Whether you’re building a decentralized application (dApp), launching a Web3 protocol, or managing a traditional SaaS platform, understanding real user behavior is essential for sustainable growth.

What Is Real User Activity?

Real user activity refers to meaningful interactions performed by genuine users rather than bots, fake accounts, or one-time visitors. These interactions demonstrate actual engagement and value creation.

Examples include:

• Returning to use an application regularly
• Completing transactions
• Providing liquidity
• Participating in governance
• Creating content
• Referring new users
• Making purchases
• Using multiple features within the platform

Unlike vanity metrics, real activity reflects authentic adoption.

Why Vanity Metrics Can Be Misleading

Many projects celebrate milestones such as:

• One million wallet addresses
• Hundreds of thousands of followers
• Millions of transactions
• High website traffic

While these achievements may attract attention, they don’t necessarily indicate an active community.

For example:

• Wallets can be created automatically.
• Followers can become inactive.
• Transactions can be generated by automated bots.
• Website visits may last only a few seconds.

Without genuine engagement, these numbers provide limited insight into long-term success.

Key Metrics That Actually Matter

Instead of focusing solely on headline numbers, successful teams monitor indicators that reflect user behavior.

Daily Active Users (DAU)

Measures how many unique users interact with the platform each day.

Monthly Active Users (MAU)

Shows sustained engagement over a longer period.

Retention Rate

Tracks how many users return after their first visit or transaction.

High retention usually indicates that users find ongoing value.

Session Duration

Longer sessions often suggest users are actively exploring features rather than leaving immediately.

Feature Adoption

Understanding which tools users actually use helps prioritize future development.

Conversion Rate

Measures how many visitors become active participants, customers, or token holders.

Real User Activity in Web3

Tracking activity becomes more challenging in decentralized ecosystems because users may have multiple wallets and interactions occur across various protocols.

Useful on-chain indicators include:

• Active wallet addresses
• Repeat wallet interactions
• Smart contract usage
• Liquidity participation
• NFT trading frequency
• Governance voting participation
• Staking duration
• Cross-chain activity

Combining blockchain analytics with application-level data provides a much clearer picture of adoption.

The Role of Analytics Tools

Modern analytics platforms help developers understand user behavior while respecting privacy.

Common capabilities include:

• Event tracking
• User journey analysis
• Funnel visualization
• Cohort analysis
• Retention reports
• Heatmaps
• Performance monitoring
• Error tracking

In Web3, blockchain analytics platforms add visibility into wallet activity and on-chain interactions.

Why Retention Beats Acquisition

Acquiring new users is expensive.

Keeping existing users is far more valuable.

A platform with 10,000 loyal users who engage weekly often outperforms one with 500,000 one-time visitors.

Returning users:

• Generate recurring revenue
• Provide feedback
• Build communities
• Create organic marketing
• Increase network effects

Retention transforms growth into sustainability.

Privacy Should Never Be Ignored

Tracking users should never come at the expense of personal privacy.

Responsible analytics emphasize:

• Anonymous identifiers
• Aggregated insights
• Consent-based data collection
• Transparent privacy policies
• Minimal data storage

Emerging technologies such as zero-knowledge proofs (ZKPs) and privacy-preserving analytics enable platforms to measure engagement without exposing sensitive user information.

This balance is becoming increasingly important as privacy regulations continue to evolve worldwide.

Turning Data into Better Products

Collecting analytics is only the first step.

The real value comes from acting on the insights.

For example:

• High abandonment during onboarding may indicate confusing instructions.
• Low governance participation may suggest voting is too complex.
• Frequent exits after connecting a wallet could reveal poor user experience.
• Strong engagement with one feature may justify expanding that functionality.

Data-driven decisions help teams allocate resources more effectively.

The Future of User Activity Tracking

Artificial intelligence is making analytics more intelligent than ever.

Future platforms will increasingly:

• Predict user churn before it happens
• Recommend personalized experiences
• Detect fraudulent behavior automatically
• Identify growth opportunities in real time
• Optimize onboarding using behavioral insights
• Measure user satisfaction through interaction patterns

For decentralized applications, AI combined with blockchain analytics could create adaptive ecosystems that continuously improve based on genuine community activity.

Conclusion

Real user activity is the foundation of sustainable digital growth. While large numbers may generate excitement, consistent engagement, strong retention, and meaningful interactions reveal whether a platform is truly delivering value.

As Web3 and decentralized technologies continue to mature, projects that prioritize authentic user behavior over vanity metrics will be better positioned to build lasting communities, improve their products, and achieve long-term success. In an increasingly competitive digital landscape, understanding how real people use a platform isn’t just helpful—it’s essential.

The post Tracking Real User Activity: Why It Matters More Than Vanity Metrics appeared first on Smart Liquidity Research.

In an increasingly transparent, data-driven, and adversarial environment, privacy is no longer optional. It is becoming a strategic requirement for capital, coordination, and long-term sustainability. This article explores why privacy has shifted from ideology to infrastructure, how compliance and privacy are converging, and why smart liquidity increasingly values privacy-preserving systems.

Why Privacy Is Shifting from Ideology to Competitive Advantage

Public blockchains introduced radical transparency, enabling trustless verification and open participation. However, as crypto markets matured, the downsides of total transparency became clear:

• Trading strategies are publicly exposed

• Wallet activity can be clustered and profiled

• Large transactions are front-run or exploited

• Economic behavior becomes predictable and extractable

For sophisticated market participants, this creates real costs. Information leakage directly translates into lost alpha.

Privacy, therefore, is no longer about hiding activity—it is about protecting economic intent. Institutions, DAOs, market makers, and long-term capital need environments where strategy, coordination, and execution are not penalized by visibility. In this context, privacy becomes a moat that preserves competitive advantage and capital efficiency.

Privacy vs Compliance: Where the Balance Is Forming

The assumption that privacy and regulation are incompatible is increasingly outdated. Modern crypto systems are moving toward a more nuanced model: privacy by default with selective disclosure.

This approach enables:

• Confidential transactions with provable compliance

• Verifiable behavior without exposing full transaction history

• Auditability without continuous surveillance

Rather than choosing between transparency and privacy, the emerging architecture allows participants to prove that rules are followed without revealing unnecessary data. This model aligns closely with regulatory objectives while preserving the economic integrity of crypto systems.

Privacy as Infrastructure: ZK, Messaging, and Private Transactions

Privacy is no longer an application-layer feature—it is becoming core infrastructure.

Zero-Knowledge Proofs (ZK)

ZK technologies allow participants to prove statements without revealing underlying data. This enables private transfers, confidential balances, and compliance proofs without exposing sensitive information.

Private Messaging and Coordination

Economic activity depends on coordination. Private, censorship-resistant messaging is critical for DAOs, traders, and cross-border organizations to function without reliance on centralized platforms.

Private Transactions and Execution

As MEV and front-running intensify, private transaction execution protects trade intent, order size, and timing—particularly for large liquidity providers whose visibility can distort markets.

Together, these primitives form the backbone of privacy-first crypto infrastructure.

How Smart Liquidity Values Privacy Primitives

Smart liquidity evaluates privacy through a pragmatic lens. The key question is not anonymity, but economic efficiency.

Privacy-preserving systems offer:

• Reduced information asymmetry

• Better execution quality

• Lower extractive costs

• Greater long-term capital participation

As a result, liquidity increasingly concentrates in environments where capital can operate without signaling intent or exposing strategy. Over time, this creates a reinforcing loop: privacy attracts liquidity, and liquidity deepens the privacy moat.

Key Dimensions of Privacy as a Crypto Moat

Future Outlook

As on-chain data becomes easier to analyze and AI-driven surveillance accelerates, the cost of operating without privacy will continue to rise. The next phase of crypto will favor systems that combine credible privacy with verifiable integrity.

Privacy will not disappear under regulation—it will mature, professionalize, and become embedded into the foundations of digital finance. In that world, privacy is not a luxury. It is the price of participation.

The post Privacy as the Ultimate Moat in Crypto appeared first on Smart Liquidity Research.

Because here’s the uncomfortable truth: perfect transparency is alpha poison.

Transparency vs. Strategy: The Hidden Tradeoff

In traditional finance, traders don’t livestream their positions. Hedge funds don’t publish their limit orders in real time. Market makers don’t expose their playbooks for fun. Privacy isn’t secrecy—it’s protection.

In DeFi, however, your wallet is your trading desk. When you open a position, rebalance a vault, or deploy capital into a new protocol, anyone—from MEV bots to copy traders—can watch, predict, and front-run your next move. Transparency stops being a virtue when it turns your strategy into public infrastructure.

This isn’t hypothetical.
It’s happening every block.

MEV, Surveillance, and the Cost of Being Visible

Maximal Extractable Value (MEV) didn’t emerge because DeFi is broken—it emerged because DeFi is too observable. Bots exploit transaction ordering, sandwich trades, and drain value from users simply because they can see everything before it settles.

And it’s not just bots. Funds track whale wallets. DAOs monitor competitors. Airdrop farmers reverse-engineer behavior. The result? A surveillance economy where the most successful players are the least visible ones.

Ironically, the more capital you manage, the more dangerous transparency becomes.

Privacy Is Not Anti-Compliance

Let’s kill a common misconception: privacy ≠ crime.

Financial privacy has always existed—bank secrecy, dark pools, OTC desks, private credit markets. DeFi didn’t invent transparency; it overcorrected. What’s happening now is a recalibration.

Modern DeFi privacy tools aren’t about hiding from the system. They’re about hiding within the system:

• Selective disclosure instead of total exposure
• Private transactions with verifiable correctness
• Shielding balances while preserving auditability

Zero-knowledge proofs, private mempools, shielded pools, and intent-based execution are turning privacy into a modular layer, not a shady workaround.

Privacy as Infrastructure, Not a Feature

Early DeFi treated privacy like an add-on. Nice to have. Optional. Something you toggle on if you’re paranoid.

That era is over.

Privacy is becoming foundational infrastructure, just like:

• Oracles for data
• Layer 2s for scalability
• Account abstraction for UX

Protocols that ignore privacy will leak value. Users will route around them. Liquidity will migrate to environments where strategies don’t get griefed in public.

The next generation of DeFi won’t ask, “Why do you need privacy?”
It will ask, “Why would you deploy without it?”

The Endgame: Transparent Systems, Private Actors

Here’s the balance DeFi is converging toward:

• Protocols stay transparent (rules, code, governance)
• Users stay private (intent, strategy, timing)

That’s how real financial systems work. That’s how capital scales. And that’s how DeFi graduates from an experimental playground into serious global infrastructure.

Transparency didn’t fail DeFi.
It just went too far without guardrails.

And in markets, if everyone can see your move before you make it, you’ve already lost.

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The post Privacy in DeFi Is No Longer Optional—It’s Infrastructure appeared first on Smart Liquidity Research.

As the defense sector accelerates adoption of AI and software-defined capabilities, the need for embedded, mathematically verifiable assurance is no longer theoretical—it’s operationally essential. Lagrange’s inclusion reflects this shift and signals growing institutional recognition of ZK-powered cryptographic proof systems as foundational to trusted autonomy.

DeepProve: ZK Verification for Critical Defense Workflows

DeepProve introduces cryptographic verification directly into AI-driven subsystems and telemetry pipelines. Using zero-knowledge proofs, it validates that outputs from AI models and mission software are derived from authorized logic, using untampered inputs, without requiring access to proprietary code or sensitive data.

In practical terms:

• AI-enabled systems can attach proofs of correctness to their outputs

• Defense operators can validate decisions without revealing internal system logic

• Entire workflows—from targeting to threat detection—can be cryptographically audited

This evolution is especially relevant for complex, dynamic environments like missile defense, battlefield intelligence, and autonomous surveillance platforms, where both correctness and accountability must be guaranteed under adversarial pressure.

Lagrange is now a registered supplier in Raytheon Technologies’ (@rtx_news) vendor network, allowing Raytheon teams to discover and evaluate Lagrange’s DeepProve as part of their supplier ecosystem.

Learn more: 🧵 pic.twitter.com/hU3WY7wyt5

— LAGRANGE (@lagrangedev) November 26, 2025

Securing the Full Lifecycle: From Firmware to Fire-Control

Defense systems increasingly operate within distributed, software-rich ecosystems that extend far beyond any single battlefield platform. This includes firmware updates, sensor telemetry, and supply-chain data flows—all of which must remain uncompromised to ensure mission integrity.

DeepProve offers an additional security layer for these lifecycles:

• Verifies authenticity of firmware and system updates

• Detects tampering in telemetry or sensor data streams

• Enforces proof-backed compliance across distributed defense networks

By doing so, Lagrange helps prevent corrupted inputs, malicious firmware injections, or insider threats from undermining critical systems.

Airborne and Naval Applications: Verifiable Mission Assurance

Platforms operating in the air and at sea—such as UAVs, fighter jets, and maritime patrol systems—face uniquely complex operating conditions. Here, DeepProve enables provable assurance that AI-driven systems adhere to mission constraints and safety envelopes during execution.

Key advantages include:

• Onboard AI can prove adherence to routing, detection, and engagement policies

• Operators and command centers gain mathematically grounded confidence in AI behavior

• Mission logs include cryptographic evidence of system performance

This significantly reduces the risks associated with model drift, sensor anomalies, or unauthorized overrides, while enhancing command transparency and accountability.

Accelerating Certification and Technical Review

Defense platforms must navigate rigorous certification processes to meet regulatory and contractual standards. DeepProve supports these efforts with reproducible, tamper-evident artifacts that simplify technical validation.

With cryptographic proofs, programs can demonstrate that:

• AI components followed authorized logic

• Data was processed according to approved standards

• No upstream manipulation or firmware compromise occurred

This reduces certification friction, streamlines technical review cycles, and enhances trust between defense contractors and government stakeholders—all without disclosing sensitive IP.

A New Security Paradigm for Trusted Autonomy

The future of defense autonomy will not rely solely on performance—it will be built on verifiable trust. Lagrange’s approach enables systems not only to operate autonomously, but also to prove, in cryptographic terms, that they did so correctly.

By entering Raytheon Technologies’ supplier network, Lagrange gains a strategic foothold in one of the most advanced defense ecosystems in the world. This partnership reflects a broader movement toward trusted AI, zero-trust architecture, and cryptographic accountability in national defense operations.

About Lagrange Labs

Lagrange is building the cryptographic backbone for verifiable computing. Through its DeepProve framework, the company delivers zero-knowledge proof systems that bring transparency, auditability, and trust to AI-powered systems across finance, defense, and industrial automation.

🔗 Website: https://www.lagrange.dev/
📣 Twitter (X): https://x.com/lagrangedev

About Raytheon Technologies

Raytheon Technologies is a global aerospace and defense company delivering advanced systems and services for commercial, military, and government customers. Its capabilities span missile defense, radar systems, cybersecurity, and next-generation aircraft systems.

🔗 Website: https://www.rtx.com
📣 Twitter (X): https://x.com/RTX_News

The post Lagrange Joins Raytheon Supplier Network to Deliver Cryptographic Verification for Mission-Critical Defense Systems appeared first on Smart Liquidity Research.

After a successful integration on Monad Testnet earlier in 2025, Band now enters production, delivering a crucial data layer that enables builders to deploy fully functional financial applications from Day 1. With this deployment, Band reinforces its commitment to supporting emerging high-throughput chains with secure and verifiable off-chain data.

Band x Monad: Unlocking the Next Generation of On-Chain Finance

Monad is a high-performance, EVM-compatible Layer 1 optimized for scalability, throughput, and ultra-low latency—capable of processing 10,000 transactions per second with 500ms block times and 1-second finality. But speed alone isn’t enough to power real DeFi—reliable data is essential.

Enter Band Protocol.

With its modular oracle network, Band provides Monad with:

• 🔹 Fast and low-latency price updates

• 🔹 Tamper-resistant, decentralized data aggregation via BandChain

• 🔹 Low-cost oracle access for high-frequency applications

• 🔹 Seamless compatibility via Band’s EVM Standard Reference Contracts

This allows Monad developers to build lending markets, perpetual DEXs, synthetic assets, and more—without compromising on price integrity or protocol security.

Live Price Feeds from Launch

At launch, Band offers secure and production-ready price feeds for:

• USDC

• USDT

• SOL

• ETH

• WBTC

These assets form the backbone of most DeFi systems. With accurate feeds in place, developers can immediately deploy protocols for:

• Lending & borrowing

• Liquidation engines

• Synthetic asset creation

• Perpetual trading

• Risk management tools

Band’s early presence ensures that Monad’s DeFi stack is not only fast—but also trust-minimized and data-rich.

How It Works: Band Oracle Architecture on Monad

Band’s integration on Monad leverages its Cross-Chain Oracle Network, delivering a fully decentralized data relay pipeline from BandChain to Monad Mainnet:

1. Data Sourcing – Band validators pull real-time prices from trusted sources (e.g., CoinGecko, CEX APIs).

2. Decentralized Aggregation – Through BandChain’s delegated proof-of-stake (dPoS), validators finalize consensus on each price update.

3. Data Relay – Finalized data is sent to Monad, optimized for high throughput and minimal latency.

4. On-Chain Access – Monad apps can query Band’s standardized reference contracts (via proxy) for reliable price data.

This architecture guarantees verifiability, redundancy, and resilience—with zero reliance on centralized relayers or opaque update logic.

Security First: Proven, Auditable, and Battle-Tested

Band Protocol has consistently prioritized security. With years of mainnet uptime, zero exploits, and fully auditable infrastructure, it has proven its reliability across multiple chains. Every price update on Monad is:

• ✅ Verifiable on BandChain

• ✅ Tamper-resistant

• ✅ Consensus-validated

• ✅ Publicly auditable

BandChain ensures that all oracle data delivered to Monad remains censorship-resistant and manipulation-proof, protecting protocols and users alike from oracle-based vulnerabilities.

Key Use Cases on Monad

Thanks to its low latency and cost-efficient model, Band opens the door for a wide range of next-gen use cases on Monad:

🔹 High-Frequency DeFi – Perpetual DEXs, AMMs, and trading platforms with sub-second updates
🔹 Lending & CDPs – Liquidation mechanisms relying on trustworthy asset valuations
🔹 Synthetic Assets & Stablecoins – Accurate collateral tracking and price pegging
🔹 AI-Native Agents – On-chain bots and wallets that depend on real-world prices for decision-making

By providing robust oracle infrastructure, Band enables these applications to launch securely, scale confidently, and compete globally from day one.

Start Building Today

Developers can already integrate Band oracles on Monad Mainnet and begin using validated price feeds through a standardized proxy contract:

Proxy Contract Address:
0x9c5490fc68005dF8b2DC124309c2C036B93d785f

Helpful links:

• 🔧 Monad Docs: Monad Introduction

• 📘 Band Docs: Band Documentation

• 💬 Join Band Discord: https://discord.gg/bandprotocol

• 💬 Join Monad Discord: https://discord.gg/monad

Whether building for finance, AI, or gaming—Band provides the real-time data foundation needed to scale with speed and confidence on Monad.

Looking Ahead: More Feeds, More Features, More Innovation

Band will continue expanding its coverage on Monad based on developer needs. New price feeds, custom integrations, and support for AI-driven apps are already in development.

As Monad’s ecosystem grows, so too will Band’s role as a foundational data provider—supporting DeFi, AI, and new primitives that push on-chain performance to new levels.

About Band Protocol

Band Protocol is a leading cross-chain data oracle platform that connects real-world data to smart contracts. Its flagship solution, BandChain, aggregates pricing and data feeds in a decentralized manner to power secure, scalable, and reliable Web3 applications.

🌐 Website: https://www.bandprotocol.com/
📣 Twitter (X): https://x.com/BandProtocol

About Monad

Monad is a high-throughput Layer 1 blockchain offering EVM compatibility, ultra-fast block times, and high transaction throughput—designed to support next-gen decentralized applications without compromising on speed, finality, or cost.

🌐 Website: https://www.monad.xyz/
📣 Twitter (X): https://x.com/monad

The post Band Protocol Goes Live on Monad Mainnet appeared first on Smart Liquidity Research.

That’s where artificial intelligence (AI) comes into play. By introducing smart algorithms into the crypto ecosystem, AI is reinventing how privacy is protected, making transactions more secure and anonymous without sacrificing performance.

Understanding Crypto Privacy Challenges

Cryptocurrencies like Bitcoin and Ethereum operate on public blockchains. Every transaction is recorded on a ledger that anyone can access. While wallet addresses don’t directly reveal personal identities, blockchain analysis tools can often link these addresses to individuals through transaction patterns and off-chain data.

This creates a critical issue: pseudonymity is not the same as anonymity. Over time, even so-called “private” users can be de-anonymized.

The Role of AI in Enhancing Privacy

Artificial intelligence is stepping in as a powerful tool to combat these vulnerabilities. Smart algorithms—particularly those rooted in machine learning (ML) and deep learning (DL)—are being used to create more advanced privacy-preserving techniques.

Here are some of the main ways AI is helping:

1. Anomaly Detection for Enhanced Obfuscation

AI models can learn transaction patterns and automatically identify “normal” versus “suspicious” activity. Privacy-focused wallets and platforms can then use this data to simulate realistic transaction behaviors, making it harder for observers to distinguish real user activity from noise.

2. AI-Driven Mixers and Tumblers

Traditional coin-mixing services group transactions to obscure their origins. However, AI enhances this by intelligently selecting transaction pairs and timing to maximize obfuscation while reducing the risk of detection by blockchain analysis tools.

3. Zero-Knowledge Proof Optimization

Zero-knowledge proofs (ZKPs) allow one party to prove the truth of a statement without revealing the underlying information. AI algorithms are now being used to optimize ZKP computations, making them faster and more energy-efficient—especially critical for privacy coins like Zcash or zkSync.

4. Adaptive Privacy Settings

Smart algorithms can analyze a user’s transaction history, behavioral data, and threat landscape to offer customized privacy configurations. This allows users to strike the right balance between performance, cost, and anonymity.

Real-World Applications

Several projects and platforms are already using AI to improve crypto privacy:

• Tornado Cash Alternatives: As regulators scrutinize tools like Tornado Cash, new decentralized apps are emerging that use AI to better simulate user behaviors and obfuscate transaction trails.
• PIVX: This privacy coin is exploring ways to integrate AI into its network, possibly enhancing its zk-SNARK-based privacy protocol.
• Monero Research Lab: While not AI-centric, its ongoing research into transaction anonymity provides a solid foundation that AI tools can build upon.

Risks and Ethical Considerations

While AI enhances privacy, it can also be a double-edged sword. Malicious actors could use AI to develop more advanced tracking tools or to automate money laundering. That’s why responsible development, transparency, and open-source auditing are essential in this space.

The Future of AI-Enhanced Crypto Privacy

We are at the beginning of a privacy revolution powered by artificial intelligence. As blockchain ecosystems evolve and regulatory scrutiny increases, AI tools will become essential in preserving financial freedom and privacy rights.

Privacy is not just a feature—it’s a fundamental human right. The AI shield offers a promising path forward, making crypto not only smarter but safer for everyone.

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The post The AI Shield: How Smart Algorithms Are Reinventing Crypto Privacy appeared first on Smart Liquidity Research.

Enter Next-Gen MEV: Untraceable & Profitable.

Backed by cutting-edge innovations in cryptographic obfuscation, private relays, and stealth mempools, the new breed of MEV strategies promises to deliver maximum profit with minimum traceability. Forget the noisy sandwich attacks or front-running visible to all on-chain sleuths—this wave is about invisible arbitrage and undetectable alpha.

What Makes Next-Gen MEV Different?

1. Privacy-Enhanced Execution

• Through integration with zero-knowledge proofs and privacy-centric blockchains, trades are no longer exposed to frontrunners or bots.
• Techniques like sealed bid auctions and private ordering protocols make it near impossible to decode intent before execution.

2. Hyper-Efficient Profit Capture

• With optimized gas usage, real-time block prediction, and mempool surveillance, next-gen bots extract value with surgical precision.
• Dynamic reordering mechanisms and latency-reduction algorithms help secure the best possible slippage and execution.

3. Untraceability

• Thanks to tools like MEV-Share obfuscation layers, Tornado-inspired mixing routes, and stealth wallets, tracking MEV flows is harder than ever.
• These tools don’t just cloak the actor—they obscure the action itself.

Why Should You Care?

Whether you’re a DeFi trader, builder, or investor, MEV affects your bottom line. But with the rise of untraceable MEV, it’s not just about protecting your transaction—it’s about participating in the profits of a stealthier, smarter system.

This evolution isn’t just an arms race. It’s the beginning of a new paradigm where profit and privacy go hand in hand.

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🌐 Want to Dive Deeper?
Stay ahead of the curve by following the latest insights from the experts:
📎 Smart Liquidity Research — Smartliquidity.info
⚡ Smart Crypto News — @SmartCryptoNew1 on X

The post Next-Gen MEV: Untraceable & Profitable — A Glimpse Into the Future of On-Chain Arbitrage appeared first on Smart Liquidity Research.