Using Marketing Analytics to Measure Consumer Preferences and Inform Policy

<div class="footer-small">
<span>
<a href="#background">Background</a> | <a href="#choose">Conjoint Studies</a> | <a href="#packages">Software</a>
</span>
</div>

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# .center[Using Marketing Analytics to Measure Consumer Preferences and Inform Policy]

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### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M304 128a80 80 0 1 0 -160 0 80 80 0 1 0 160 0zM96 128a128 128 0 1 1 256 0A128 128 0 1 1 96 128zM49.3 464H398.7c-8.9-63.3-63.3-112-129-112H178.3c-65.7 0-120.1 48.7-129 112zM0 482.3C0 383.8 79.8 304 178.3 304h91.4C368.2 304 448 383.8 448 482.3c0 16.4-13.3 29.7-29.7 29.7H29.7C13.3 512 0 498.7 0 482.3z"/></svg> John Paul Helveston, Ph.D.
### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M243.4 2.6l-224 96c-14 6-21.8 21-18.7 35.8S16.8 160 32 160v8c0 13.3 10.7 24 24 24H456c13.3 0 24-10.7 24-24v-8c15.2 0 28.3-10.7 31.3-25.6s-4.8-29.9-18.7-35.8l-224-96c-8-3.4-17.2-3.4-25.2 0zM128 224H64V420.3c-.6 .3-1.2 .7-1.8 1.1l-48 32c-11.7 7.8-17 22.4-12.9 35.9S17.9 512 32 512H480c14.1 0 26.5-9.2 30.6-22.7s-1.1-28.1-12.9-35.9l-48-32c-.6-.4-1.2-.7-1.8-1.1V224H384V416H344V224H280V416H232V224H168V416H128V224zM256 64a32 32 0 1 1 0 64 32 32 0 1 1 0-64z"/></svg> Eng. Management & Systems Eng.
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:black;overflow:visible;position:relative;"><path d="M152 24c0-13.3-10.7-24-24-24s-24 10.7-24 24V64H64C28.7 64 0 92.7 0 128v16 48V448c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V192 144 128c0-35.3-28.7-64-64-64H344V24c0-13.3-10.7-24-24-24s-24 10.7-24 24V64H152V24zM48 192h80v56H48V192zm0 104h80v64H48V296zm128 0h96v64H176V296zm144 0h80v64H320V296zm80-48H320V192h80v56zm0 160v40c0 8.8-7.2 16-16 16H320V408h80zm-128 0v56H176V408h96zm-144 0v56H64c-8.8 0-16-7.2-16-16V408h80zM272 248H176V192h96v56z"/></svg> February 21, 2024

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# Hello World!

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### John Helveston, Ph.D.

Assistant Professor, Engineering Management & Systems Engineering

- 2016-2018 Postdoc at [Institute for Sustainable Energy](https://www.bu.edu/ise/), Boston University
- 2016 PhD in Engineering & Public Policy at Carnegie Mellon University
- 2015 MS in Engineering & Public Policy at Carnegie Mellon University
- 2010 BS in Engineering Science & Mechanics at Virginia Tech
- Website: [www.jhelvy.com](http://www.jhelvy.com/)

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## Technology Change Lab

> I study how consumers, firms, markets, and policy affect technological change, with a focus on accelerating the transition to low-carbon technologies

### .center[Electric & Sustainable Vehicle Technologies]

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### .center[Market & Policy Analysis]

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### .center[U.S. - China Climate Relationship]

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# What is Data Analytics?

.font120[The science of analyzing raw data to draw out<br>**meaningful & actionable insights** to **inform decision-making**]

Image from https://r4ds.hadley.nz/

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## I'm interested in questions like...

<br>

### - How can we get people to buy more efficient vehicles?

### - How will emerging technology like autonomous and electric vehicles compete against existing technologies?

### - Would people be willing to pay a premium to reduce pollution?

## **Answers depend on knowing what people want**

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# So I try to figure out what people want

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## Which feature do you care more about?

.cols3[
## .center[Battery Life?]
<center>
<img src="images/phone_battery.png" width=100%>
</center>
]

.cols3[
## .center[Signal quality?]
<center>
<img src="images/phone_signal.png" width=100%>
</center>
]

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## **Conjoint Analysis**:
## Use choice data to model preferences

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### .center[Use random utility framework to predict probability of choosing phone _j_]

<br>

### 1. `$u_j = \beta_1\mathrm{price}_j + \beta_2\mathrm{brand}_j + \beta_3\mathrm{battery}_j + \beta_4\mathrm{signal}_j + \varepsilon_j$`

### 2. Assume `$\varepsilon_j \sim$` iid Gumbel distribution

### 3. Probability of choosing phone _j_: `$P_j = \frac{e^{\beta'x_j}}{\sum_k^J e^{\beta'x_k}}$`

### 4. Estimate `$\beta_1$`, `$\beta_2$`, `$\beta_3$`, `$\beta_4$` via maximum likelihood estimation

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<br>

### `$P_j = \frac{e^{\hat{\beta}'x_j}}{\sum_k^J e^{\hat{\beta}'x_k}}$`

<center>
<img src="images/phone_price_sens.png" width=500>
</center>
]

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# Choose your own adventure

## [.red[Electric Vehicles]](#ev)
## [.orange[Low-carbon Fuels]](#fuel)
## [.yellow[Multi-modal Trips]](#modes)
## [.green[Autonomous Vehicles]](#av)
## [.blue[Electric Vehicle Incentives]](#incentive)

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## Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China

Yimin Liu, Ford

Elea M. Feit, Drexel U.

Erica R.H. Fuchs, CMU

Erica Klampﬂ, Ford

Jeremy J. Michalek, CMU
]

.rightcol[
_Transportation Research Part A: Policy and Practice_, 73, 96–112. (2015)
DOI: 10.1016/j.tra.2015.01.002
]

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.leftcol35[
## Chinese car buyers may be more willing to adopt full electric vehicles than Americans.
]

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# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

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## Choice at the Pump: Measuring Preferences for Lower-Carbon Combustion Fuels?

Stephanie M. Seki, CMU

Jihoon Min, CMU

Evelyn Fairman, CMU

Arthur A. Boni, CMU

Jeremy J. Michalek, CMU

Inês M. L. Azevedo, CMU
]

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background-color: #fff

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## On average, respondents WTP $150/ton CO2 avoided

- 26 mpg car
- 12-gallon tank
- Gas: $3/gallon

**A WTP of $150/ton CO2 avoided means increasing fuel price by 45%!**
]

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# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

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## Measuring consumer preferences for multi-modal trips

<br>

John P. Helveston, Assistant Professor, EMSE

Lujin Zhao, Ph.D. Student, EMSE

Saurav Pantha, MS Alumni & Visiting Scholar, EMSE

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<br>

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# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

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# Undercutting Transit?

## Exploring potential competition between autonomous vehicles and public transportation in the U.S.

<br>

John P. Helveston, Assistant Professor, EMSE

Leah Kaplan, Ph.D. Student, EMSE

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class: middle, center

## Imagine you are going out for an evening leisure activity - <br> Which transportation option would you choose/

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class: center, middle

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background-color: #fff
class: center, middle

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background-color: #fff
class: center, middle

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background-color: #fff
class: center, middle

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# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

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## Not All Subsidies are Equal:<br>Measuring Preferences for EV Financial Incentives

<br>

John P. Helveston, Assistant Professor, EMSE

Laura Roberson, Ph.D. Student, EMSE

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background-color: #fff
class: middle, center

# Which incentive option would you prefer?

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## Sample

- Used [formr.org](https://formr.org/) Survey Platform
- Fielded September, 2021
- Nationwide sample of Dynata panel

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### Immediate rebate is **$1,400** more valuable than tax credit

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class: center

### Immediate rebate even more preferred for **low-income households**

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background-color: #fff

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# [.green[Return to choices]](#choose)

# [.red[Skip to end]](#packages)

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# .blue[.center[[`logitr`](https://jhelvy.github.io/logitr/)]]

Fast estimation of multinomial and mixed logit models in R with “Preference” space or “Willingness-to-pay” space utility parameterizations.

[https://jhelvy.github.io/logitr/](https://jhelvy.github.io/logitr/)

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# .blue[.center[[`cbcTools`](https://jhelvy.github.io/cbcTools/)]]

Tools for designing choice based conjoint (cbc) survey experiments and conduction power analyses.

[https://jhelvy.github.io/cbcTools/](https://jhelvy.github.io/cbcTools/)

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<br>

# .center[.font150[Thanks!]]

### .center[Slides: https://slides.jhelvy.com/]

@JohnHelveston <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg><br>
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