Glossary

Marketing and AI terminology defined with context from the book.

B

Brand Equity

Brand equity is the commercial value that derives from consumer recognition, recall, and positive associations with a brand. It represents the financial premium a company can command in the marketplace simply because customers know and remember the brand name. Strong brand equity makes consumers more likely to choose your product over competitors, even when alternatives offer similar features or pricing.

Brand Salience

Brand salience is the propensity for a brand to be noticed or come to mind in buying situations. It represents your brand's share of people's minds, defined by the quantity and quality of memory links to and from that brand. Salience determines whether your brand springs to mind when customers have a need, making it the foundation of mental availability.

C

Category Entry Points

Category Entry Points (CEPs) are the mental triggers or cues that bring a product category to mind in buying situations. Developed by researcher Jenni Romaniuk, CEPs represent the moments when consumers realize they have a need that could be satisfied by purchasing something. The brands most strongly linked to these entry points enjoy higher mental availability and greater likelihood of consideration.

Conversion Lag

Conversion lag is the time delay between when a customer interacts with an ad and when they complete a conversion action. This delay can range from hours to months, depending on the product complexity and purchase decision timeline. The lag creates measurement blind spots that systematically favor immediate-response campaigns over brand-building activities that drive long-term growth.

D

Double Jeopardy Law

The double jeopardy law states that smaller brands face two disadvantages simultaneously: they have fewer customers and those customers buy from them less frequently than customers of larger brands. This marketing principle, discovered by Andrew Ehrenberg, explains why market share leaders maintain their dominance not through superior loyalty but through broader market penetration.

I

Incrementality Testing

Incrementality testing is a controlled experimental methodology that measures the true causal impact of marketing activities by comparing business outcomes between test groups exposed to marketing and control groups that are not. Unlike attribution models that track correlation, incrementality testing reveals whether marketing campaigns actually drive additional sales or merely capture existing demand that would have occurred anyway.

M

Marketing Mix Modeling (MMM)

Marketing mix modeling is a top-down statistical approach that analyzes historical business data to measure the effectiveness of different marketing channels and tactics. MMM examines correlations between marketing spend and business outcomes while controlling for external factors like seasonality and economic conditions. It's primarily used for strategic budget allocation and planning decisions across quarters or years.

P

Performance Max Campaigns

Performance Max (PMax) campaigns are Google's fully automated, AI-driven advertising format that optimizes across all Google properties simultaneously. Unlike traditional campaign types that require manual audience targeting and placement selection, PMax uses machine learning to automatically find the best combinations of audiences, creatives, and placements to achieve your conversion goals. The system represents Google's push toward maximum campaign liquidity, where the algorithm has unrestricted access to all available inventory and user signals.

R

ROAS (Return on Ad Spend)

ROAS is a marketing metric that measures the revenue generated for every dollar spent on advertising, calculated as Revenue ÷ Ad Spend. While useful for measuring campaign efficiency, ROAS can mislead marketers by encouraging short-term optimization at the expense of long-term brand building and sustainable growth.

S

Satisficing

Satisficing is a decision-making strategy where consumers select options that meet their minimum acceptable criteria rather than seeking the optimal choice. Originally coined by Nobel laureate Herbert Simon, the term combines 'satisfy' and 'suffice,' describing how people make practical decisions when faced with too many options or limited time.

Smart Bidding

Google's suite of automated bidding strategies that use machine learning to optimize bids in real-time for conversions or conversion value. These algorithms process vast amounts of data signals to predict the likelihood of conversion for each auction, automatically adjusting bids to meet specified performance targets like Target CPA or Target ROAS.