Challenges and Future Directions of Computational Advertising Measurement Systems

Figures & data

Figure 1. Computational advertising measurement system.

Table 1. Data inputs and outputs in computational advertising.

Process	Objectives	Data Sources	Metrics
Enterprise strategy	Quantify the marketplace in which the firm operates	Consumer culture, macroeconomics, competition, regulation	Business and brand value (e.g., total shareholder value, return on capital, pricing power, brand equity, customer loyalty, lifetime value)
	Quantify the firm’s business objectives for advertising	Business objectives, short- and long-term key performance indicators (e.g., return on investment for multiple stakeholders)
	Quantify the firm’s financial metrics for investment decisions	Financial objectives (e.g., internal rate of return, organizational competencies, resources, constraints)
Advertising strategic planning	Define the strategic plan (e.g., consumer insight, brand communications, media paid/owned/earned)	Advertising investments, media activities Brand health and consumer perceptions	Revenue, sales, and profitability (e.g., revenue, sales margins, customer acquisition, retention and cross-selling, channel, audience, and campaign return on investment)
Advertising tactical execution	Define the tactical executions (e.g., targeting, messaging, consumer journeys, channel delivery, sales distribution)	Consumer profiles, media audiences, and context Brand and curated content in multiple formats	Consumer behaviors, actions, and interactions (e.g., traffic, responses, likes, posts, shares, following, sign-ups, registrations, comments, complaints)

Figure 2. Metrics important to different stakeholders. Order of boxes do not reflect importance.