Marketing Analytics Ch. 5

Front 1

Marketing Oriented Approach to Strategy

Back 1

-SWAT Analysis -- Strength Weakness Opportunity Threats
-Objectives and Strategy Generation & Evaluation
-Added Strategic Marketing Dimensions (MS Office Suite)

Front 2

Seven Limitations of Typical Marketing Strategy -- Improper Analytic Focus

Back 2

Market defined by customer needs & values instead of physical characteristics
Improved forecasting models

Front 3

Seven Limitations of Typical Marketing Strategy -- Functional Isolation

Back 3

Integration among departments
Cost dynamics models

Front 4

Seven Limitations of Typical Marketing Strategy -- Ignoring Synergy

Back 4

Marketing Mix analysis
Product Line methods
(MS office Suite)

Front 5

Seven Limitations of Typical Marketing Strategy -- Short-Run Analysis

Back 5

Dynamic Models Product Life Cycle analysis

Front 6

Seven Limitations of Typical Marketing Strategy -- Ignoring Competition

Back 6

Competitive Analysis models Steal market share from others to grow

Front 7

Seven Limitations of Typical Marketing Strategy -- Ignoring Interactions

Back 7

Market Definition models Interaction between segments

Front 8

Seven Limitations of Typical Marketing Strategy -- Lack of Integrated View

Back 8

Strategic Approach PIMS, Product-Portfolio methods
(based on how businesses are opporating .. what lessons can we learn)

Front 9

Market Demand and Trend Analysis -- Potential Market

Back 9

Set of customers showing interest

Front 10

Market Demand and Trend Analysis -- Available Market

Back 10

Customers with interest AND income & access

Front 11

Market Demand and Trend Analysis-- Qualified Available Market

Back 11

Available market qualified to buy (18+)

Front 12

Market Demand and Trend Analysis --Served/ Target Market

Back 12

Part of qualified market that firm pursues

Front 13

Market Demand and Trend Analysis -- Penetrated Market

Back 13

Customers who have already purchased

Front 14

Forecasting: Judgmental Methods -- Salesforce composite

Back 14

Salespeople know market Adds credibility to forecasts; “Sanity Check” But: Can be overly influenced by short-term But: Might guess low to reduce quotas

Front 15

Forecasting: Judgmental Methods -- Jury of Executive Opinion

Back 15

Combine views of key stakeholders Executives, Suppliers, Consultants But: Tendency to over-weight some areas But: Availability of executives

Front 16

Forecasting: Judgmental Methods -- Delphi Models

Back 16

Developed by RAND in 1950s Forecast which US cities Russia would attack Experts give their opinions Opinions are aggregated, then given back Experts re-forecast until consensus reached

Front 17

Forecasting: Market Analysis Methods -- Buyer Intentions

Back 17

Ask buyers how much they plan to buy Administer survey using probability sample But: Intentions do not always reflect behavior But: Non-response bias (not intending to buy)

Front 18

Forecasting: Market Analysis Methods -- Market Tests

Back 18

Run small-scale test in selected market **Tests what people actually do, not what they say** But: Fairly expensive But: Exposes new products to competition

Front 19

Forecasting: Time Series -- Naive Methods

Back 19

Plot out sales data over past few years Graphically “eyeball” estimate for next year/ quarter Good: Fast & easy; Very popular; Good sanity check But: Different people interpret differently

Front 20

Forecasting: Time Series -- Moving Averages

Back 20

Data incorporates trend (good) + noise (bad) Filters out noise by averaging over time Sales (time = t) = [S(t-1)+S(t-2)+S(t-3)] / 3 Fast & easy; Very popular, but may mask complexity

Front 21

Forecasting: Time Series -- Exponential Smoothing

Back 21

Variation of Moving Average, available in Excel Substitute exponential equation for average Recent values weighted heavier Same problems as moving averages (same as moving avgs but with excel)

Front 22

Forecasting: Time Series -- Box-Jenkins Method

Back 22

also: ARMA: Auto-Regressive and Moving Average Uses successive differences between years for trend Fairly powerful, but **very complex** Multivariate version available: MARMA

Front 23

Forecasting: Causal or Correlational Analysis -- Regression Analysis

Back 23

Causal methods predict behavior using equations Assumption: Sales related to size of company Sales = a(0) + a(1)(Number of employees) Available in Excel (know what all the variables are)

Front 24

Forecasting: Causal or Correlational Analysis -- Econometric Models

Back 24

Huge, complex models to simulate economy Used today only in niche applications (like regression but with tons of models)

Front 25

Forecasting: Causal or Correlational Analysis -- Input-Output Analysis

Back 25

Provides estimates of industrial growth Based on Inputs = Outputs (conservation of mass) Consumer sales drive Retail sales + Intermediate Used in 1960s & 1970s, rarely used today

Front 26

Forecasting: Causal or Correlational Analysis-- Neural Networks

Back 26

Relates inputs (advertising) to outputs (sales) Emulates organization of the human brain Network = Interlinked neurons (complex models) “Activation Functions” to make neurons “fire” Modeler “trains” network to behave correctly But “Black Box” problem; e.g. What is effect of ads?

Front 27

Forecasting: One Approach

Back 27

**Gather Gather** data: Historical sales: By region, By product Ask Salespeople Ask other knowledgeable stakeholders: Suppliers, etc.

Plot **Plot out existing data **Smooth if necessary

Causal **Run regression analysis** Determine constants for each input variable Consider Neural Nets if no structure known or data incomplete

Triangulate Aggregate data to determine “final” estimate (important)

Test Market Consider test market if substantial investment at stake

Refine Refine forecast over time, as new data becomes available

Front 28

Product Life Cycle -- Introduction

Back 28

Slow growth; Negative profit Strategy: Focus on early adopters (ppl that bought iphone first)

Front 29

Product Life Cycle -- Growth

Back 29

Rapid market acceptance Strategy: Focus on competitors (IPAD now, next QPAD, GPAD, etc)

Front 30

Product Life Cycle -- Maturity

Back 30

Slowing sales growth; maximum profits Strategy: Focus on keeping product fresh (IPOD)

Front 31

Product Life Cycle -- Decline

Back 31

Sales dip; profits erode Strategy: Consider new version (New Version "Scallop -- limited edition)

Front 32

Product Life Cycle: Technology Substitution -- Substitution

Back 32

Demand for computing continues Product form changes with technology (concentrate on need or want it is trying to fill not actual product)

Front 33

Sales Dynamics: Scale & Experience Effects -- Market Share

Back 33

Primary determinant of business profitability 10% market share increase  5% ROI increase (be #1 or #2 not #3)

Front 34

Sales Dynamics: Scale & Experience Effects -- Economies of Scale

Back 34

Lower costs through size Reduction of excess capacity Systems more cost-effective per unit Enhanced procurement power

Front 35

Sales Dynamics: Scale & Experience Effects -- Experience Effect

Back 35

Lower costs through learning Operator innovations: Harley & Chromed Aluminum Management innovations: KanBan Process innovations: Just in Time

Front 36

Price-Cost Relationship

Back 36

Phase A Start-Up: Price < Cost, Losing money

Phase B Price umbrella: Lid on prices to discourage new competition Trade lower current profit for future market share

Phase C Shake-Out: Competitor lowers prices as costs decline

Phase D Stability: Margins return to normal; Commodity status