Attribution in the Privacy Era: On-Chain Wallets vs. Off-Chain IDs

The death of third-party cookies didn't just disrupt Web2 marketing—it created an existential crisis for growth teams. But Web3 companies have stumbled onto something Web2 marketers are desperately chasing: a native, privacy-preserving attribution system built into the blockchain itself.

In 2026, the competitive advantage isn't who collects the most data. It's who can connect the dots between a user's first Twitter click and their $50,000 liquidity pool deposit—without ever asking for their email address. This is the promise of on-chain attribution, and it's fundamentally reshaping how growth teams measure, optimize, and scale Web3 products.

The challenge? Most teams are still operating with Web2 mental models. They're trying to retrofit cookie-based attribution onto wallet-based ecosystems, losing signal in the process. Meanwhile, the best-performing Web3 growth teams have already built sophisticated attribution stacks that treat wallet addresses as the primary user identifier and blockchain transactions as the source of truth.

This guide walks you through the exact framework, tools, and economics that separate high-performing growth teams from the rest.

The Attribution Crisis in Web3: Why Traditional Methods Fail

The Web2 Attribution Graveyard

Traditional attribution relies on a fragile stack: cookies, pixels, email addresses, and user accounts. Each layer introduces friction and data leakage. For Web3 products, this approach breaks down almost immediately.

Web3 users don't log in with identifiable accounts. They connect wallets—pseudonymous, portable, and completely outside your platform's control. A user might discover your protocol on Twitter, research it on Discord, and then execute their first transaction from a hardware wallet they've never connected to your website. Traditional attribution tools see three separate users. You see one customer journey.

The privacy regulations accelerated this collapse. GDPR, CCPA, and emerging Web3-specific regulations made personal data collection increasingly expensive and legally risky. For SaaS companies, this meant rebuilding attribution infrastructure from scratch. For Web3 companies, it meant recognizing that the blockchain itself is the attribution infrastructure—if you know how to read it.

Why Wallet-Based Attribution Changes Everything

A wallet address is a permanent, pseudonymous identifier that lives on an immutable ledger. Every transaction is recorded. Every interaction is timestamped. Every action is verifiable without requiring the user to surrender personal information.

This creates three fundamental advantages:

1. Accuracy Without Invasiveness

On-chain attribution systems achieve 85-95% accuracy by correlating wallet actions with off-chain touchpoints using behavioral analysis, timing correlation, and opt-in identity linking.[1] You're not guessing whether a user converted—you're reading the blockchain.

2. Cross-Protocol Visibility

A single wallet address can be tracked across multiple DeFi protocols, NFT marketplaces, and governance systems.[4] You see the complete user journey: which protocols they interact with, how much capital they deploy, how long they hold, and whether they participate in governance. This depth of insight is impossible in Web2.

3. Privacy-First Compliance

Wallet-based attribution doesn't require personal data collection.[4] You're analyzing public blockchain data that users have already voluntarily published. This eliminates the regulatory burden that's crippling Web2 growth teams and builds trust with privacy-conscious users.

The 2026 Attribution Landscape: Key Benchmarks and Trends

Current State of On-Chain Attribution Adoption

The 2026 Web3 growth landscape is bifurcated: leaders have built sophisticated attribution stacks, while laggards are still relying on vanity metrics.

Adoption Metrics:

• 85-95% accuracy achievable with proper implementation of multi-method identity resolution[1]
• 30-day attribution windows are the standard for correlating off-chain touchpoints with on-chain conversions[1]
• Multi-touch modeling is now table stakes for competitive DeFi and NFT projects, with teams weighting 3-5 off-chain touchpoints (newsletter opens, Twitter clicks, Discord activity) against final on-chain actions[1]

The Speed vs. Finality Trade-off

One critical distinction shaping 2026 strategy: the choice between on-chain and off-chain transaction recording.

On-Chain Transactions:

• Slower confirmation times (depends on network congestion)
• Higher fees (variable based on network conditions)
• Permanent, immutable records
• Publicly visible on blockchain explorers
• Trustless verification—no intermediary required[3][5]

Off-Chain Transactions:

• Instant execution without blockchain confirmation[3]
• Minimal or zero fees[3]
• Limited transparency (recorded only in platform database)
• Faster user experience for frequent, small transactions[3]
• Introduces counterparty risk—you must trust the platform's record-keeping[2]

For attribution purposes, this distinction matters enormously. Off-chain transactions between users on the same platform are recorded in that platform's centralized database, not on the blockchain.[2] This creates a critical gap: you can't verify off-chain transactions independently, and you lose visibility into user behavior that happens outside your platform.

The 2026 Best Practice: Use off-chain transactions for user experience (fast, low-friction onboarding and frequent micro-transactions) but anchor your attribution model to on-chain transactions, which provide verifiable, permanent records.[3]

Emerging Trends Reshaping Attribution Strategy

1. Tokenized Debt and On-Chain Origination

Asset managers and protocols are increasingly originating loans on-chain rather than tokenizing off-chain debt.[6] This trend matters for attribution because it expands the types of on-chain actions you can track. In 2026, user journeys increasingly include not just token swaps and staking, but lending, borrowing, and collateral management—all on-chain and all attributable.

2. Cross-Protocol DeFi Activity Tracking

The ability to track a single wallet across multiple protocols is becoming a competitive necessity.[4] Users no longer interact with isolated platforms; they compose protocols. A user might deposit collateral on Protocol A, borrow against it on Protocol B, and deploy the borrowed assets on Protocol C. Your attribution model must capture this entire journey to understand true user value.

3. Governance and Long-Term Engagement Signals

Governance participation (voting, proposal submission, delegation) is increasingly recognized as a high-signal indicator of long-term user commitment.[2] In 2026, growth teams are optimizing not just for first deposit, but for governance participation, which correlates strongly with retention and lifetime value.

The 2026 On-Chain Attribution Framework: Step-by-Step Implementation

Phase 1: Identity Resolution and Wallet Linking (Weeks 1-4)

The foundation of any attribution system is knowing which wallets belong to the same user.

Step 1.1: Implement Opt-In Identity Linking

Users voluntarily connect their wallet to your platform. This is your primary identity anchor. Capture:

• Wallet address (primary identifier)
• Connection timestamp
• Connection source (direct app, referral link, campaign URL)
• Initial UTM parameters (if applicable)

Implementation:

Use Web3 authentication libraries (Web3Modal, RainbowKit, or ConnectKit) to standardize wallet connection flows. These tools handle multiple wallet types and provide consistent identity data.

Captured Data Structure:
{
  "wallet_address": "0x742d35Cc6634C0532925a3b844Bc9e7595f42bE",
  "connection_timestamp": "2026-02-15T14:32:00Z",
  "connection_source": "twitter_campaign_q1_2026",
  "utm_source": "twitter",
  "utm_medium": "organic",
  "utm_campaign": "defi_education_series"
}

Step 1.2: Implement Behavioral Analysis for Multi-Wallet Detection

Advanced users operate multiple wallets. Identify likely connections using:

• IP address correlation (same IP, different wallets within 24 hours = likely same user)
• Timing correlation (wallet A deposits, wallet B withdraws from same protocol within 30 minutes = likely same user)
• Transaction pattern analysis (similar transaction sizes, frequencies, and protocol preferences)

Step 1.3: Build a Privacy-Preserving Identity Graph

Create a mapping layer that connects wallets without storing personal data:

Identity Graph Entry:
{
  "user_id": "hash_of_primary_wallet",
  "primary_wallet": "0x742d35Cc6634C0532925a3b844Bc9e7595f42bE",
  "linked_wallets": [
    "0x8f3Cf7ad23Cd3CaDbD9735AFF958023D60c2eBe1",
    "0x1234567890123456789012345678901234567890"
  ],
  "confidence_score": 0.92,
  "linking_method": "behavioral_analysis"
}

Phase 2: Off-Chain Touchpoint Tracking (Weeks 2-5)

While on-chain data is your source of truth, off-chain touchpoints are the leading indicators that predict on-chain conversion.

Step 2.1: Implement UTM Tracking Across All Channels

Every marketing touchpoint should include UTM parameters:

• utm_source: twitter, discord, newsletter, paid_ads, organic_search
• utm_medium: social, email, cpc, organic
• utm_campaign: specific campaign identifier
• utm_content: specific creative or message variant

Step 2.2: Capture Website Behavior Events

Track user interactions on your website without personal data:

Event Schema:
{
  "event_type": "page_view",
  "wallet_address": "0x742d35Cc6634C0532925a3b844Bc9e7595f42bE",
  "page_path": "/docs/liquidity-pools",
  "referrer": "twitter.com",
  "timestamp": "2026-02-15T14:35:00Z",
  "session_id": "hash_of_wallet_plus_timestamp"
}

Step 2.3: Integrate Community Touchpoints

Track interactions in Discord, Telegram, and other community channels:

• Message count and engagement
• Role assignments
• Community event attendance
• Referral code usage

Implementation: Use Discord bots (like Collab.Land or similar Web3-native tools) to capture wallet-linked community activity.

Phase 3: On-Chain Transaction Mapping (Weeks 4-8)

This is where attribution becomes concrete. You're now correlating off-chain touchpoints with verifiable on-chain actions.

Step 3.1: Implement Time-Based Attribution

Link wallet actions to recent off-chain touchpoints within a specified time window.

Standard Attribution Windows by Action Type:

Example:

User visits landing page from Twitter on Feb 15 at 2:35 PM. They stake tokens on Feb 16 at 3:15 PM (25 hours later). Within your 24-48 hour window? No. Within 7-14 days? Yes. Attribution: Twitter campaign.

Step 3.2: Build Multi-Touch Attribution Models

Don't stop at last-touch. Weight multiple off-chain touchpoints across your attribution window.

Recommended Model: Time-Decay Attribution

Assign higher weight to recent interactions, lower weight to older ones.

Weight Calculation:
For each touchpoint within attribution window:
weight = e^(-decay_rate × days_before_conversion)

Example with decay_rate = 0.1:
- Touchpoint 1 day before conversion: weight = 0.90
- Touchpoint 3 days before conversion: weight = 0.74
- Touchpoint 7 days before conversion: weight = 0.50
- Touchpoint 14 days before conversion: weight = 0.25

Step 3.3: Implement Referral-Based Attribution

Direct attribution for referral programs. When a user deposits using a referral code, the attribution is explicit.

Referral Attribution:
{
  "referrer_wallet": "0x742d35Cc6634C0532925a3b844Bc9e7595f42bE",
  "referred_wallet": "0x8f3Cf7ad23Cd3CaDbD9735AFF958023D60c2eBe1",
  "referral_code": "HANGRYFEED_Q1_2026",
  "deposit_amount": "50000",
  "deposit_timestamp": "2026-02-16T15:30:00Z",
  "attribution_type": "referral"
}

Phase 4: Cross-Protocol Activity Tracking (Weeks 6-12)

The most sophisticated attribution models track users across the entire DeFi ecosystem.

Step 4.1: Index Wallet Activity Across Protocols

Use blockchain indexing services (The Graph, Alchemy, or Infura) to track wallet activity across multiple protocols.

Tracked Actions:

• Token swaps (DEX activity)
• Liquidity provision (LP deposits/withdrawals)
• Lending/borrowing (Aave, Compound, etc.)
• Staking (protocol-specific)
• NFT minting and trading
• Governance participation
• Bridge transactions (cross-chain activity)

Step 4.2: Build a Unified User Activity Timeline

Combine all on-chain and off-chain data into a single timeline per user.

Unified Timeline Example:
2026-02-10 14:30 | OFF-CHAIN | Twitter click from @YourProtocol
2026-02-10 14:35 | OFF-CHAIN | Website visit, 8 min session
2026-02-11 09:15 | OFF-CHAIN | Discord message in #general
2026-02-12 16:45 | ON-CHAIN  | First wallet connection
2026-02-13 11:20 | ON-CHAIN  | Token swap (0.5 ETH → 1000 TOKENS)
2026-02-14 08:30 | ON-CHAIN  | LP deposit (500 TOKENS + 0.25 ETH)
2026-02-15 14:00 | ON-CHAIN  | Governance vote on Proposal #42
2026-02-16 10:15 | ON-CHAIN  | Staking deposit (1000 TOKENS)

Step 4.3: Segment Users by Behavior Patterns

Identify high-value user archetypes based on on-chain activity.

Example Segmentation:

Tool Stack: The 2026 Attribution Tech Stack

Data Collection and Indexing

1. The Graph (Subgraphs)

Purpose: Index on-chain data from smart contracts Use case: Track token transfers, swaps, staking, governance Cost: Free for basic queries, paid for high-volume indexing Integration: GraphQL API

2. Alchemy or Infura

Purpose: RPC endpoints and enhanced APIs for wallet activity Use case: Real-time transaction monitoring, balance tracking Cost: Free tier available, $0.02-0.10 per 1M requests at scale Integration: REST API or WebSocket

3. Moralis

Purpose: Pre-indexed blockchain data with Web3 authentication Use case: Wallet history, token balances, transaction details Cost: $0.50-2.00 per 1M API calls Integration: REST API, Web3 SDK

Attribution and Analytics

4. Mixpanel or Amplitude (Web3-Configured)

Purpose: Event tracking and funnel analysis Use case: Off-chain touchpoint tracking, user journey visualization Cost: $995-5000/month depending on volume Configuration: Use wallet address as primary user ID instead of email

5. Formo (Web3-Native Attribution)

Purpose: Purpose-built on-chain attribution platform Use case: Multi-touch attribution, cross-protocol tracking, campaign ROI Cost: Custom pricing, typically $2000-10000/month Key feature: Automatically correlates off-chain UTMs with on-chain wallet actions

6. Dune Analytics

Purpose: SQL-based blockchain data analysis Use case: Custom attribution queries, cohort analysis, retention tracking Cost: Free tier available, $300-1000/month for advanced features Advantage: Direct access to blockchain data, no intermediary

Identity Resolution and Privacy

7. Lens Protocol or Farcaster

Purpose: Decentralized identity layer Use case: Optional user identity linking without personal data Cost: Free (protocol-level) Benefit: Users can voluntarily link their on-chain identity to social profiles

8. Privy or Magic

Purpose: Web3 authentication and wallet abstraction Use case: Simplified wallet connection, multi-wallet support Cost: Free tier available, $0.01-0.05 per transaction at scale Benefit: Handles identity resolution and multi-wallet detection

AI-Powered Attribution

9. Langchain + GPT-4 for Attribution Analysis

Purpose: AI agents that analyze attribution patterns and recommend optimizations Use case: Automated cohort analysis, anomaly detection, campaign recommendations Implementation: Build custom agents that query your attribution database and generate insights

Example Agent Prompt:
"Analyze wallet cohorts from the 'twitter_q1_2026' campaign. 
Identify which cohorts have highest LTV. 
Recommend which cohorts to retarget and with what messaging."

10. Chainlink Functions

Purpose: Off-chain computation triggered by on-chain events Use case: Automated attribution calculations, real-time campaign optimization Cost: $0.20-1.00 per function call Benefit: Trustless attribution calculations that can trigger smart contract actions

Marketing Economics: Calculating CAC, LTV, ROAS, and Payback Period

The Attribution Economics Framework

Attribution precision directly impacts your ability to calculate true unit economics. With 85-95% accurate attribution, you can finally answer: "Which campaigns actually drive profitable growth?"

CAC Calculation: Attribution-Based Approach

Traditional CAC (Broken):

CAC = Total Marketing Spend / Total New Users
CAC = $100,000 / 500 users = $200 per user

Problem: This doesn't account for which campaigns actually drove conversions. You're averaging across high-performing and low-performing channels.

Attribution-Based CAC (Accurate):

CAC by Channel = Channel Marketing Spend / Attributed Conversions

Twitter Campaign:
- Spend: $25,000
- Attributed conversions: 180 users
- CAC: $138.89

Paid Ads Campaign:
- Spend: $40,000
- Attributed conversions: 95 users
- CAC: $421.05

Discord Community:
- Spend: $5,000
- Attributed conversions: 125 users
- CAC: $40.00

Key Insight: Your Discord community has 10x better CAC than paid ads. Traditional attribution would have hidden this.

LTV Calculation: On-Chain Behavior Signals

On-chain data provides superior LTV predictors compared to Web2 metrics.

LTV Calculation Framework:

LTV = (Average Revenue per User × Gross Margin) / Monthly Churn Rate

For DeFi protocols:
- Average Revenue per User = (Protocol fees collected from user × 12 months)
- Gross Margin = 100% (marginal cost of serving user is ~0)
- Monthly Churn Rate = (Users who don't transact in month N+1 / Users in month N)

On-Chain Behavior Predictors of High LTV:

Example LTV Calculation:

User Cohort: Twitter Campaign, Feb 2026

Base Metrics:
- Average fees generated per user: $450/year
- Gross margin: 100%
- Monthly churn rate: 8%

LTV = ($450 × 1.0) / 0.08 = $5,625

Adjusted for Behavior:
- 40% of cohort participates in governance (3.2x multiplier)
- 30% provides liquidity (2.8x multiplier)
- 30% single swap only (0.3x multiplier)

Weighted LTV = (0.40 × $5,625 × 3.2) + (0.30 × $5,625 × 2.8) + (0.30 × $5,625 × 0.3)
            = $7,200 + $4,725 + $506.25
            = $12,431.25 (average LTV for cohort)

ROAS Calculation: Campaign-Level Attribution

ROAS = Revenue Attributed to Campaign / Campaign Spend

Twitter Campaign Q1 2026:
- Campaign spend: $25,000
- Attributed users: 180
- Average LTV per user: $12,431
- Total attributed revenue: 180 × $12,431 = $2,237,580
- ROAS: $2,237,580 / $25,000 = 89.5x

Paid Ads Campaign Q1 2026:
- Campaign spend: $40,000
- Attributed users: 95
- Average LTV per user: $8,200 (lower engagement cohort)
- Total attributed revenue: 95 × $8,200 = $779,000
- ROAS: $779,000 / $40,000 = 19.5x

Critical Insight: Twitter's ROAS is 4.6x higher than paid ads. Without attribution, you'd continue overspending on paid ads.

Payback Period: When Attribution Drives Profitability

Payback Period = CAC / (Average Monthly Revenue per User)

Twitter Campaign:
- CAC: $138.89
- Average monthly revenue per user: $450/12 = $37.50
- Payback period: $138.89 / $37.50 = 3.7 months

Paid Ads Campaign:
- CAC: $421.05
- Average monthly revenue per user: $37.50
- Payback period: $421.05 / $37.50 = 11.2 months

Strategic Implication: Twitter breaks even in 3.7 months. Paid ads takes 11.2 months. For a growth team with 12-month planning horizons, Twitter is dramatically more efficient.

2026 Benchmarks: What High-Performing Teams Achieve

Based on leading Web3 growth teams:

Scaling Tactics: From Attribution to Growth Acceleration

Tactic 1: Cohort-Based Campaign Optimization

Once you have accurate attribution, segment users by acquisition source and optimize messaging for each cohort.

Implementation:

1. Identify your top 5 acquisition channels by ROAS
2. Analyze on-chain behavior differences between cohorts
3. Create channel-specific messaging that emphasizes the features each cohort values most

Example:

Twitter Cohort: 60% governance participation
→ Emphasize governance participation, community voting, protocol direction

Discord Cohort: 45% LP deposits
→ Emphasize liquidity rewards, yield farming, capital efficiency

Referral Cohort: 70% referral activity
→ Emphasize referral rewards, community building, network effects

Tactic 2: Predictive Churn Modeling Using On-Chain Signals

Build models that predict which users will churn based on on-chain behavior.

High-Risk Churn Signals:

• No transaction activity for 30+ days
• Single transaction only (no follow-up)
• Withdrawal of all capital
• Lack of governance participation after 60 days

Intervention Strategy:

When users show churn signals, trigger automated campaigns:

• Email (if opted in): "We noticed you haven't traded in 30 days. Here's what's new..."
• On-chain incentives: Airdrop small amount of governance tokens to re-engage
• Community outreach: Discord DM from community manager

Expected Impact: 15-25% reduction in churn for users who receive interventions.

Tactic 3: Referral Program Optimization Using Attribution Data

Referral programs are your highest-ROI channel (often 50-100x ROAS). Attribution data lets you optimize them.

Optimization Levers:

1. Identify your best referrers: Which wallets refer the highest-LTV users?
2. Reward quality, not quantity: Pay referral bonuses based on referred user LTV, not just conversion
3. Create tiered programs: Top referrers get higher rewards, exclusive benefits

Example Tiered Referral Program:

Tier 1 (0-10 referrals): 5% of referred user fees for 12 months
Tier 2 (11-50 referrals): 10% of referred user fees for 12 months + governance tokens
Tier 3 (51+ referrals): 15% of referred user fees for 12 months + governance tokens + exclusive access

Expected outcome: Top 5% of referrers drive 40-50% of referral volume

Tactic 4: Attribution-Driven Product Development

Use attribution data to inform product roadmap decisions.

Questions Attribution Answers:

• Which features do high-LTV users engage with most?
• What's the correlation between governance participation and retention?
• Do users who interact with multiple protocols have higher LTV?
• Which on-chain actions predict long-term engagement?

Example Insight:

Analysis shows that users who participate in governance within 30 days of first deposit have 3.2x higher LTV. Product decision: Build governance participation into the onboarding flow.

Tactic 5: Real-Time Campaign Optimization Using AI Agents

Build AI agents that continuously analyze attribution data and recommend optimizations.

Agent Capabilities:

Daily Agent Tasks:
1. Analyze yesterday's conversions by channel
2. Calculate ROAS for each campaign
3. Identify underperforming campaigns (ROAS < 10x)
4. Recommend budget reallocation
5. Flag anomalies (sudden drop in conversions, unusual patterns)
6. Suggest new audience segments to target

Weekly Agent Tasks:
1. Cohort analysis: Compare behavior of users from different channels
2. Churn prediction: Identify users at risk of churning
3. LTV analysis: Update LTV estimates based on new on-chain data
4. Referral analysis: Identify top referrers and their characteristics
5. Competitive analysis: Compare your metrics to industry benchmarks

Implementation:

Use Langchain to build agents that query your attribution database (Dune, Mixpanel, custom database) and generate insights.

# Pseudocode for attribution agent
agent = AttributionAgent(
    data_sources=[dune_analytics, mixpanel, blockchain_indexer],
    models=[churn_prediction_model, ltv_prediction_model],
    decision_framework=roas_optimization
)

daily_report = agent.analyze_daily_performance()
# Output: "Twitter campaign ROAS dropped 20% yesterday. 
# Recommend pausing underperforming creative and testing new messaging."

Common Pitfalls and How to Avoid Them

Pitfall 1: Ignoring Multi-Wallet Users

Problem: Users operate multiple wallets. If you don't link them, you undercount conversions and overestimate CAC.

Solution: Implement behavioral analysis (IP correlation, timing correlation, transaction pattern analysis) to identify likely multi-wallet users. Aim for 85%+ confidence before linking.

Pitfall 2: Attribution Windows That Are Too Short

Problem: Using 24-hour attribution windows for DeFi products misses most conversions. Users research for days or weeks before committing capital.

Solution: Use 14-30 day attribution windows for first deposits, 7-14 days for follow-up transactions. Adjust based on your product's decision cycle.

Pitfall 3: Treating All Conversions Equally

Problem: A $100 deposit and a $100,000 deposit are not equivalent. But traditional attribution treats them the same.

Solution: Weight conversions by transaction size or use revenue-based attribution instead of conversion-based attribution.

Pitfall 4: Neglecting Off-Chain Touchpoints

Problem: Focusing only on on-chain data misses the full user journey. Off-chain touchpoints (Twitter, Discord, website) are the leading indicators.

Solution: Build a comprehensive attribution model that correlates off-chain touchpoints with on-chain conversions. Use time-decay weighting to prioritize recent interactions.

Pitfall 5: Not Accounting for Seasonality and Market Conditions

Problem: Comparing CAC and ROAS across different market conditions (bull market vs. bear market) is misleading.

Solution: Segment your analysis by market condition. Calculate separate benchmarks for bull and bear markets. Adjust expectations accordingly.

The Path Forward: 2026 and Beyond

The competitive advantage in Web3 growth is no longer about who can collect the most data. It's about who can extract the most signal from the data that users have already voluntarily published on the blockchain.

In 2026, the best-performing growth teams have:

1. Implemented 85%+ accurate attribution that correlates off-chain touchpoints with on-chain conversions
2. Built identity resolution systems that handle multi-wallet users without invasive data collection
3. Calculated true unit economics (CAC, LTV, ROAS, payback period) based on attributed revenue
4. Optimized campaigns based on attribution insights, not vanity metrics
5. Scaled through referral programs that reward quality, not just quantity
6. Deployed AI agents that continuously analyze attribution data and recommend optimizations

The teams that master these capabilities will grow 3-5x faster than competitors still operating with Web2 mental models.

The question isn't whether to implement on-chain attribution. It's how quickly you can build it before your competitors do.