Marketing Analytics Foundations

Marketing Analytics Foundations

Marketing is increasingly a quantitative discipline. The era of "we know half our advertising works, but not which half" is ending. Data infrastructure, tracking tools, and analytical frameworks now enable marketers to measure what drives growth with far greater precision — and to be accountable for results.

The Measurement Framework

Before collecting data, establish a measurement framework that connects marketing activities to business outcomes.

Business goals → KPIs → Metrics → Data:

• Business goal: "Grow revenue by 30% this year"
• KPI: "Acquire 500 new customers per month at ≤ $150 CAC"
• Metrics: new customers, CAC by channel, conversion rate by funnel stage
• Data: ad spend, sessions, signups, revenue by cohort

Without this chain, marketers collect data that doesn't inform decisions. The most common failure: optimizing CTR (a metric) while the business KPI is revenue per customer (not correlated with CTR).

Funnel Metrics

Every marketing system has a funnel structure. The specific stages vary by business model but the principle is constant: a large pool at the top narrows to a smaller pool of high-value customers.

B2C e-commerce funnel:

• Impressions → Reach → Clicks → Sessions → Product Views → Add-to-Cart → Checkout → Purchase

B2B SaaS funnel:

• Impressions → Clicks → Site Sessions → Trial Signups → MQLs (Marketing Qualified Leads) → SQLs (Sales Qualified Leads) → Demos → Closed/Won → Retained Customer

Funnel analysis: each transition has a rate. A 2% conversion from checkout to purchase is a friction problem. A 0.5% CTR on display ads may be fine (wide reach, low purchase intent) or terrible (poor targeting). Context determines whether a rate is healthy.

Finding leverage: the highest-leverage intervention is at the stage with the highest volume but the worst conversion rate. If 70% of customers start checkout but only 15% complete it, checkout optimization outperforms any top-of-funnel investment.

Customer Acquisition Cost

CAC = Marketing Spend + Sales Spend / New Customers Acquired

CAC payback period: how many months of customer revenue are needed to recover the acquisition cost. A CAC of $300 with $30/month in gross profit = 10-month payback. This determines how much working capital is required to grow.

CAC by channel: paid search, social, content, SEO, referral, and events all have different CACs. Knowing CAC by channel enables budget allocation toward the most efficient channels.

Blended vs. marginal CAC: blended CAC averages all channels. Marginal CAC tells you what the next additional customer costs. Marginal CAC increases as channels become saturated — the first 100 customers from paid search may cost $50; the next 100 cost $120.

Customer Lifetime Value

CLV is the single most important number in acquisition economics. If you don't know CLV, you can't know whether your CAC is sustainable.

Simple CLV formula:

CLV = (Average Order Value × Purchase Frequency × Gross Margin) / Annual Churn Rate

A customer buying $80 twice per year, at 60% gross margin, with 25% annual churn:

CLV = ($80 × 2 × 0.60) / 0.25 = $384

This means paying up to $384 to acquire this customer breaks even on CLV. In practice, a 3:1 CLV:CAC ratio is the minimum healthy benchmark; 4:1 or higher is target.

CLV segmentation: some customers are worth 10× others. High-CLV customer acquisition is worth more per unit than low-CLV acquisition. Building a model to predict CLV from acquisition characteristics enables smarter bidding and targeting.

Attribution

Every touchpoint in a customer's journey could claim credit for the conversion.

Last-touch: 100% of credit to the final touchpoint before conversion. Biases toward bottom-of-funnel channels (branded search, retargeting) and undervalues awareness-building activities.

First-touch: 100% of credit to the first touchpoint. Biases toward discovery channels (organic social, display) and undervalues the closing touchpoints.

Linear: equal credit to every touchpoint. Treats a display impression the same as a branded search click — probably wrong.

Position-based (U-shaped): 40% to first touch, 40% to last touch, 20% distributed across middle touchpoints. Attempts to balance acquisition and closing credit.

Data-driven / algorithmic: uses machine learning to assign fractional credit based on the actual marginal contribution of each touchpoint to conversion probability. Most accurate but requires large data volumes.

Attribution's fundamental limitation: any model that attributes 100% of a conversion to online touchpoints ignores brand exposure, word of mouth, offline activity, and the customer's prior experience with the category. Marketing Mix Modeling (discussed in M03) addresses these limits.

Key Performance Indicators

Efficiency metrics: CAC, ROAS, Cost per Lead (CPL), Cost per Click (CPC)

Volume metrics: Impressions, Reach, Sessions, Leads, Conversions

Quality metrics: Engagement Rate, Bounce Rate, Time on Site, MQL Rate, SQL Rate

Revenue metrics: Revenue, AOV, CLV, Gross Margin

Retention metrics: Churn Rate, Net Revenue Retention (NRR), Cohort Retention

NRR (Net Revenue Retention): (MRR at start of period + expansion - churn - contraction) / MRR at start. NRR > 100% means the existing customer base is growing even without new acquisition. This is the most powerful growth metric — it means your best acquisition channel is your existing customers.