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Direct Interaction Advertising and Its Effects: A Study of reddit’s Ask Me Anything

By Zakary Campbell

Date: December 2014

Abstract

Direct interaction advertising is my own term for a new form of advertising that has emerged in recent years along with the rise of social media. Using information provided by a number of instances of a reddit feature, “Ask Me Anything,” and information from Google Trends, I estimate the impact of this sort of advertising on interest both in the person doing the interaction and the product or service being advertised. There is a large positive effect on interest in the person and a smaller but significant effect on interest in the product or service. These effects do not seem to depend upon the reception of the AMA.

I. Introduction

Online and digital advertising have opened up a number of possibilities previously

unavailable to advertisers. Advertisers can now more effectively target a specific

demographic at a time when they are presumably in need of a specific product or

service. Theoretically, given this targeting ability, advertising online should be extremely

effective. However, the effects of online advertising remain disputed (Lewis and Reiley

2014).

Online advertising is an extremely varied practice. In one short online session,

one might encounter banner ads, video spots, branded viral content, subtle product

placement, and a number of other forms of online advertising. Although it may be useful

to lump these into one category for the sake of research, advertisers, themselves,

probably find it more useful to consider the effects of each of the many different types of

online advertising separately.

This paper will focus specifically on the form of online advertising that I term

direct interaction advertising. This is a particularly new form of advertising that has

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begun to thrive due to the proliferation of social media in recent years. Direct interaction

advertising occurs when someone involved with a product or service reaches out

directly to potential consumers through social media to interact with them. The

interaction, itself, does not necessarily directly refer throughout to the product or service

being advertised; indeed, this is rarely the case. Often, the individual engaging in the

advertising will merely briefly refer to the product or service s/he is advertising in some

sort of introduction or signoff, and the content of the interaction will be entirely up to the

potential consumers with whom the individual interacts. The effects of direct interaction

advertising are presumably twofold. If the advertising works as it is intended,

consumers’ interest in the product or service being advertised will increase. Similarly,

consumers’ interest directly in the person doing the advertising is likely to increase.

There are a number of examples of direct interaction advertising. Celebrities

often host Twitter Q&As wherein people can participate by using a specific hashtag or

tweeting directly at the celebrity hosting the Q&A. Facebook pages for a person or

product often host similar events, allowing people to post questions on said page for a

given period of time. Some people from more tightly defined niche industries often visit

forums dedicated to that industry to interact with consumers.

The present paper will focus on the reddit Ask Me Anything (AMA) to measure

the effects of direct interaction advertising. reddit is a content aggregation website that

is divided into a growing list of forums, called subreddits, focused on a specific topic. As

of December 8, 2014 at 5:54 pm, there are 537,389 subreddits according to

redditmetrics.com. One of the most followed of these subreddits is /r/IAmA. This

subreddit is a dedicated forum for AMAs.

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An AMA is a post on /r/IAmA in which some person—generally a celebrity or

somebody with a particularly interesting occupation—answers questions asked by the

reddit community for some period of time. AMAs can last anywhere from 15 minutes to

an entire day, but they tend to last about one hour. Although AMAs occur on one

particular day, the posts generally remain on the front page of /r/IAmA throughout the

next day, and the posts tend to get quite a bit of traffic both on the day of the post and

the day after.

As mentioned above, this type of advertising is quite new, so there is not, to my

knowledge, any prior work that has studied its effects. However, there does seem to be

a feeling in the advertising community that it can be effective; one PR strategist

describes the AMA as something that “can generate hundreds of thousands of

impressions and drive fans into a frenzy. It can reinvigorate an established persona or

hoist a completely obscure or unusual person into the Internet spotlight” (Holiday 2013).

This paper seeks to be the first to empirically answer questions regarding this form of

advertising. The underlying question for all of this research is how well direct interaction

advertising functions as a form of advertising. First, I address the question of how much

this advertising affects interest in both the products advertised and the people doing the

AMA. Second, I question whether the effect is greater if the post is more well-received.

Section II outlines the sources of the data in this paper, including AMAs,

themselves, and Google Trends. Section III outlines the estimated regression model.

Section IV presents the resulting positive observed effect of direct interaction

advertising on interest. Section V discusses the results, and Section VI concludes and

presents the major limitations of the analysis.

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II. Data

reddit is a constantly evolving website; there are already millions of users, with

more joining every day. All of this is simply to say that which AMAs are studied is likely

to have some effect on the measured effects of these AMAs. In an attempt to best

quantify the effect that an AMA has currently, the data is restricted to AMAs that have

occurred within the past year. For the sake of convenience in gathering the data, then,

AMAs in the dataset come from the section of /r/IAmA labeled “Top This Year.”

Specifically, I use 95 AMAs from this list.

Each AMA contains four data points of interest to this study: the name of the

person doing the AMA, the product that s/he is advertising, the date of the AMA, and the

points that the AMA scored (Figure 1). Points are a measure associated with every post

on reddit. It is equal to the number of “upvotes” the post receives minus the number of

“downvotes” the post receives. Generally, if reddit users choose to vote, they upvote the

posts that they enjoy and downvote the posts they dislike or consider irrelevant. Thus,

points can be considered a proxy for how well-received a given post is.

Given the date of an AMA, the Google Trends data surrounding and during an

AMA can be found at google.com/trends. Google Trends is a site that allows anyone

with an internet connection to examine search volume for any search term over time.

Google terms the search volume for a query in any time period the “interest” in that

query. Interest is normalized to a 0-100 scale, where 100 is the greatest search volume

in the time period of interest, and other numbers represent the percentage of this

greatest search volume.

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For Trends data for people, the search term is the person’s first and last name.

For products, the search term is mostly simply the full name of the product. Names of

some products were subjectively deemed too common to be exclusively referring to the

products (i.e., Selfie); the search terms for these were in the form [product name]

[product type] (i.e. Selfie show) (See Appendix A). For each product and person, the

data contains interest data from one week before the AMA until one week after the

AMA. The data is restricted in this way to combat attributing the effect of concurrent

advertising not associated with the AMA to the effect of AMA. Furthermore, for any AMA

which did not contain an interest value equal to 100 in the given time frame, the data

was normalized in the same way as Google Trends data by multiplying each value by

100 and dividing by the largest interest value for that person/product. This is to ensure

that all of the data are measuring the same thing: the percentage of the highest value of

interest from one week before to one week after the AMA. Some AMAs were dropped

when comparing product interest due to a lack of available Trends data.

A major strength of this data is that it represents a good gauge of interest. It is

unlikely that somebody will search for precisely somebody’s first and last name or

precisely the name of a movie or television show, movie, book, etc. without having

some sort of interest in that product. Choi and Varian (2011) have shown that “simple

seasonal AR models that include relevant Google Trends variables tend to outperform

models that exclude these predictors by 5% to 20%.”

Unfortunately, there are also some weaknesses to the data. As mentioned

above, data from Google Trends is normalized. Thus, this data does not provide

information on the absolute effect of AMAs on interest, and it precludes accurate

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comparison across AMAs. An interest value of 100 for millionaire playboy Robert

Downey Jr. is likely to differ markedly in absolute terms from an interest value of 100 for

astronaut Chris Hadfield. Trends data also includes information on web queries coming

from all sources, so there is no way to identify precisely what additional interest comes

from reddit users and word-of-mouth from the AMAs. Additional web traffic could be

coming from anywhere; the hope is that by restricting the time horizon, this concern

becomes irrelevant.

III. Methods

Users can continue to revisit an AMA into perpetuity, but the bulk of visits come

while the AMA is on the front page of /r/IAmA which generally only occurs on the day of

the AMA and the day after the AMA. Thus, I estimate the following regression model for

both interest in people and interest in products:

(1) 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 = 𝛼 + 𝛽𝐷𝑎𝑦𝑂𝑓 ∗ 𝐷𝑎𝑦𝑂𝑓 + 𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 ∗ 𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 + 𝜀𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡

𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 is the amount of interest in a person or product; 𝛼 is the expected interest on a

day without an AMA occurring that day or the day before; 𝐷𝑎𝑦𝑂𝑓 and 𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 are

indicators for the day of an AMA and the day after and AMA, respectively; 𝛽𝐷𝑎𝑦𝑂𝑓 and

𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 are the estimated effects of an AMA on interest on the day of an AMA and the

day after an AMA, respectively; and 𝜀𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 is an error term that is assumed to be

uncorrelated with 𝐷𝑎𝑦𝑂𝑓 and 𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟.

For my initial estimate of the regression, I use all 15 days of interest. However, in

anticipation of challenges that this is still misattributing the effects of concurrent

advertising to the AMA, I estimate a second form of the regression in which I limit the

time horizon to only the period from the day before the AMA to two days after the AMA.

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If the effects decrease with this alteration, the original regression is likely picking up

some effects of concurrent advertising. However, it seems unlikely that concurrent

advertising suddenly spikes the day of and after an AMA when compared to the day

before and the day two days after. Furthermore, only two AMAs coincided with the day

of or the day before the release of their advertised products, and these are removed in

the limited regression.

After determining whether there is an observable effect to an AMA, I determine

whether this effect depends on the reception of the AMA. To do so, I estimate the

following regression model separately for people and products:

(2) 𝐸𝑓𝑓𝑒𝑐𝑡 = µ + 𝛾 ∗ 𝑃𝑜𝑖𝑛𝑡𝑠 + 𝜀𝐸𝑓𝑓𝑓𝑒𝑐𝑡

𝐸𝑓𝑓𝑒𝑐𝑡 is the combined effect of the AMA on the day of and the day after the AMA; µ is

the expected effect of an AMA that receives zero points; 𝛾 is the change in the 𝐸𝑓𝑓𝑒𝑐𝑡

given an increase of one point; and 𝜀𝐸𝑓𝑓𝑒𝑐𝑡 is an error term. I first calculate the point

estimates of 𝐸𝑓𝑓𝑒𝑐𝑡 for each AMA by regressing interest in each AMA on an indicator

variable for being the day of or the day after the AMA then regress these estimates on

the points for each AMA.

I expect competing effects in (2). On the one hand, AMAs with a high number of

points are well-liked which probably piques users’ interest in the person and the product

being advertised. In this case, 𝛾 should be positive. On the other hand, AMAs with a

higher number of points are probably done by people who are already well-known and

liked, so the AMA does not increase awareness much for the person or product being

advertised because these measures are already high. In this case, AMAs with low

scores should exhibit a greater effect on interest, and 𝛾 should be negative.

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IV. Results

Figure 2 shows the mean interest in people and products over time starting one

week before the AMA and ending one week after the AMA. The effect on person

interest is striking, with mean interest nearly doubling on the day of the AMA, increasing

further on the day after, then immediately dropping back to the previous levels two days

after the AMA. There appears to be some effect on product interest though the effect

appears smaller and does not exhibit the same immediate fade away after the time of

the expected effect.

Table 1 shows estimates of the effects of an AMA on interest in people. Columns

1-3 estimate the effects using the entire sample; columns 4-6 limit the sample to interest

from the day before the AMA to interest two days after the AMA and drop observations

of AMAs that coincide with the release of a product that is being advertised in the AMA.

Columns 3 and 6 correspond to the model outlined. Using the full sample, estimates of

𝛽𝐷𝑎𝑦𝑂𝑓 and 𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 are 33.43 and 43.69, respectively. When the sample is restricted,

the estimate of 𝛽𝐷𝑎𝑦𝑂𝑓 increases to 38.68, and the estimate of 𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 drops to 28.26.

Each of these estimates is statistically significant at a 99 percent confidence level.

Table 2 show the estimates of the effects of an AMA on interest in products. As

with the previous table, columns 1-3 use the entire sample, and columns 4-6 limit the

sample in the same way as discussed above. Once again, columns 3 and 6 correspond

to the model above. Using the full sample, estimates of 𝛽𝐷𝑎𝑦𝑂𝑓 and 𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 are 9.02

and 15.19, respectively. When the sample is restricted, the estimate of 𝛽𝐷𝑎𝑦𝑂𝑓 slightly

increases to 9.61 while the estimate of 𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟 drastically decreases to 3.22. Both

estimates are statistically significant with 95 percent confidence in the model using the

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full sample. The estimate of 𝛽𝐷𝑎𝑦𝑂𝑓 remains significant while the estimate of 𝛽𝐷𝑎𝑦𝐴𝑓𝑡𝑒𝑟

loses statistical significance when the sample is restricted.

Figure 3 plots the point estimates of the effects of each AMA on interest in

people and products against the points that each AMA received. It certainly seems that

the number of points an AMA receives has no direct bearing on its effect on interest.

Table 3 confirms this intuition. 𝛾 is statistically indistinguishable from zero both in the

regression concerning people and the regression concerning products.

V. Discussion

AMAs appear to have a large positive effect both on interest in people who do

AMAs and on interest in the products that they advertise. The magnitude of these

effects may not be obvious at first glance. Recall that a value of interest in this study

corresponds to the percentage of the greatest number of Google searches for a term in

the time period one week before to one week after the AMA occurs. Compared to the

days immediately surrounding the AMA, interest in a person’s name increases by 38

percentage points on the day of the AMA and 28 percentage points the day after.

Interest in a product name increases by 9 percentage points the day of the AMA and—

though not statistically significant—3 percentage points the day after.

The implications for direct interaction advertising are huge. The cost of this form

of advertising come exclusively in the form of the time that one person gives up to do

the AMA. Unlike public appearances, this time does not involve transportation, makeup,

wardrobe, and similar time costs to drastically improving one’s appearance (people

generally take a single picture as proof that it is indeed them doing the AMA, but

preparation for this presumably takes only a fraction of the time that it takes to prepare

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for a public appearance). On the contrary, the cost to an AMA is about one hour of

one’s time sitting in front of a laptop answering various questions from reddit users.

Assuming that limiting the data to such a short time horizon successfully fully

attenuates the effects of concurrent advertising, AMAs appear to be much more

successful in generating interest than other forms of advertising. The success is,

however, short-lived, disappearing after only two days since the AMA. It cannot be

inferred from the data in this study, but it is entirely possible that, while interest

measurable by Google Trends fades away, general interest persists beyond the AMA. If

the aim of advertising is to increase consumer information and knowledge of a

product—or even, in this case, a person—this aim can likely be achieved by the initial

Google search for a product (or person). An initial search leads a user to numerous

different sources of information on products and people, so the fading away of

measurable interest may be due not to a fading away of actual interest but a fading

away of the need to educate oneself further. In simpler terms, people may find out all

that they need to know about a product or person from the information that they gain

from one Google search.

Finally, the positivity of reception of an AMA appears to exhibit the conflicting

effects described above. Advertisers cannot expect more popular AMAs to have a

greater positive effect on interest in products or people. Sometimes with more popular

AMAs, the fact that the AMA is more liked prevails, and higher points correspond to

larger positive effects on interest. Other times, the fact that people and products

associated with higher-scoring AMAs are already more popular prevails, and higher

points correspond to smaller positive effects on interest.

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VI. Conclusion and Limitations

Direct interaction advertising appears to be not only a good form of advertising

but an excellent form of advertising. The effect is huge for people; up-and-comers in the

entertainment industry should probably look to AMAs as an effective way to boost their

own image early. The effect is not quite as large but still striking for products;

filmmakers, actors, and authors should incorporate AMAs into their digital advertising

strategies as a way to increase awareness and knowledge of their products.

As a note, most of the products and services that were dropped due to

insufficient Trends information were books and charities. It seems that the AMA works

best as a tool for advertising in the film and television industries. Perhaps authors or

social advocates would be better served spending their time on other forms of

advertising.

The fact that the reception of an AMA does not have a significant effect on the

amount that an AMA affects interest people or products may be good news for

advertisers of lesser-known people or products. Even if a product does not do so well in

terms of receiving upvotes and downvotes from reddit users, it is entirely possible that

the effect on interest will be just as great as or greater than the effect for a high-scoring

post.

There are a number of limitations for this study. One that I have mentioned

throughout the paper is that the measurement of interest as an effect of the AMA may

be picking up the effect of concurrent advertising. I attempted to address this issue by

shortening the time horizon and dropping any observations that coincide with a release

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of the product. I consider this a convincing way of addressing the problem, but some

may still challenge this assumption.

Furthermore, I use a small sample size relative to the entire population of AMAs.

Given the statistical significance of the findings, I do not consider this a major limitation.

Google Trends includes traffic from all sources, so the data on interest is not

restricted to reddit users. I do not see this as a large limitation, as the traffic from other

sources should remain relatively stable throughout the shortened time horizon. On a

similar note, AMAs often include direct links to websites with information on the

products being advertised. Any clicks of these links are not included in the data on

interest but are still valuable to advertisers. Future research should somehow

incorporate the traffic to any sites directly related to the products being advertised.

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Figure 2

Figure 2: Visual example of Google Trends data. In this particular situation, the dot represents the day of

the AMA.

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Person Product Search Term

Jeff Bridges the giver film

David Attenborough (dropped)

Peter Dinklage little feet film

Buzz Aldrin (dropped)

Bill Murray monuments men

Harrison Ford years of living dangerously

Tim Berners-Lee (dropped)

LeVar Burton reading rainbow

Keanu Reeves john wick

Bill Gates gates foundation

Julian Assange wikileaks

Sean Bean legends show

Arnold Schwarzenegger (dropped)

Weird Al Yankovic mandatory fun

Robert Downey Jr. the judge film

Guillermo del Toro the strain show

Mark Hamill make a wish foundation

John Cho selfie show

Chris Hadfield (dropped)

Pedro Pascal game of thrones

Warwick Davis (dropped)

Jerry Seinfeld comedians in cars getting coffee

Cary Elwes as you wish book

Idris Elba no good deed film

Denzel Washington (dropped)

Gordon Lightfoot (dropped)

Mike Tyson judgment day

Bill Nye europa

Josh Brolin sicario

Jerry Seinfeld 2 comedians in cars getting coffee

Jeff Goldblum grand budapest hotel

Pierce Brosnan the november man

Simon Pegg hector and the search for happiness

Bob Odenkirk (dropped)

Uzo Aduba (dropped)

Les Stroud (dropped)

Gillian Anderson (dropped)

Matt Damon monuments men

Ron Perlman easy street book

Dan Mintz the stranger album

Ray Liotta kill the messenger film

Ken Burns (dropped)

Appendix A: Product Search Terms

Associated with AMAs

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Jewel Staite (dropped)

Dwayne Johnson hercules

Bill Cosby (dropped)

Magnus Carlsen (dropped)

Ben Stiller (dropped)

Rob Lowe love life book

John Ratzenberger american made

Luc Besson lucy film

Alfonso Cuaron gravity film

Jason Bateman bad words film

Jeremy Renner kill the messenger film

Roger Daltrey (dropped)

Jamie Hyneman mythbusters

Will Ferrell (dropped)

George Clooney (dropped)

Nick Offerman parks and recreation show

H. Jon Benjamin archer

Clark Gregg trust me film

Neil Diamond melody road

Hannibal Buress (dropped)

Terry Gilliam the zero theorem

John C. Reilly (dropped)

Ellar Coltrane boyhood film

Lou Ferrigno (dropped)

Judy Greer (dropped)

Justin Long tusk film

Richard Ayoade the double film

Hans Zimmer (dropped)

Lucky Yates archer

Mark Gatiss (dropped)

James Cameron years of living dangerously

Gilbert Gottfried (dropped)

Bernie Sanders (dropped)

Frank Miller a dame to kill for

Mark Duplass the one I love

Norm MacDonald (dropped)

Mark Ruffalo (dropped)

Donald Faison (dropped)

Verne Troyer (dropped)

John Cusack adult world film

Karyn Parsons little feet film

Betty White hot in cleveland

Tilda Swinton snowpiercer

Appendix A (cont.)

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Sanjay Gupta (dropped)

Spike Jonze her film

Nathan Fielder nathan for you

Tony Hawk (dropped)

Amber Nash (dropped)

Arthur Chu (dropped)

Drew Carey the price is right

Karen Gillan selfie show

Steve Nash (dropped)

Glenn Howerton the wilderness of james

Appendix A (cont.)

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References

Choi, Hyunyoung, and Hal Varian. 2014. 'Predicting the Present With Google

Trends'. University Of California, Berkeley School Of Information.

Holiday, Ryan. 2013. 'How to Host a Successful Reddit AMA'. Mashable.

http://mashable.com/2013/03/25/reddit-ama-tips/.

Lewis, Randall A., and David H. Reiley. 2014. 'Online Ads and Offline Sales: Measuring

The Effect of Retail Advertising via a Controlled Experiment On

Yahoo!'. Quantitative Marketing And Economics 12 (3): 235-266.