Juan tello advanced quant - 2011

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Advanced Quant Techniques July 14, 2011

Product Por4olio and Revenue Op9miza9on for CPG

Juan Andrés Tello, SKIM

Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011

Juan Andrés Tello SKIM US Director

Product Portfolio and Revenue Optimization for CPG

expect great answers


Outline

1.  Mo=va=on for Revenue Op=miza=on (RO)

2.  Concept of RO

3.  RO requires a MR shiS from insight to foresight

4.  Building blocks of a RO system

a.  Consumer behavior models

b.  Demand forecas=ng

c.  Constrained op=miza=on tools

5.  Some RO strategies

6.  Delivering op=miza=on results to clients


Revenue Op=miza=on -‐ Mo=va=on

•  Maximize: Profits = f(Pricing, Product por]olio composi=on | Selling channel)

•  Turns data into ac=onable foresight tools for clients •  Determine op=mal pricing/por]olio strategy within given constraints

•  RO pioneers: fixed capacity industries


•  Solu=on: price differen=a=on (some=mes controversial – Smart vending machines)

B

How to charge the max willingness to pay to each customer?

Dem

and

0 $5 $10 $15 Price

1,000

d(p)

ß marginal cost

A

C


Traditional MR function

Business contribution team

Strategic insight organization

Strategic foresight organization

RO requires a MR shiS from insight to foresight 90% of consumer-‐facing companies have a Consumer Insights (CI) func=on in early stages of development (1) or (2)

1 2

3 4

Source: BCG Consumer Insight Benchmarking (May 2009)

Consumer insight as a source of

competitive advantage

MR as an order-taking

function

BCG’s CI stages of development


Building blocks of a RO system

1.  Quan=ta=ve models of consumer behavior à Choice based Conjoint (CBC)

2.  Demand forecasts à Market simulator

3.  Constrained op=miza=on tools à Search algorithm of op>mal solu>on within market constraints


1. Choice based Conjoint

•  Proven and unbiased research technique to model consumer preferences and market heterogeneity

•  Rooted in U=lity Theory (Von Neumann–Morgenstern)

•  Preferences es=ma=on process has evolved over =me:

1.  Aggregate Logit model (one size fits all)

2.  Latent class (segmenta=on)

3.  Hierarchical Bayes (individual level)

•  Choice task resembles purchase behavior process


1. Choice tasks within a compe==ve context


2. Market Simulator: from consumer preferences to market shares, to revenue forecas=ng •  Volumetric adjustments and calibra=ons

•  Ability to test unlimited pricing /por]olio strategies and poten=al compe==ve reac=ons

•  In its simplest form, the simulator is a “show of hands” from respondents given a number of choice op=ons

Input prices

Market share output

Revenue output

Change por]olio

composi=on


3. Searching for the op=mal: define the feasible space first

Total space of possible solutions

Constrained space of feasible solutions

Sample of solutions within constraints


3. Searching for the op=mal: define objec=ve func=on & apply search algorithm

Max Revenue (Optimal solution)

Revenue Surface


3. It’s not only about finding the winning solu=on, but about the pa>erns observed

• While the main goal is to uncover the strategy that maximizes revenue, ask yourself: •  What makes it the op=mal solu=on? •  Are there alternate strategies with different tradeoffs yielding posi=ve results?

•  In this example: •  80,000 scenarios generated •  40% yield gains in both revenue and share. Cluster analysis is used to further group and interpret

Focus on upper right quadrant

Max Rev gain = 8%


Some RO strategies

1.  Maximize volume share profitably (capping revenue loss)

Balanced “investment” strategy to grow customer base

2.  Maximize revenue while capping volume loss

Ideal situa=on, not always feasible; will depend on price elas=city

3.  Game theory strategies: compe==ve reac=ons


Delivering op=miza=on results to clients

A few insights for a successful deployment: •  Involve key stakeholders from different func=ons early in the game •  Plan accordingly

•  Kick-‐off: constraints from every func=on are expressed and discussed •  Delivery: results are discussed in a workshop style

•  Dynamic session •  Create tools that allow clients to interact with the data (e.g. ability to ac=vate/deac=vate constraints, rank and select scenarios)

•  Don’t be afraid to show the “raw” data; involve stakeholders in the analysis •  As always, be clear about the model’s assump=ons and limita=ons


Q & A

Andrew Jeavons Survey Analy=cs

Juan Andrés Tello SKIM


contact us or follow us online!

SKIM | Consumer Goods

Juan Andrés Tello | Director US [email protected] | +1 201 963 8430

twi>er.com/ skimgroup

facebook.com/ skimgroup

linkedin.com/ company/skim

youtube.com/ skimvideos skimgroup.com


contact us or follow us online!

SKIM | Locations New York, USA Juan Andrés Tello [email protected] +1 201 963 8430 Rotterdam, NL Mini Kalivianakis [email protected] +31 10 282 3535

twi>er.com/ skimgroup

facebook.com/ skimgroup

linkedin.com/ company/skim

youtube.com/ skimvideos skimgroup.com

Geneva, Switzerland Vicky Nef [email protected] +41 22 747 7519 London, UK Debora Corfield [email protected] +44 203 178 6910

Juan tello advanced quant - 2011

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