Problems of Customer Experience in Web3: Unmasking the Paradox of the Crypto World

Published on May 30, 2023

Imagine standing in front of a door that leads to a world full of possibilities – a world that promises unprecedented freedom, privacy, and decentralization. But as you reach out to turn the knob, you find it’s not a knob at all. It’s a complex puzzle, requiring a nuanced understanding of sophisticated algorithms, codes, and terminologies. This is the current state of the crypto world, the Web3, where the promise of a revolutionary digital frontier is held back by poor customer experience (CX).

The world of Web3 and cryptocurrency offers a disruptive platform that challenges the traditional financial system’s very fabric. Yet, paradoxically, it struggles to provide an intuitive, user-friendly experience, crucial for mainstream adoption. This article examines the reasons behind the poor customer experience in Web3 and explores strategies to transform this landscape.

Web3 has solved a lot but there’s still a lot to solve in Web3

Dilemma of Decentralization and CX

Decentralization, the cornerstone of Web3, empowers users with greater control and privacy. However, it simultaneously presents unique challenges to crafting a seamless user experience.

1. Complexity of Interaction

The inherent complexity of blockchain technology poses a significant barrier to entry for non-tech-savvy users. Even simple interactions, such as setting up a wallet or understanding a transaction’s status, can be arduous tasks.

For instance, to use a decentralized exchange (DEX) like Uniswap, users need to first understand the concept of a MetaMask wallet, gas fees, and how to connect their wallets to the DEX. This complex process often deters potential users, contributing to a sluggish adoption rate.

2. Lack of User Support

In a traditional centralized system, a customer service team is available to resolve user issues. In contrast, Web3’s decentralized nature lacks a centralized authority or support system. Users are expected to solve problems independently, often requiring extensive research and technical knowledge.

A report by The Defiant indicated that 74% of new crypto users find it challenging to navigate this landscape without adequate support.

3. Security Concerns

While Web3 enhances user privacy, it also exposes users to potential security risks. Unlike traditional banking systems, transactions on the blockchain are irreversible. If a user loses access to their wallet or falls prey to scams, there’s no centralized authority to reverse the transaction or recover the lost assets. A study revealed that about $10 billion worth of cryptocurrency was stolen in 2022.

Table: Annual Cryptocurrency Stolen (2018-2022)

Year	Cryptocurrency Stolen (in USD billion)
2018	1.7
2019	4.5
2020	3.8
2021	7.6
2022	10

The need for users to manage their own security often creates a stressful experience, further deterring mainstream adoption.

Tracing the stolen assets is one of the biggest challenges in Web3

4. Volatility and Unpredictability

The crypto market’s volatility often results in unpredictable transaction costs, mainly due to fluctuating gas fees. This unpredictability creates an unstable environment, causing confusion and frustration among users. In 2021, the Ethereum network, one of the most popular blockchains, saw its average transaction fee spike by over 300% in just a month[^3^].

Clearly, the crypto world is entangled in a paradox. While it offers a path to a decentralized, democratic future, it struggles with a complex, unpredictable, and often stressful customer experience.

But this doesn’t mean that the situation is hopeless. With the right strategies, the community can revolutionize the customer experience in Web3.

Strategies to Improve CX in Web3

Education and Simplification

The complex nature of blockchain and cryptocurrency needs to be broken down into simpler, more accessible terms. Comprehensive educational resources, like interactive guides, explainer videos, and user-friendly blogs, can help demystify the crypto world.

Coinbase, a leading cryptocurrency exchange, is a notable example. It uses Coinbase Earn, an educational program that rewards users for learning about different cryptocurrencies1. This initiative not only educates users but also incentivizes learning, making the process enjoyable and beneficial.

Community Support and Engagement

Building robust community support systems can significantly improve CX in Web3. Forums, social media groups, and chat platforms can be invaluable resources for users to learn, share experiences, and troubleshoot problems.

Discord and Reddit communities are thriving examples of such support systems in the crypto world. They provide platforms for users to interact, exchange ideas, and assist each other, fostering a sense of community and shared purpose2.

Enhanced Security Measures

Enhancing security measures is crucial to instilling confidence in users. This could involve developing more secure wallet options, implementing multi-factor authentication, and educating users on safe practices.

“Security is not just a feature, it’s a fundamental aspect of any digital platform. In the realm of cryptocurrency, it’s a critical pillar of customer experience,” says David Schwartz, CTO at Ripple3.

Predictability and Stability

While complete stability might be unrealistic in the dynamic crypto market, efforts can be made to mitigate extreme volatility. Layer 2 solutions, like Optimism and zkSync, can help provide more predictable transaction costs by reducing dependence on gas fees4.

Wrapping Up

The crypto world’s promise of a decentralized future is currently overshadowed by poor customer experience. However, through concerted efforts in education, community support, enhanced security, and market stability, the Web3 landscape can be transformed into a more user-friendly platform.

The journey towards improving CX in Web3 is one of constant evolution, and at Mantra Labs, we’re committed to being a part of that journey. With our deep expertise in creating seamless digital experiences, we’re ready to help businesses navigate and thrive in this exciting new frontier of Web3.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled