact, the channel isn’t the issue any longer.
The issue is whatever this account holder needs at this moment. That’s WHY they’re contacting you.
The research shows that out of all the myriad needs different account holders have at different times in their lifecycle, virtually ALL of them fit into one of just four categories. Think for just a minute about the variation of needs different users are having when they’re interacting with you:
These factors create a grid called the 4 Categories of Need. Once you’ve determined which category a given user’s issue falls into, you can now seamlessly transition them to the interaction type that’s the most efficient and the most effective and the best experience.
Today, it would seem to be harder to instantly detect each account holder’s need, but many companies have been surprised that it’s actually easier to ascertain specific issue types–the reason they are contacting you–among online users than with traditional phone-first users. Here are three best practices to address this:
Can you start to see how easy it would be to offer each user the perfect interaction type once you know the exact thing they need? There are only 4 categories, and the specific type of interaction that’s best for each is entirely predictable. This is the science behind the practice of Unified Interaction Management (UIM). Glia has powered over 5 billion interactions between financial services companies like credit unions and their customers and members. The ability to correctly “match” each user’s need to a specific interaction type is successful at a very high rate of accuracy (Glia 2024 Interactions Report).
Here’s one way to think about it: Imagine the same account holder contacts you two days in a row, but they are greeted with two completely different interaction types–each of which is exactly right for that specific need:
Tuesday: An account holder wants to report suspicion of fraud on their account, and they go to your website. Since we already know that this kind of need is best handled through a live conversation with an agent, once the account holder is logged on they are immediately offered an opportunity to speak with a representative through OnScreen Voice. All they have to do is touch a button on their screen. The representative who joins them already knows their identity (since they have been authenticated) and likely knows their issue as well (fraud), and are already working to solve it.
An account holder wants to report suspicion of fraud on their account
“Hi, Mr. DeLisi, I’m Sarah, and I’m going to help you with this potential fraud situation and make sure you’re fully protected, OK?”
“Wow, [your institution] is on it! This is the kind of excellent service I deserve. [I made a great choice to choose these guys compared to their competitors!]”
But just a day later…
Wednesday: The same account holder wants to send a wire transfer. We know this doesn’t require a conversation, it can be handled through messaging, and in fact, this issue can be perfectly executed through automation.
A dialogue box pops up on the user’s screen, and a bot offers the following:
“It looks like you’re trying to make a wire transfer. Is that correct [Y / N]?”
If yes, the bot guides the user to the self-service portal where they can enter their information, and send the wire. They get an instant confirmation that it’s been executed, and a reminder that they will also get an email in about an hour to doubly confirm the details. No humans were required, and everything was resolved in less than a minute.
The bot guides the user to the self-service portal where they can enter their information
“That was really simple. I didn’t even have to call in to execute the transfer. [I should consider using this institution for more of my banking needs!]”
So what happened here? These were two different interactions with the same user, but the same exact positive reaction to each, because each interaction type was perfectly matched to their need at that moment.
There isn’t a company in the world that hasn’t said, “One of our biggest goals–and a true differentiator–is that we strive for consistently excellent service.” That should be EVERY company’s goal, right?
But with today’s heightened digital expectations, it’s time to refine what “consistency” truly means. What it doesn’t mean is “I get the same exact experience every time I interact with you.”
What it should mean is “Every time I contact you I get the exact thing I need–sometimes messaging, sometimes a live conversation, sometimes self-guided, but presented in a totally effortless, intuitive way. [Your company] always seems to know exactly what I need!”
That kind of consistency was never possible before. But now that it is, why would you allow yourself to still be limited to a channel-centric strategy that actually costs more than the far more efficient practice of UIM?
At the end of the day, great service isn’t about which channel someone uses—it’s about giving them exactly what they need, right when they need it.
With the right customer service tools in place, financial institutions can automatically detect user intent and guide each interaction to the most efficient and effective path.
That’s the real win in personalized banking—and now, it’s finally achievable within your digital banking solutions.
