Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, offers profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. The Marketing Analytics course is provided by UNIVERSITY OF VIRGINIA. The following are the notes I took during this course.

1. Marketing Analytics

Descriptive analytics

Predictive analytics

Prescriptive analytics

2. Marketing Process

Objectives: Customer, Company, Competitor, Collaborators, Context

Strategy: Segmentation, Targeting, Positioning

Tactics: Product, Price, Place, Promotion

Financials: Margin, ROI, CLV

3. Marketing Strategy with Data

Mental models

Text analytics

4. Brand Architecture

Brand value

Brand personality: Sincerity / Excitement / Competence / Sophistication / Ruggedness

Brand Architecture

1. Brand core / Essence
2. Brand personality
3. Emotional benefits
4. Product benefits
5. Product attributes

5. Calculating Brand Value

Interbrand brand valuation model

• Financial analysis -> Residual earnings -> Brand earnings
• Marketing analysis -> Role of branding -> Brand earnings
• Brand analysis -> Brand strength score -> Risk rate

Y &R brand asset valuator

• Brand strength (Strength / Vatality) -> Differentiation & Relevance
• Brand stature (Emotional capital) -> Esteem & Knowledge

Brand equity: long term estimate

Revenue Premium

• Equity = Annual revenue premium * (1 + discount rate) / (1 + discount rate - stability factor)
• Annual revenue premium = Revenue premium - Additional variable cost

6. Customer Lifetime Value (CLV)

Both backward looking and forward looking

Net present value (NPV)

CLV = (Gross margin - Detention spending) * (1 + discount rate) / (1+discount rate - retention rate)

Cohort and incubators

7. Experimental Design

Correlation and causation / Causality

Marketing return on investment

Test group & Control group / Randomization

Experiments assess cause and effect

8. Calculating Break Even and Lift

Full factorial design

Projrcting lift

Pitfalls of marketing experiments

Maximizing effectiveness

Experiments provide forecasts of expected ROI

9. Regression Basics

Regression analysis

Regression outputs (about intuition)

• R-squared (sales/promotion)
• P-value (lower than 10% is trustable)

Multivariable regressions

Omitted variable bias: price -> Units sold + feature / display

10. Price Elasticity

PED = (Change in Sales / Change in Price) * (Price / Sales)

Coefficient * Average price/ Average sales

Measures the impact of a change in price on sales

Enhances your ability to utilize regressions

Allows you to track marketing efforts over time

11. Log-Log Models

LOG = Percentage Change

12. Marketing Mix Model

Statistical significance & Economic significance

1. Product line
2. Place
3. Price
4. Promotion

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