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2009-2010 ISMS-MSI Practice Prize Competition

Dynamic Marketing Budget Allocation across Countries, Products, and Marketing ActivitiesMarc Fischer (University of Passau) Snke Albers (Christian-Albrechts-University at Kiel) Nils Wagner (University of Passau) Monika Frie (Bayer AG)

Cambridge, MIT, January 15, 2010

Bayer in 2006 a healthy company

EUR 29 bn (US$ 40 bn) sales 106,000 employees worldwide EBITDA EUR 5.5 bn (US$ 7.8 bn) Very comfortable 20% EBITDA margin

Source: Bayer Annual Report (2006) Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

1

Bottom-up allocation process at Bayer

Local country managers report their budget needs upwards

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

2

lead to inefficient and suboptimal allocation decisions

Gut feeling guides management decisions Very strong influence of negotiation skills Local managers learn how to manipulate allocation process

Politics and tensions shape the decision process instead of fact-based discussions

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

3

Money is left on the table

1

Missed opportunities to promote markets with highest growth potential

2

Insufficient resources for countries and products with highest ROI

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

4

Marketing theory confirms that

The profit improvement potential from better allocation of a total marketing budget is much higher than from optimizing the total budget!

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

5

Video message Dr. Monika Frie (Bayer AG)

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

6

Contribution to marketing theory and practice

Innovative allocation tool

Fact-based and focused on relevantcharacteristics

Insights into the solution structure Application to Bayers Primary Careportfolio (36 products with sales > US$ 4 bn)

Implementation

Impact

Profit improvement potential from betterallocation > 50% (DCF=US$ 685 m)

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

7

AGENDA

The Allocation Problem Company and Market Background Problem Solution The AllocationHeuristic

Implementation and Impact at Bayer

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

8

Bayers Primary Care business (EUR 3.1 bn / US$ 4.5 bn)

Therapeutic areas Hypertension Infectious diseases

Leading brands Adalat Avelox Ciprobay EUR 626 m (US$ 870 m) EUR 445 m (US$ 618 m) EUR 338 m (US$ 470 m) EUR 341 m (US$ 474 m) EUR 304 m (US$ 423 m)

Erectile dysfunction Diabetes

Levitra Glucobay

Source: Bayer Annual Report (2008) Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

9

Pharmaceutical marketing activities

Detailing Ads in professional journalsWerbungactivities Other in Fachzeitschriften

Physician oriented

(e.g., symposia invitations)

Marketing activities Direct-to-consumer advertising (only US) Patient oriented Below the lineactivities (e.g., PR)

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

10

AGENDA

The Allocation Problem Company and Market Background Problem Solution The AllocationHeuristic

Implementation and Impact at Bayer

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

11

Constrained dynamic profit maximization problem

(tT)

Discount Countries Products Profit con- tribution Time factort kit

Unit saleskit

(kK)

(iIk)

k i Activities Marketing expense(nNi)

kint

Max!

Discounted net value of product portfolio where Unit sales = f(life cycle, marketing expense, etc.)

Restrictions

(1) (2) (3)

k i n Marketing expensekint = Total BudgettMarketing effects decay at a constant rate over time Boundary conditions (e.g., positive marketing budgets)

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

12

Optimal solutionOptimal allocation weightkint

Optimal budgetkint =

Products Activities Optimal allocation weightkint Countries(kK) (iIk) (nNi)

Total budget

t

Optimal allocation = weightkint

Profit con- Optimal unit Optimal mktg.+ Optimal growth tributionkit saleskit elasticitykint elasticitykit 1 + Discount rate Marketing carryoverkin

Optimal values only available from numerical optimization

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

13

Heuristic allocation weight

Last periods marketing elasticity Discount Marketing 1+ rate carryover

Profit con- Last tribution periods margin (%) revenue

Expected revenues in T periods Last periods revenues

1(Discounted) long-term marketing effectiveness

2Size of profit contribution

3Growth potential(T = Planning horizon)

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

14

Performance of heuristic allocation rule (16 experimental situations)

Both scenariosDegree of suboptimality (profits)

Monopoly

Duopoly

5% 4% 3% 2% 1% 0% 1 3 5 7 9 11Planning cycle

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

15

Data for empirical application at Bayer

10 years (1996-2006) of quarterly marketing and sales data Countries: France, Germany, Italy, Spain, U.K.

Types of product: Hypertension, Antiinfectives, Erectile dysfunction, Antidiabetics

525 Bayer and competitor products

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

16

Market response model

Type

Double-log model Accommodates nonlinear andinteraction effects

Marketing mix

Stocks for own and total competitive marketing expenditures Own and competitive price Brand/quality effects

Other variables

Asymmetric life cycle Order of entry Country and seasonal effects

Fischer, Albers, Wagner, and Frie (Dynamic Marketing Budget Allocation)

17

Model validation and robustness

Model fit

Very good in-sample fit with R > .93

Holdout prediction

High R (>.92) and low MAPE(