Marketing Metrics and AI Concepts
Marketing Metrics
Key Performance Indicators (KPIs)
Gross Rating Points (GRPs) = Ratings * Number of inserts (total exposure on advertising of a specific target audience)
GRPs = (Total impacts * 100) / Target audience
GRPs = % Coverage * Opportunities to See (OTS)
Rating Points/Impacts = Media A / Target audience (also: GRP / Number of inserts)
% of Penetration = (Audience number / Total population) * 100
Audience Number = (% Penetration / 100) * Total population
Useful Audience = ((Target audience * Average audience) / Universe)
Cumulative Audience = Sum of insertions of the target audience
Affinity = Target audience % / Universe (Total Target audience %) + 1 (good) or -1 (bad)
Investment = CPC / Clicks (Total = Sum)
Conversion Rate % (CR) = (Transactions / Clicks) * 100 (Total = Formula over totals)
Transactions in % = (CR / 100) * Clicks (Total = Sum)
Cost Per Acquisition (CPA) = Investment / Transactions (Total = Formula over totals)
Revenue = Investment * ROAS (Total = Sum)
Transactions = Investment / CPA (Total = Sum)
Cost Per Mile (CPM) = (Price / Audience) * 100
Cost per RP = Price / GRP (Method for buying media on TV)
Return on Advertising Spend (ROAS) = Revenue / Investment (Total = Formula over totals)
Marketing Funnel
Brand Awareness: KPIs: Impressions, Website Traffic, Social Media Engagement, Search Volume Data, Brand Awareness Survey
Consideration: KPIs: Pages per session, CTR, Database growth, Open rate, Brand Consideration Survey, Distribution
Conversion: KPIs: Sales by channel, ROAS, Conversion rate, Acquisition cost, Profitability
Maintenance: KPIs: Customer lifetime value, ROI, Repeat purchase rate
Expansion: KPIs: Market share growth, Customer Lifetime Value (CLV), Net Promoter Score
Marketing and Advertising
Marketing: Process of anticipating/satisfying customer needs profitably. KPIs: Media impact, brand image, market share, logistics costs, after-sale service
Advertising: Placement of persuasive advertisements and messages to inform and/or persuade a target market.
AI and Data Concepts
AI Definitions
- DL: Deep Learning (extract patterns from data using neural networks)
- ML: Machine Learning (ability to learn without being programmed)
- AI: Artificial Intelligence (machine enables human behavior)
- AI Ethical Principles: Epistemic (interpretability, reliability/security), General (accountability, human agency, safety, fairness, beneficial AI, data privacy, lawfulness)
AI Technologies
IoT, Metaverse, Robotics, VR, Smart City, Biometrics, Blockchain, Location Intelligence, Cloud Computing, Cybersecurity
Machine Learning Paradigms
- Supervised: Labeled, direct feedback, predict future
- Unsupervised: No labels or feedback, find hidden structure
- Reinforcement Learning: Decision process, reward, series of actions
Data Concepts
- Data Sets: Systems are not objective, have biases, tech is based
- Data: Raw/unorganized facts with little meaning until sorted
- 3 Stages: Input, process, output
- Big Data 4Vs: Volume, variety, velocity, veracity
- 7Vs: Value, visualization, variability
- Digitization: Converting analog data to digital
- Digitalization: Integrating digital technology into business opportunities to optimize processes
Agile and Usability
12 Principles of Agile Manifesto: Customer satisfaction, welcome changing requirements, frequent delivery, collaboration, motivated individuals, face-to-face conversation, working software, sustainable development, technical excellence, simplicity, self-organizing teams, reflection and adaptation.
Components of ERP: Financial management, HR, supply chain management, sales and marketing management, project management.
Usability Principles: Simple, familiar, consistent, guidance, feedback.
User-Centered Design Process: Plan, design, review, prototype (heuristic, cognitive, verification).
GAN: Generative Adversarial Network – uses generator and discriminator to generate new data.