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Background

Cliff Sosin is the founder of CAS Investment Partners. We cover what makes Cardlytics' value proposition so valuable to the ecosystem, how Cardlytics' measurement capabilities differ from Google, and what is needed for Cardlytics to reach its full potential.

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Date

July 14, 2021

Episode Number

17

Cardlytics Business Breakdown

Background / Overview

Cardlytics, founded in 2008, is a digital advertising platform integrated into the digital channels of major U.S. banks such as JPMorgan Chase, Wells Fargo, and Bank of America. It leverages transactional data to deliver targeted offers to bank customers within their mobile banking apps, creating a closed-loop advertising ecosystem. The company processes data on approximately 50% of all card swipes in the U.S., with 170 million monthly active users (MAUs) who are shown offers, covering about 55% of total card spend. Cardlytics operates as a technology intermediary, connecting advertisers, banks, and consumers, with a business model that emphasizes precise targeting and measurement through randomized controlled trials (RCTs).

The company’s history reflects a challenging journey. Early attempts by founders Scott Grimes and Lynne Laube, both ex-Capital One executives, focused on a unique data architecture where consumer data never leaves the bank’s firewall, addressing privacy concerns and enabling partnerships. Initial wins with regional banks like Regions, followed by a pivotal Bank of America contract, were tempered by limited reach and low consumer engagement due to the nascent state of mobile banking. Cardlytics faced near insolvency multiple times and a nearly failed IPO in 2018. However, securing Wells Fargo and Chase in 2018 marked a turning point, significantly expanding reach and aligning with the rise of mobile banking, which drives ~80% of offer engagement despite representing ~40% of banking activity.

Ownership / Fundraising / Recent Valuation

The transcript does not provide specific details on current ownership, private equity sponsors, or recent transaction multiples. Cardlytics is publicly traded, having completed an IPO in 2018 that nearly failed due to financial struggles. No enterprise value (EV) or valuation multiples are explicitly mentioned, but the business’s revenue of ~$200 million (based on the 70 cents per ad dollar after consumer rewards) suggests a modest scale relative to major ad platforms like Google or Meta. The lack of valuation data precludes a precise market cap estimate, but the narrative implies a growth-oriented valuation given the large total addressable market (TAM) potential, estimated at over $100 billion.

Key Products / Services / Value Proposition

Cardlytics’ core product is a digital advertising platform that delivers personalized offers to bank customers within their mobile banking apps. The value proposition is threefold:

1. For Advertisers: Unparalleled targeting and measurement precision. Advertisers (e.g., Starbucks) can segment consumers based on spending patterns (e.g., café category shoppers who avoid Starbucks), location, loyalty program status, or offer responsiveness. RCTs provide penny-accurate measurement of incremental spend, yielding ~$5 of incremental spend per $1 spent, with advertiser margins of ~60% translating to $2 net value per dollar.
2. For Banks: Revenue sharing (35 cents per ad dollar) and significant customer loyalty benefits. Offers reduce churn, increase card spend, and boost credit card revolving balances, generating economic value 5-10x the revenue share ($1.50-$3.50 per ad dollar).
3. For Consumers: Cashback offers (30 cents per ad dollar) incentivize purchases at preferred merchants, requiring minimal behavior change (e.g., choosing Starbucks over Dunkin).

Segments and Revenue Model

Cardlytics operates a single primary segment: digital advertising within bank channels. The revenue model is based on billings from advertisers, where for every $1 spent:

• 30 cents goes to consumers as cashback rewards (not counted as revenue per GAAP).
• 70 cents is recognized as revenue, split ~50/50 with banks (35 cents to Cardlytics, 35 cents to banks as cost of revenue).

The company is expanding into sub-segments:

• Enterprise Clients: Large brands (e.g., Starbucks, Chipotle) dominate current revenue.
• Mid-Sized Businesses: Targeted via a self-service platform for ad agencies, representing ~2/3 of U.S. ad spend.
• SKU/Category Offers: Enabled by the Bridg acquisition, allowing granular offers (e.g., $1 off Campbell’s Soup at Target), expanding advertiser flexibility.

Splits and Mix

Revenue Mix

• Channel Mix: Primarily mobile banking apps (~80% of engagement), with minor desktop banking activity.
• Geo Mix: U.S.-centric, with no international presence mentioned.
• Customer Mix: Enterprise advertisers (large brands) dominate, with mid-sized businesses as a growth area.
• End-Market Mix: Retail, dining, hospitality, and CPG are implied as key verticals, though specific splits are not provided.
• Product Mix: General offers (e.g., 5% off Starbucks) are standard, with SKU/category offers emerging.

EBITDA Mix

No specific EBITDA contribution by segment is provided, but the high gross margin (~45-50%) and low incremental costs suggest that enterprise clients drive most profitability, with mid-sized business and SKU offers as future contributors.

Historical/Forecasted Mix Shifts

• Historical: Shift from desktop to mobile banking increased engagement significantly post-2018 (Chase/Wells Fargo partnerships).
• Forecasted: Growth in mid-sized business revenue via self-service platforms and SKU/category offers via Bridg. Improved UI/UX (e.g., U.S. Bancorp’s 50% MAU offer activation rate) will drive engagement, potentially doubling channel size as mobile banking adoption grows.

KPIs

• MAUs: 170 million, covering 55% of U.S. card spend.
• Offer Activation Rate: At U.S. Bancorp, 50% of MAUs activated offers within three weeks of new UI/UX launch.
• Incremental Spend ROI: ~$5 per $1 spent by advertisers.
• Engagement Driver: Mobile banking drives ~80% of offer interactions.
• Growth Indicator: Post-2018 bank partnerships and mobile banking adoption suggest acceleration, though advertiser adoption remains a bottleneck.

Headline Financials

Long-Term Financial Trends

• Revenue: Expected to grow significantly (5x or more) due to increased MAUs (potentially 250-300M), higher engagement, and new revenue streams (mid-sized businesses, SKU offers).
• EBITDA Margin: Should converge toward gross margins (~45-50%) as fixed costs (tech, sales) are spread over larger revenues.
• FCF: High cash conversion expected due to low incremental costs and minimal capex.

Value Chain Position

Cardlytics sits at the intersection of banks, advertisers, and consumers, acting as a technology platform that processes transactional data and delivers targeted ads. Its primary activities include:

• Data Processing: Analyzing bank transaction data behind firewalls to segment consumers.
• Campaign Management: Designing and executing ad campaigns for advertisers.
• Offer Delivery: Presenting offers in bank apps and tracking redemptions.

Value Chain Role

• Midstream: Cardlytics is a platform intermediary, not producing goods or directly serving end consumers but enabling transactions between advertisers and banks.
• GTM Strategy: Enterprise sales to large advertisers, expanding to ad agencies via self-service platforms. Banks are partners, not customers, incentivized by revenue shares and loyalty benefits.
• Value-Add: Precise targeting and RCT-based measurement, unmatched by other ad platforms, create a competitive edge. The closed-loop system ensures accurate attribution.

Competitive Advantage

Cardlytics’ advantage lies in its access to bank transaction data, secure data architecture, and RCT measurement, creating high barriers to entry. Its position as a neutral platform enhances its role as an “industrial utility” akin to Visa, benefiting all stakeholders without direct merchant friction.

Customers and Suppliers

• Customers: Advertisers (enterprise brands like Starbucks, mid-sized businesses via ad agencies). Banks are partners, not customers, receiving revenue shares.
• Suppliers: Banks provide data and distribution channels. The Bridg acquisition enhances SKU-level data access from retailers.
• Dependency: Cardlytics relies on bank partnerships for data and reach, but its multi-bank network reduces single-partner risk. Advertisers value reach, making scale critical.

Pricing

• Contract Structure: Advertisers pay per campaign, with pricing based on targeted segments and expected ROI (~$5 incremental spend per $1). No long-term contracts are mentioned, suggesting flexibility.
• Pricing Drivers:
• Mission-Criticality: High ROI and precise measurement make Cardlytics attractive, though adoption lags due to unfamiliarity with RCT quality.
• Blended Price: Currently advertiser-friendly, with potential for higher markups as the platform matures.
• Demand Elasticity: Advertisers are price-sensitive due to competing channels (Google, Meta), but Cardlytics’ measurable ROI reduces sensitivity for informed buyers.

Bottoms-Up Drivers

Revenue Model & Drivers

Cardlytics generates revenue by charging advertisers for targeted campaigns. For every $1 spent:

• 30 cents to consumers (not revenue).
• 35 cents to banks (cost of revenue).
• 35 cents retained by Cardlytics (gross profit).

Revenue Models

• Enterprise Sales: Direct sales to large brands, focusing on broad offers (e.g., 5% off Starbucks).
• Self-Service Platform: Targets mid-sized businesses via ad agencies, expanding addressable market.
• SKU/Category Offers: Granular offers (e.g., $1 off specific products) increase advertiser flexibility and margins.

Volume Drivers

• MAU Growth: From 170M to 250-300M as mobile banking adoption rises.
• Engagement: Improved UI/UX (e.g., U.S. Bancorp’s 50% activation rate) and SKU offers boost offer activations.
• Advertiser Adoption: Overcoming skepticism about RCT measurement is key. Scale (more banks, MAUs) attracts advertisers.
• Industry Dynamics: Retail, dining, and CPG verticals drive demand, with mid-sized businesses (~2/3 of ad spend) as a growth lever.

Pricing Drivers

• Value-Based: High ROI (~$5 per $1) justifies pricing, with potential for higher markups as trust in measurement grows.
• Mix Effect: SKU/category offers may command premium pricing due to higher conversion rates.

Revenue Mix

• Absolute Size: ~$200M, with multi-billion-dollar potential in enterprise sales alone.
• Growth: 5x+ potential from MAU growth, engagement, and new segments.
• Mix: Shifting from enterprise to include mid-sized businesses and SKU offers.

Cost Structure & Drivers

Variable Costs

• Bank Revenue Share: 35 cents per ad dollar (~50% of revenue), the primary variable cost.
• Computing Costs: Minor, related to data processing and campaign delivery.

Fixed Costs

• Technology Development: Building self-service platforms, UI/UX improvements, and integrations (e.g., Bridg).
• Sales Teams: Small teams for enterprise and ad agency sales, with modest scaling needs.
• Overhead: Administrative and R&D costs, relatively fixed.

Cost Analysis

• % of Revenue: Bank share (50%), tech/sales (10-20% estimated), with operating leverage as revenue scales.
• % of Total Costs: Bank share dominates, with tech/sales as secondary drivers.
• Operating Leverage: Low incremental costs mean margins approach gross margins (~45-50%) at scale.

EBITDA Margin

• Not specified, but expected to converge to gross margins long-term due to high operating leverage.
• Drivers: Revenue growth and fixed cost dilution. Bank revenue share terms have historically improved in Cardlytics’ favor.

FCF Drivers

• Net Income: Driven by EBITDA growth, with minimal interest/tax details provided.
• Capex: Low, primarily tech development (software, not physical assets).
• NWC: Not detailed, but digital nature suggests minimal inventory/receivables cycles.
• Cash Conversion: High potential due to low capex and incremental costs, though not quantified.

Capital Deployment

• M&A: Acquired Bridg for SKU-level data and Dosh for modern tech stack to serve neobanks, enhancing platform capabilities.
• Organic Growth: Investments in self-service platforms and UI/UX to drive advertiser adoption and engagement.
• No Buybacks/Dividends: Focus on growth investments.

Market, Competitive Landscape, Strategy

Market Size and Growth

• TAM: Estimated at >$100 billion, based on bottoms-up analysis of advertiser budgets across enterprise and mid-sized businesses, extrapolated to optimal engagement (e.g., U.S. Bancorp’s 50% activation rate).
• Growth Drivers:
• Volume: MAU growth (170M to 250-300M), mobile banking adoption, and UI/UX improvements.
• Price: Potential for higher markups as measurement trust grows.
• Absolute Growth: 5x+ revenue potential over 5-10 years, aligning with U.S. digital ad market growth (~$200B+).

Market Structure

• Fragmented: Digital advertising is dominated by Google and Meta, with Cardlytics as a niche player leveraging unique bank data.
• Competitors: Google, Meta (less precise measurement), neobank platforms (e.g., Dosh, acquired by Cardlytics), and potential bank in-house solutions.
• MES: High due to scale requirements (bank partnerships, tech investment), limiting competitors.
• Cycle: Early growth phase, with adoption hurdles but strong long-term potential.

Competitive Positioning

• Matrix: High precision (RCT measurement, bank data) but limited scale compared to Google/Meta.
• Risk of Disintermediation: Low from banks due to scale economies and tech complexity; moderate from neobanks (mitigated by Dosh acquisition).

Market Share & Relative Growth

• Share: Small relative to Google/Meta, but dominant in bank-channel advertising.
• Growth: Likely outpacing market due to unique value proposition, though constrained by advertiser adoption.

Hamilton’s 7 Powers Analysis

1. Economies of Scale: Strong. Larger bank networks increase advertiser interest, reducing sales costs per ad dollar. Fixed tech costs spread over growing revenue.
2. Network Effects: Moderate. More advertisers improve offer variety, boosting consumer engagement, but category-specific offers may cannibalize (e.g., Home Depot vs. Lowe’s).
3. Branding: Weak. Cardlytics is invisible to consumers, relying on bank branding.
4. Counter-Positioning: Strong. Unique bank data and RCT measurement differentiate from Google/Meta, with incumbents unlikely to replicate due to data access barriers.
5. Cornered Resource: Strong. Exclusive bank partnerships and transaction data are hard to replicate.
6. Process Power: Moderate. Advanced targeting and self-service platforms are improving, but not fully optimized.
7. Switching Costs: Moderate. Advertisers face low switching costs, but banks are locked in due to integration complexity.

Strategic Logic

• Capex Bets: Investments in self-service platforms, UI/UX, and acquisitions (Bridg, Dosh) are offensive, aiming to capture mid-sized businesses and neobanks.
• Vertical Integration: Limited, focused on platform enhancements rather than owning data or distribution.
• Horizontal Expansion: Targeting mid-sized businesses and SKU offers to broaden advertiser base.
• M&A: Strategic acquisitions (Bridg, Dosh) enhance capabilities without overpaying, avoiding negative mix shifts.

Opportunities & Risks

Opportunities

• Self-Service Platform: Unlocks mid-sized businesses (~2/3 of ad spend), doubling TAM.
• UI/UX Improvements: Boosts engagement (e.g., U.S. Bancorp’s 50% activation), enabling complex offers.
• SKU/Category Offers: Increases advertiser flexibility and margins, driving adoption.
• Mobile Banking Growth: Doubles channel size as adoption rises.
• Data Optimization: Predictive analytics could personalize offers, reducing advertiser effort and increasing ROI.

Risks

• Advertiser Adoption: Skepticism about RCT measurement quality hinders growth. Loss of champions (e.g., CMO changes) disrupts campaigns.
• Bank Disintermediation: Unlikely but possible if banks insource, though scale and tech barriers make this costly.
• Neobank Competition: Mitigated by Dosh acquisition, but fragmentation could challenge legacy bank relevance.
• Privacy/Regulation: Data leaks could be catastrophic, though secure architecture minimizes risk.
• UI/UX Dependency: Poor bank app engagement could limit offer visibility.

Conclusion

Cardlytics’ business model is uniquely positioned at the nexus of bank data, advertiser needs, and consumer incentives, creating a closed-loop advertising platform with unmatched targeting and measurement precision. Its $200 million revenue base, high gross margins (~45-50%), and low incremental costs suggest strong operating leverage and FCF potential as it scales. The company’s journey from near insolvency to partnerships with major banks highlights its resilience and strategic focus on mobile banking, which drives ~80% of engagement. Key growth drivers include expanding to mid-sized businesses, improving UI/UX, and leveraging SKU-level offers via acquisitions like Bridg.

However, challenges remain. Advertiser adoption lags due to unfamiliarity with RCT measurement, and the platform’s success hinges on banks maintaining app relevance. The estimated >$100 billion TAM and 5x+ revenue growth potential over 5-10 years underscore the opportunity, but execution risks—particularly around cultural shifts in advertising—could delay or derail progress. Hamilton’s 7 Powers analysis reveals strong competitive advantages in scale, counter-positioning, and cornered resources, though network effects are nuanced and branding is absent. Cardlytics exemplifies a high-potential business where the end-state is compelling, but the path is fraught with inertia and adoption hurdles.