Marketing Automation

Humanization vs. Personalization What’s the Difference?

5 mins read
July 31, 2019
By
Total Expert

Financial services organizations taking a customer-centric approach are quickly setting themselves apart from competitors. And personalization, hyper-personalization and humanization are all at the forefront of implementing this approach to improve the customer experience.  

However, nowadays, these terms are thrown around so often it’s hard to know the difference between the three – and the benefits they each derive.

It’s time to set the record straight once and for all.

This post will dive into the true meaning of personalization, hyper-personalization and humanization – and how financial brands can leverage these techniques to grow their business.

Personalization

Personalization considers high-level elements such as a consumer’s name, location and purchase history. Personalization in its most basic form may include a customer’s first name in the beginning of an email or at the top of the landing page.  

Keep in mind, however, that email is just one of the many channels consumers use. Personalization techniques can be applied to social media, landing pages, blogs and more.

Personalization is the first step in offering a user experience tailored to the individual as opposed to a cold interaction fit for the masses. After all, no one likes to feel like they are receiving the same email that 10,000 other people just received.

The goal of personalization is to encourage consumers to take the next step, such as opening an email or clicking a link. In a sense, it is warming consumers up to trusting your brand in hopes that they will continue moving down the path to transact.  

Done well, personalization can be quite successful. Eighty percent of customers are more willing to transact with brands that offer a personalized experience. Furthermore, 93 percent of companies successfully convert prospects if a personalized marketing strategy is at play.  

However, to truly capture consumers’ attention and make it known that they are more than a number, companies need to go deeper than that – with hyper-personalization.

Hyper-Personalization

Hyper-personalization takes personalization to the next level. Hyper-personalization leans into machine learning and artificial intelligence to provide greater insights to consumer behavior and buying patterns.  

For example, a customer opens and engages with three different emails about opening a new credit card, but they haven’t yet taken the plunge and applied for the card. With the help of a Marketing Operating System (MOS), an email can be triggered the next day with a pre-filled out application encouraging the user to apply.

A study by Selligent Marketing Cloud found that 74 percent of consumers expect to be treated as an individual rather than a segment. By leveraging real-time data, financial brands develop a better understanding of their customers and their needs. This in turn lets your customers know you value them on a personal level, which builds their trust and the likelihood of repeat business.  

Hyper-personalization is more successful than traditional personalization techniques because intelligent automation not only sends communication on behalf of customer-facing team members, but it engages prospects and customers with highly relevant content at exactly the right time and on the right channel.  

With hyper-personalization, timing is everything. When you think about hyper-personalization, you should also think about being hyper-relevant. Reaching out at pivotal moments during the customer journey can make or break the duration of your relationship.  

Humanization

We’ve said it before. People want to do business with people – not with business entities or screens.  

A machine might be able to tell you to pick up the phone and call someone, but it can’t make the call for you. Companies don’t complete transactions that turn into lifetime relationships – people do.  

Strong humanization strategies leverage technology to enhance relationships. Technology saves customer-facing teams time from sifting through hundreds of messages to know when a follow up is needed. This is the first pillar of humanization. When a member of your customer-facing team reaches out personally (due to a trigger from your software solution – hyper-personalization at work) it reinforces the idea that there is a person behind the screen.  

But how do you bring your people – producers, prospects and customers alike – to the forefront of your digital strategies – even when a human interaction hasn’t occurred?

This brings us to the second pillar of humanization. It’s instilling your company’s brand, voice and the people behind the screens into all elements of the customer journey. No matter what channel of communication your prospect or customer is engaging on, it’s as if they are engaging with the same person (and the same brand).  

Humanization increases consumer trust, which in turn boosts customer advocacy and retention.

Proper execution of humanization – at scale – requires the support of technology to deploy hyper-relevant messaging and engagement that stays consistent with your brand identity. The right technology keeps customers engaged at all times while still allowing financial brands to include that “personal touch.”  

Conclusion

Personalization, hyper-personalization and humanization all play a role in optimizing the customer experience. The next time you’re considering weaving one of these techniques into your marketing strategy, keep in mind the goal of each and at what stage of the customer journey it will be most effective.

Financial services organizations that leverage these techniques will not only set themselves apart from competitors, they will establish themselves as industry leaders and become the trusted brand consumers trust – for life.

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AI is no longer a future state—it’s already here, embedded in everything from ride-sharing apps and food service to factories and farms. In the world of financial services, though, this ubiquity comes with pressure to integrate AI fast, appear innovative, and keep up with competitors—all while being mindful of evolving federal and state compliance requirements. Moving fast without a plan or awareness of up and downstream implications often leads to AI-enabled solutions that either underdeliver or don’t deliver at all.

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Let’s make one thing clear: not all AI is created equal.  

Chatbots have been commonplace in financial services for a decade now, but remain rigid, rule-based tools that handle repetitive tasks.  I’ve worked with “AI” services for more than 15 years and each had their own place and potential when used properly. Herein lies the opportunity. Modern lenders that are focused on retaining and growing their customers in an ultra-competitive market need something more dynamic. Enter AI agents that can understand context, adapt on the fly, and speak in a human-like way. These agents are coachable, brand-aware, and learn from every interaction. They don’t follow scripts—they think in real time. And when built correctly, they become a seamless part of your customer experience.

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For example, when mortgage rates dropped for a few weeks in September 2024, our customer intelligence capabilities identified nearly $2 billion in immediate refinance opportunities. But no team of loan officers could scale quickly enough to reach every qualified lead. That’s where AI tools prove invaluable—automating first-touch outreach at scale, surfacing the best opportunities, and empowering human teams to scale up execution to drive retention and growth.

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The types of AI-enabled solutions we are talking about can’t function effectively in isolation. Without access to timely and accurate customer data, and invoked within a specific workflow process, it can’t personalize interactions, anticipate needs, or drive conversions at the right time.

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Generalist AI offerings can be a gamble that increase costs—and time to value

Implementing AI that’s not purpose-built for financial services introduces two major risks:

1. Usability failure: Your team must spend months customizing and configuring a generalist AI tool to make it work for your specific needs—if it will ever work at all. For example, imagine you’re a loan officer and one of your referral partners introduces you to a borrower. Now, you have to choose the best way to approach the first conversation with this borrower. There are countless permutations of questions and answers which all require deep personalization, compliance awareness, and consistent representation of the sales processes and brand tone of the lender. Generalist AIs will quickly reach their limitations in these complex use cases.

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2. Compliance risk: Without built-in industry guardrails, you’re gambling with regulatory violations and brand safety.  As we know, the compliance landscape for financial services is broad and evolving at the federal and state level.  Look for AI offerings that are regulatory aware and enable you to configure them based on your organization’s risk tolerance and interpretations.

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Ask these questions before you commit to an AI offering  

To maximize the probability of success, here’s a quick checklist for vetting solutions:

  • Can it solve a real, high-value business problem, and how? Review specific examples and ask to speak with other organizations that have implemented the tool.
  • Does it function as a true AI agent, not a static bot?
  • Can it be deeply integrated into your core system(s), workflow orchestration, and data?
  • Does it include financial industry compliance and brand guardrails?
  • Can it scale without sacrificing quality or regulatory integrity?

Building the future with purpose-built AI

Total Expert has always designed technology with financial services in mind, and our approach to utilizing AI is no different. We’re not chasing hype. We’re solving problems.

Our focus on AI isn’t simply building standalone features—it’s about embedded, intelligent, and deeply integrated AI solutions. It’s helping lenders scale smarter, engage more meaningfully, and turn data into action. Our AI Sales Assistant is just the beginning—an example of how purpose-built, AI-enabled solutions can solve real problems and deliver tangible value. We are already testing and exploring other AI-enabled solutions and I could not be more excited about the current and potential value our clients and our market will achieve.

Because when AI works, it’s not just impressive—it’s indispensable.

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