ThinkVine UMich Presentation F

8/6/2019 ThinkVine UMich Presentation F

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6/1/2011 1

Marketing Mix Using

Agent-Based Modeling

November 17-18, 2010

Curt StengerKevin Li


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About Curt:

VP, Analytic Services

Experience in Marketing Science and Analytics

About Kevin:

Product Manager, ThinkVine

Background in new product consulting and forecasting Rules of engagement:

We would like this to be more of a guided discussion and not a one-

way presentation

Please ask questions whenever

A few words before we dive in


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Complexity in the world around us

Background from a 50000 ft view

Definition of complexity sciences

Generalizations and brief background

Drilling down to agent-based models (ABMs)

Why the need? Applying ABMs to the marketplace

Our methodology

Case studies

Our thought process behind this discussion


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From the beginning

Lets all think of processes that are complex and

uncertain in the world around us.


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What comes to Kevins mind?

We are all involved with stocks, bonds, mutual funds, etc.

The financial market is a complex and uncertain environment.

Oops!

(Uncertain)

A lot cooks in the

kitchen!

(Complex)


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Do you ever see a traffic jam coming?

Way too many cooks in

the kitchen! (Complex)

How did this

happen to

me?(Uncertain)

Ever think about why you always quote someone 4-6 hours when

youre driving 300 miles? Uncertainty and complexity.


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Forecasting Airline Demand

Parker, VIRTUAL MARKETS: THE APPLICATION OF

AGENT-BASED MODELING TO MARKETING SCIENCE, 2010

How can an airline manufacturer

determine how many planes to

build 30 years from now?


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Plug-In Hybrid Vehicles

Eppstein, An agent-based model for estimating

consumer adoption ofPHEV technology, 2010

How do you judge a new product

sales volume affect by macrocultural issues?


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How do people exit the building in a fire?


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Turning the tables around

Your turn.


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Others?

Why do birds

fly in a V?

How do you

win a war?

Why is your

brain shaped a

certain way?

Why do ants

build their

colonies this

way?


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Introducing the complexity sciences

A system in which large networks of components with no

central control and simple rules of operation give rise to

complex collective behavior, sophisticated informationprocessing, and adaptation via learning or evolution

(Mitchell, 2009)


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Ethnology of Simulation Modeling

From Roger Parker, VIRTUAL MARKETS: THE APPLICATION OF

AGENT-BASEDMODELING TO MARKETING SCIENCE


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Why the need for dynamic models?

t


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t

If I were to ask you to predict what will happen in the

future, what would you do?


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today

Best fit and extrapolate

Probably something close to this?


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But reality is not so simple

ttoday

Possible futures

Different things can happen!

We care about which path will happen and

how itll get there.


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Same example using ABM

ttoday

ABM


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ttoday

ABM What if?


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ttoday

ABM and... what if?


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ttoday

ABM or what if?


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ttoday

ABM or what if?

We care about the underlying assumptions. If we

understand the bottom-up behavior, we can generate the

aggregate output.


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Applying ABMs to the consumer marketplace

In a traditional model, TV drives sales.

In reality, TV influences consumers to buy more.


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A Simple Simulation

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Typical new product forecasts

use simple aggregate

assumptions about the effect

of media and word of mouth

when estimating the diffusion

of new products

Simple simulations like this oneallow the testing of the impact

of different levels of media on

the acceptance rate of a new

product at the consumer level.

Yellow people are users

Blue are target

Flags are ads

Ads and WOM influence targets to buy


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Todays general framework

Experience product

Use inventory

Talk about it!

Use media, see ads

Experience need!

Choose channel

Evaluate in-store

options

Choose brand, pack,

number


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How do we do it?

First, we create a representative sample of virtual consumers (50K+) inside

of our simulation environment from the bottom up. Each consumer agent

is different from one another demographically.


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How do we do it?

Then, we assign them media usage habits using known sources


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How do we do it?

Natural correlations affect sales separately from media, as in reality.

and build behavioral rules to link demographics to media consumption.

Media usage habits realistically impact media effectiveness:

Agents with more education

Also tend to earn more:

Agents years education positively

correlated with income

Older people tend to

earn higher incomes:

Agents age positively correlated

with years education and income

These higher earning agents are more

likely to purchase luxury goods:

If Agent income is in top X% of population

then probability to buy luxury watch +Y%

Younger people on

average use more Internet:

Agents age negatively correlatedto digital media minutes

Digital Ads are seen more on average by

younger consumers than other demographics:

Agents digital minutes positively correlatedto probability to see a digital ad


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How do we do it?

Each agent is different from another, but aggregate averages still hold true

for population (heterogeneity).


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How do we do it?

Simulated Interactions

Consumers Marketing

Lastly, we simulate likely sales outcomes based off of how

these consumers react to different mixes of marketing activity.


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How do we do it?

We recreate the past 2 years of sales from the bottom up by building rules that

explain why those sales outcomes occurred.

Once trained, we compare the most recent 6-months of actual sales (data not used to

train the model) to a simulation of the most recent 6-months of sales.

Once the model has been calibrated & validated, we can begin to project forward and

provide clients with the ability to run what if planning scenarios.

Tr i Pr v s

The model is trained by using your demographic, marketing & sales data. Track against hold out data Forecast

2 Years Sales Hist ry 1-2 Quarters Future

Trai theTool = CALIBRATE

Prove theTool = VALIDATE

Use theTool = SIMULATE

1

2

3

Simulated

Actual


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Wit a kly E f 4. , av str g vi c t at t E ra l fits

ist rical sal s ata v ry ll.

l Fit

Stat

as s (K)

Our g al is t ac i v

a

kly

E (

a

bs

lut

rc

tag

f Err r), f 15 r

l ss.

ctual

Si ulat


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Marketing Impacts FY10

Stat

Cases

(K)

% of

Stat

Cases

% Mkt

Vol

Spend

($K)

%

Spend

Eff.

IndexROI

ROI

+/-

0 0.

0.

0.

- - -

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- - -

177 1.7

.

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80

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107

8 31.7

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137

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.0 0.01

708

.

Total 10

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FY10 sees an investment of $10.8MM in TV with a modest $.29 ROI

Trade has highest level of ROI

Print, ASM, and PR have high variability due to low spends


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Long-term Impact of TV

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Base Volume (87 Week Total) = 320.26MM

Lift from Network TV = 62.66MM (11.23%)

Base Volume (MM) Lift from Network TV (MM) Network TV Reach


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ThinkAhead: Graphical Representation

We model the path to purchase for your product, from the

marketing levers you pull all the way to consumer purchase.


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Incorporates the consumer (50K different consumers)

Demographics Media consumption behavior

Shopping behavior

Natural consequences of the model mimics reality, i.e.emergence

Sales decline because people leave, not because your marketingsuddenly contributes negative volume

The right people buy Can get a read on segmentation and loyalty

Heavy media users tend to buy more and vice versa

Saturation occurs naturally due to non-linear assumptions Awareness, purchasing, etc.

Future simulations and past diagnostics much more realistic

Benefits


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Founded in 2000, ThinkVine is an Analytic Services and SimulationSoftware consultancy, with a strong focus on market & consumermodeling, analytics and decision support systems.

In addition to traditional analytic techniques, we also apply leading-edge techniques from the complexity sciences to tackle toughbusiness problems. These include:

Agent-Based Models & Simulations

Genetic algorithms

Neural networks

Game Theory

ThinkVine works with some of the worlds top companies across abroad range of industries such as: consumer packaged goods,advertising & media, energy, technology, and financial services.

ThinkVine: Who We Are


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Our core product is the ThinkVine ThinkAhead platform

Marketing simulation and planning tool Launched in 2009

Historical diagnostics with robust forecasting capability

Built as an agent-based model

Delivered through software as a service

We answer complex business questions such as:

Attribute: What did digital do for me in the context of other tactics?

Evaluate: 8MM Facebook fans or another $5MM in trade?

Forecast: If I shift 20% of print into digital, what will happen?

Improve: Whats the best way to close the volume gap for this fiscal? Target: Am I hitting my target segments with my media?

Portfolio: Should I spend on cat food or canned vegetables?

And more

Core Product


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Our Clients

Documents

ThinkVine UMich Presentation F