Reading...
Play button
Play button
Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
19 Cards in this Set
- Front
- Back
|
Marketing Engineering
|
Use of Decision Models to make marketing decisions
Complements (does not substitute) Conceptual Marketing (rely on Mental Model) |
|
|
Traditional Approaches
|
-Rely on Experience (Mental Model)
Example: “TV advertising is the best approach.” -Use Practice Standards Example: “Advertising budget should be 5% of sales” “Advertising for new products should be 30% of total” |
|
|
Marketing Analytics/ Engineering: Approach
|
Data (ie sales data) -(sorting)->Information (ie sales by industry) -(modeling)-> Insights (ie 80% of sales from 3 industries)-(judgment)->Decisions (ie. focus sales team) -(resource allocation)-> Implement (ie sales preparation)
|
|
|
Marketing Analytics/ Engineering: Why?
|
Acess to PC's
Exploding Data Corporate Pressures - make sense of db's: suppliers, demo, geo, CRM's (customer relationship management) |
|
|
Types of Models
|
Verbal: Describe in words
Graphical: Describe in pictures Mathematical: Describe in equation |
|
|
Decision Model Characteristics - Purpose
|
The reason for its creation; ADBUDG model: Ad budgets
|
|
|
Assumption
|
Provides context or framework for model
|
|
|
Variables
|
Aspects of marketing problem that can vary, e.g., Sales
|
|
|
Decision Model Characteristics -- Variables
-Uncontrollable |
Controlled by others: Population aging, New regulations
|
|
|
Variables
-Controllable |
Firm controls: Ad spending, Product features
|
|
|
Decision Model Characteristics -- Variables -Independent
|
Input Variables: Controllable + Uncontrollable variables
(input -- change in ad spend and ad spending) |
|
|
Decision Model Characteristics -- Variables - Dependent
|
Output Variables: Sales (almost always)
|
|
|
Verbal Models
|
Described in words
“Awareness leads to knowledge leads to liking…” Almost all models start out as verbal Easy to explain, but no quantification |
|
|
Graphical Models
|
Pictures or Charts
Graphs, Flow diagrams, Organizational charts Bridge between verbal & mathematical |
|
|
Mathematical Models
|
Relationship described by equation
Sales = a(1-e(-bx) ) a = market potential, x = advertising, b = constant |
|
|
Descriptive Decision Models
|
Predictive
“What will happen if we do x?” Explore impact of alternative scenarios Find explanation for phenomenon Predict possible outcomes Classic “What If?” spreadsheet exercise |
|
|
Normative Decision Model
|
Prescriptive
“Given x, what should we do?” Constrained optimization Example: Allocating limited sales personnel |
|
|
Benefits of decision models
|
Consistency, More Options, Relative Impact, Group Decisions, Mental Models -- "focus group on model vs emotion"
|
|
|
Why reluctance
|
Mental models good enough, human judgment still needed, opportunity not always clear, analysis (managers prefer action)
|
