Author: Austin

Lessons I Learned Running an E-Commerce Store

Posted on by Austin

For many years, my wife and I have enjoyed spending our weekends at estate sales, yard sales, auctions, and thrift stores. It’s fun treasure hunting for the rare and unusual, and antiques give you a physical connection to the past that I find worthwhile. I collect old books, vintage technology, art supplies, pencils, and whatever else I happen to find interesting. At some point, I started routinely culling my collections so they wouldn’t get out of hand. Then I began looking for things I could sell for a profit to fund other purchases. I sold smaller items in my eBay store, and larger items locally on Facebook Marketplace. At a certain point, I realized that my side hustle was generating enough revenue that I could quit my day job as a Web App Dev and become a full-time e-commerce business owner. So I did. This is what I learned from that experience:

Become an Industry Expert

I thought that I was in the business of flipping rare antiques for profit. What I came to realize is, I was really in the business of leveraging knowledge. In order to acquire valuable inventory, I needed to be able to identify value where most people saw trash. Moving items from a market where they are under-valued to a market where they are highly sought. This is difficult to do, and requires a great deal of knowledge.

I studied certain areas in great depth. I read books and blogs, I watched videos, I joined online groups, I pulled sales data from various sources… I made myself an expert on the sorts of things I could likely find in the places I most often looked. Not just the monetary value, but also the market trends for those categories.

For example, some items sell for a high rate, but don’t sell very quickly, so you have to account for how long your funds will be tied up in that piece of merchandise. Every dollar trapped in inventory is a dollar that you can’t spend on acquiring new inventory. Some items you might be able to buy for $50 and sell for $500, a healthy ROI, but the customers for that item are few and difficult to reach. Some items are so rare that it’s impossible to find data on previous sales, which creates an element of risk that must be accounted for. Data is king.

I’ll give you a real example. There was a certain model of Polaroid camera that I would occasionally come across, usually for about $5. Most people saw Polaroids as virtually worthless since they require a special film that hasn’t been produced in decades, making the camera essentially a paper weight. However, I knew that a new company had recently sprung up to cater to the vintage camera enthusiast market, and this company was now producing film for old Polaroid cameras. Suddenly, these “worthless” cameras were in high demand in certain circles. They would sell almost instantly for around $80, meaning every time I came across one for $5 I knew that I would make a quick return of 16 times my investment. I did this many times. Knowledge equals profit.

Find Passionate Niche Markets

My specialty was finding hard-to-find, high-value antiques, and selling those antiques to passionate collectors. I sold fountain pens, movie props, cameras, books, art, industrial decor, and anything else I knew a collector would highly prize. Truly obscure items don’t pop up on eBay as often as common items, and when they do, they are often not optimized in such a way that collectors can easily find the listing. This can make it difficult for collectors to find the items they are the most motivated to acquire, and with the economics of supply-and-demand, the more difficult it is to acquire an item the more a collector is willing to pay for it. Connecting these motivated collectors with their holy grail equals profit. Thankfully, groups of like-minded collectors tend to gather on social media platforms, which made my job easier. To reach them:

  • I joined niche collector groups on Facebook, Reddit, and certain forums.
  • I posted quality pictures of my inventory on Instagram, with hashtags that would attract the attention of collectors, and descriptions that indicated that the items were for sale.
  • I marketed my social media accounts to grow followers, who would eagerly watch to see if I posted the items they collected.
  • I kept lists of good customers so I could send them special offers.

When you sell common items, there is a larger market, but also a greater level of competition. Greater supply, less demand. On the other hand, passionate niche markets have less supply and greater demand, but you have to work harder to reach those customers. You have to go where the customers are.

Know when to Pivot

I enjoyed running my own company. I learned a great deal, and I earned enough money to live pretty comfortably. But I also came to the realization that what I was doing was not scalable. There is only one of me and only so many hours in a day. Unfortunately, that means there is a limit to how much I could conceivably earn (since the nature of the business wasn’t conducive to expansion), and I also knew that my personal income potential was higher than this business could provide. Besides this, I found myself ready to take on a new challenge. After running my company for a few years, I decided to pivot and channel the passion I had devolved for business and marketing into a new career. (However, I still sell as a side hustle, and likely always will.)

School Shooting Data Analysis

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I came across this interesting dataset on Kaggle on U.S. school shootings from 1990 to 2022. I decided to poke around in the set and see if I could find any trends. Since it was amassed from multiple sources, there were some duplicate entries that I removed. Then I filtered out instances at colleges, so I would be left with only K-12 data. Then I filtered out instances that did not result in any fatalities and visualized the results.

Fatalities remain fairly consistent over time: between 2 and 37, with a mean average of 13. These numbers thankfully are quite small in relation to the 49,400,000 U.S. students. Of course, each school shooting is horrifically tragic, but it is a statistically rare occurrence. News Media outlets focus on school shootings when they do happen, creating a false sense that they occur at a much higher rate (this is the principle of Cultivation Theory, that because the news focuses disproportionately on negative incidents, people are led to cultivate a disproportionate view of how often these negative events occur). To put the odds in perspective, I made a chart showing the likelihood of a U.S. student dying in a school shooting, compared to the odds of getting struck by lightning. As you can see, you are more than twice as likely to be struck by lightning.

While I was at it, I also broke the data down by state and city. It seems Texas and California have had the highest number of fatalities with 53 and 52, respectively. In Texas, cities near their southern border have been hit the hardest, with the remainder concentrated around the Dallas area. California also has pockets of increased incidents, near Hollywood, Belmont, and Sacramento. The city with the largest number of fatalities however is Newtown, Connecticut with 28. Newtown represents an outlier though, as this where the infamous Sandy Hook Elementary School shooting took place, and this single incident is responsible for all 28 fatalities.

Data Analysis of Tuscaloosa Tweets

Posted on by Austin

I wanted to play around with Sentiment Analysis of Tweets; specifically, I wanted to try the Python TextBlob library, which has a built-in function that performs text analysis to determine if a string has a positive or negative sentiment. After pondering a bit, I decided it would be fun to search for tweets that were created specifically within the city limits of Tuscaloosa, where I am currently attending school. I wrote a script that scrapes Twitter and returns tweets by geolocation, and then uses TextBlob on the results. 

# -*- coding: utf-8 -*-
"""
Created on Wed Jul  6 15:58:58 2022

@author: austin
"""

import snscrape.modules.twitter as sntwitter #Social Network Scraping Library
import pandas as pd #so I can make a dataframe of results
from textblob import TextBlob
import csv
import time

#Tuscaloosa = geocode:33.23726448661455,-87.58279011262114,20km
query = "geocode:33.23726448661455,-87.58279011262114,20km"
tweets = []
combinedtweets = []
limit = 10000000 #set a limit on how many results I want to pull

for tweet in sntwitter.TwitterSearchScraper(query).get_items():
    
    if len(tweets) == limit:
        break
    else:
        # set sentiment 
        text = tweet.content
        analysis = TextBlob(text)
        if analysis.sentiment.polarity >= 0:
            sentiment = 'positive'
        else: 
            sentiment = 'negative'
        tweets.append([tweet.date, tweet.user.username, tweet.content, sentiment])

df = pd.DataFrame(tweets, columns=['Date', 'User','Tweet', 'Sentiment'])
df.to_csv('twitter_scrape_results.csv') #save dataframe as csv

print("\014") #clear console
time.sleep(10) 
print("CSV Successfully Created")

The results were pretty interesting (I uploaded the dataset to Kaggle if anyone is interested). It seems sentiment stays roughly the same each year, hovering around 85% positive and 15% negative. I really would have thought negative sentiment would be much higher based on my personal observations of Twitter content: makes me wonder if Tuscaloosa is an unusually happy place, or if my Twitter observations are influenced by negative bias…

In any case, perhaps a more interesting bit of data is that the total amount of Tweets seems to decline quite a bit each year. This raises the question, why are Tuscaloosians tweeting less often? I put the results into this Tableau dashboard, which displays just how steady and steep a decline there has been.

Update:

I decided to test a hypothesis: perhaps the high level of positive tweet sentiment is due to the fact that this is a college town, and numerous tweets were posted by official University of Alabama departments? I used OpenRefine to filter out official UA accounts, which was easy enough to do since their usernames seem to either begin with “UA_” or end with “_UA”. Surprisingly though, that didn’t change the sentiment percentages at all. I now suspect that even if you factor in all official UA Twitter accounts, you would also have to factor for the fact that a large number of Tuscaloosians work for UA (45,000 employees). I know many of my professors post UA related content using their personal Twitter accounts, and by design this content will logically slant positive.

The Power of Building a Personal Library

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As the famous American writer Mark Twain is believed to have once stated: “The man who does not read good books is no better than the man who can’t.” Perhaps you are an avid reader, or like most you may only read books rarely if at all. There is a great benefit however in building a quality personal library and reading from it regularly. In fact, I am going to argue that this is critically vital if you wish to be successful in your career.  If you don’t start building your own personal library now, and adopt an aggressive pattern of daily reading, then you simply won’t be competitive with other prospective employees in the marketplace.

For example, I personally read 45 mins every morning, of a book that I believe will be beneficial to my career. I take Saturdays off, so that is 4.5 hours of reading a week, or 234 hours of reading per year. I average 2 books a month, or 24 books a year. Meaning that in four years, someone following this same regiment would have read 96 books in their field of study. That is a lot of self-study. And when you are interviewing for jobs, you will be up against interviewees like me who use their free time to get a leg-up on their future competition. As Amitesh Jasrotia says in a 2021 blog post for Bookjelly.com, “your library can be your armory of knowledge with which to aim at higher things in life.”

So allow me to share the many benefits of building your own personal library, how to find relevant books to buy, and what sort of books you may want to start with. 

A lack of reading is a critical problem in modern society. According to a 2021 article by Pew Research, titled “Who doesn’t read books in America?”, roughly a quarter of American adults (23%) say they haven’t read a book in the past year. Maybe this describes you. You may think to yourself that you can just google any information you need. Living in the modern digital age, it is easy to get into the mindset that any question can be answered with a few keystrokes, but this simply isn’t the case. For starters, there is a lot of information that can only be found in the pages of a book. Most books have never been digitized, and books tend to explore subjects at a much greater depth than an online article or YouTube video. Some books are rare and difficult to acquire, full of information difficult to find elsewhere. It may be that reading such a book gives you an advantage over people who don’t have access to that knowledge. That is one of the reasons that I like to search for and collect rare books that are relevant to my field of study. The extra effort it takes to hunt down and read a rare book might just pay dividends in the future, when you know things that people that you are in competition with don’t. As historian Matthew Muller stated in a 2020 blog post, “collecting actual books is a great way to gain access to knowledge and preserve it over time.”

There is also a psychological difference in absorbing information from a book rather than from a computer screen. The media theorist Marshall McLuhan wrote an entire book, “The Guttenberg Galaxy”, on the power of the printed page. It’s a rare book, difficult to find. I am lucky enough to own a copy. McLuhan states in this book that when we read text off a page of a book, that our brain processes that information differently than when we absorb content through electronic mediums. In other words, the method that we take in information has a degree of influence over how we understand it. So we can’t equate googling an answer to finding answers from a book, as they are quite different things.

So why build a library and not simply check books out at your local library? There certainly is a financial benefit to using your local library, however it isn’t a great substitute for building your own library. For one, it isn’t very efficient. Every time you are ready to pick up a new book to read, or anytime you need a quick reference, you would have to jump into your car and drive to your nearest library. With the price of gas being what it is, this is quickly becoming less economical than it may have originally seemed. You are also at the mercy of what the library has in stock, which means the books most beneficial to you might not be accessible to you. Furthermore, especially niche books or truly rare books aren’t often stocked by libraries. Building your own personal library will allow you to select the books most relevant to your own needs, and you will have instant access to them 24/7.

There are lots of ways to help offset the cost of books if that is a concern. Frequenting used bookstores and searching auction sites like eBay will often allow a frugal book buyer the opportunity to amass a large collection quite economically. But how do you figure out what books to buy? There is after all an almost limitless number of books, and a finite amount of time to read them, and certainly a finite budget to spend on them. Surely it would be wise to sort through them all and make a list of the most beneficial books? Well, one way that I like to do this is my searching appropriate subreddits and forums for recommended books. The social media platform Goodreads also contains user recommended lists for any number of book subjects. Sometimes with a bit of googling you can find blog posts or YouTube videos where content creators share books that were helpful to them. I also find that as I am reading, it is often the case that an author might suggest another book to read on the subject, so I keep a notepad next to my reading chair so I can jot these recommendations down to find later.

Now you may be wondering where to start? Well, lets take my own library as an example. Your library should be unique to you and your own goals, however there will likely be a good deal of commonality with other libraries as there are certain topics and genres that have universal benefit. Your library should also reflect a diverse range of subjects, so you can be a well-rounded reader. According to Justin Brown’s 2018 blog post for IdeaPod, titled “15 incredible benefits from reading every day”, being a well-rounded reader will lead to you being better equipped to handle many of life’s challenges. I have a number of books always at hand, and they are organized by category. One shelf is entirely fiction, including a number of the classics and many science fiction and fantasy. You may think that reading fiction won’t be any help to you professionally, but I beg to differ. Fiction opens your mind to places, cultures, and circumstances. It places you in the shoes of others, and lets you explore different points of view. This experience can be beneficial to us in a lot of ways.

The next bookcase mostly contains books that are directly relevant to my career. I am an Advertising Major, so the first row contains books on Communication Theory and Media Ecology and the second row contains the most recommended books on Advertising. Then, because I am interested the data analytics side of things, we have a row of books about Programming and Databases, and a row about Math and Statistics. The last two rows are Religion.

The third bookcase is where I keep books on subjects that I find personally interesting. Including Art books, Graphic Design, and Typography on the top shelf. The next row of books are all about Economics, and finally a row of books about Politics and American History.

As you can see, there is a great deal of variety in my library. The goal is to become well-rounded in your reading, while still being relevant for your personal career goals. I implore you to begin constructing your own library, one that is inline with your own goals and aspirations, and that will give you a well-rounded base of knowledge. And as you build this library, read from it, every day. This will give you a competitive advantage that can’t be acquired adequately in any other way.

Brown, J. (2019). 15 incredible benefits from reading every day. IdeaPod. https://ideapod.com/15-incredible-benefits-reading-read-every-day/

Muller, M. (2020, December 9). Matthew Muller New Orleans: The Benefits of Collecting Books. Matthew Muller New Orleans. https://matthewmullerneworleans.com/matthew-muller-new-orleans-the-benefits-of-collecting-books/

Jasrotia, A. (2021, July 5). Monday Musings of a Bibliophile | Why should you build a personal library at home? BookJelly. https://bookjelly.com/why-should-you-build-personal-library/

Twain, M. (n.d.). Quotation of uncertain origin. The man who does not read good books is no better than the man who can’t.

Perrin, A., Gelles-Watnick, R. (2021, September 21). Who doesn’t read books in America? Pew Research. https://www.pewresearch.org/fact-tank/2021/09/21/who-doesnt-read-books-in-america/

McLuhan, M. (1962). The Gutenberg Galaxy: The Making of Typographic Man. University of Toronto Press.

Data Analysis of the MechanicalKeyboards Subreddit

Posted on by Austin

Developers tend to take their keyboards seriously. I have been using classic buckling spring IBM Model M computer keyboards since I first began programming. These are great to type on, and I still love them (kind of feels like typing on a typewriter), but I decided recently that I should upgrade to a compact keyboard that uses modern mechanical switches. This would give me more space on my desk, and allow for some customization. There seems to be an endless sea of options to choose from, though; the first step in my consumer journey is to narrow my options down to a few top brands, so what is an aspiring data scientist to do? I thought a good way to cut through the clutter would be to scrape the r/MechanicalKeyboards subreddit to see what brands are the most talked about currently. So I wrote this Python script that uses Reddit’s API to scrape the subreddit.

import praw
from praw.models import MoreComments
import datetime
import pandas as pd

# Lets use PRAW (a Python wrapper for the Reddit API)
reddit = praw.Reddit(client_id='', client_secret='', user_agent='')

# Scraping the posts
posts = reddit.subreddit('MechanicalKeyboards').hot(limit=None) # Sorted by hottest
 
posts_dict = {"Title": [], "Post Text": [], "Date":[],
               "Score": [], "ID": [],
              "Total Comments": [], "Post URL": []
              }

comments_dict = {"Title": [], "Comment": [], "Date":[],
              "Score": [], "ID": [], "Post URL": []
              }

for post in posts:
    # Title of each post
    posts_dict["Title"].append(post.title)
     
    # Text inside a post
    posts_dict["Post Text"].append(post.selftext)
    
    # Date of each post
    dt = datetime.date.fromtimestamp(post.created_utc) # Convert UTC to DateTime
    posts_dict["Date"].append(dt)
     
    # The score of a post
    posts_dict["Score"].append(post.score)
    
    # Unique ID of each post
    posts_dict["ID"].append(post.id)
     
    # Total number of comments inside the post
    posts_dict["Total Comments"].append(post.num_comments)
     
    # URL of each post
    posts_dict["Post URL"].append(post.url)
    
    # Now we need to scrape the comments on the posts
    id = post.id
    submission = reddit.submission(id)
    submission.comments.replace_more(limit=0) # Use replace_more to remove all MoreComments
    
    # Use .list() method to also get the comments of the comments
    for comment in submission.comments.list(): 
        # Title of each post
        comments_dict["Title"].append(post.title)
        
        # The comment
        comments_dict["Comment"].append(comment.body)
        
        # Date of each comment
        dt = datetime.date.fromtimestamp(comment.created_utc) # Convert UTC to DateTime
        comments_dict["Date"].append(dt)
        
        # The score of a comment
        comments_dict["Score"].append(comment.score)
         
        # Unique ID of each post
        comments_dict["ID"].append(post.id)
         
        # URL of each post
        comments_dict["Post URL"].append(post.url)

# Saving the data in pandas dataframes
allPosts = pd.DataFrame(posts_dict)
allPosts

allComments = pd.DataFrame(comments_dict)
allComments

# Time to output everything to csv files
allPosts.to_csv("MechanicalKeyboards_Posts.csv", index=True)
allComments.to_csv("MechanicalKeyboards_Comments.csv", index=True)

Reddit limits API requests to 1000 posts, so the most current 1000 posts is my sample size. My code outputs two files: the last 1000 posts, and more importantly the comments on those 1000 posts, which ended up being 9042 rows of data. (I posted the files to Kaggle if anyone would like to play with them.) Then I imported my comments dataset into OpenRefine so I could run text filters to find brand names, and I recorded the number of mentions for each brand. Finally, using Tableau, I created a couple of Data Visualization charts to express my findings. Here are the most talked about keyboard brands on r/MechanicalKeyboards currently:

Update:

I decided to go with the Keychron keyboard that my research found to be the most discussed (and I also added Glorious Panda Switches and HK Gaming PBT Keycaps). Couldn’t be happier; it’s a pleasure to code on.

My New Linux Server

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I was going to use this cool cart I found as a Raspberry Pi station, but I found myself needing a decent Linux server for a project, so I decided to rethink my plans. Being on a budget, I repurposed a 2012 Mac Mini I bought off eBay by installing Ubuntu on it and fitting it into the cart. I was lucky to find a Quad-Core i7 Mac Mini that was already tricked-out with 16GB RAM, a 1 TB SSD, and an additional 1TB HDD for storage. This makes it a surprisingly swift little machine, despite its age. I had to find the right networking drivers to get the Wi-Fi working, but otherwise it was a pretty painless installation. I’m going to use this for running longer Python scripts, so I don’t have to use up my main computer’s resources.

Raspberry Pi Station

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I wanted a dedicated space for tinkering on Raspberry Pi, so I set this up. I bought a Medical Teleconference cart from a government surplus auction for $50, which I repurposed to be a great adjustable standing desk. Plus, it has lots of built-in cable management and a perfect cubby for holding the Pi. Then I lucked out and found an awesome monitor at the thrift store for $30. The rest of the gear I had lying around. Now I have a really convenient place where I can play with Pi, and as a bonus my home lab has a computer. I plan on using this setup to do some experimenting with Machine Learning soon, so stay tuned.

AI Generated Advertising Content

Posted on by Austin

Due to recent progress in the fields of Artificial Intelligence (AI) and Machine Learning, many of the creative tasks within advertising, such as writing ad copy or ad image selection, are increasingly being performed by machine rather than by humans. The rise of AI generated content stands to shake the advertising world, as some professional roles become obsolete. The ways that consumers and brands interact are also rapidly changing as a result. To understand this phenomenon, we must delve into the benefits and pitfalls of AI generated content.

Some advertisers dream of a time when they can enjoy a three-hour work week, utilizing a myriad of AI tools to streamline and automate their workflows to extreme lengths. While this particular scenario isn’t very likely to happen, it’s not hard to understand the desire: This would be quite the leap from the current day-to-day slog that many advertisers find themselves struggling through. In the digital era, marketing departments must churn out dizzying numbers of variations of digital ads for the various social media platforms currently popular, each with slightly different imagery and calls to action. Wouldn’t it be nice to automate this process, and let robots handle the boring bits? Well, that might seem like some manner of science-fiction futurism, but it is actually a possibility today.

AI can be used to completely generate both the advertising copy and the visual imagery for the ad, and when combined with customer profile data, AI can even customize the ad to be more persuasive to that particular viewer. These ads do not exist before the target consumer is ready to view the ad, then in an instant an ad is automatically generated just for that particular viewer. The AI takes into account the viewer’s interests, behaviors, and demographics. The result is a very tailored communication, which will likely be more effective than a traditional one-size-fits-all ad. It also has the benefit of saving the brand a fortune in advertising costs. All the man hours that would have traditionally been spent in crafting the ad, and then creating endless derivatives for every possible platform, was all accomplished without any man hours spent at all (well, besides the initial setup of the AI campaign that is).

Multiple AI technologies can be used in tandem for particularly creative results. Companies like DataGrid and Rosebud.ai have developed AI technology that allows advertisers to utilize completely artificial models that are almost indistinguishable from real, human models. These virtual models can be used as actors in commercials, or as fashion models for brands. You could even showcase fashion products on a generated model that looks identical to the viewer, letting the viewer know how those items would look on them specifically. The possibilities are almost endless. Albert.ai is another AI brand, one that autonomously plans and executes paid search and social media campaigns. Tech company OpenAI (co-founded by Elon Musk) launched the AI tool “GPT-3,” which can write copy so well that it’s hard to tell that the text wasn’t written by an actual human. Using tools like these, brands can save advertising costs, allowing smaller brands the ability to make advertisements that rival the quality of larger brands. They will also be able to experiment with more creative advertising, since the cost to experiment will be much lower than using traditional methods.

However, the technology isn’t all AI generated roses. For example, according to Google’s Search Advocate John Mueller, content automatically generated with AI writing tools is considered spam and against webmaster guidelines. It is possible platforms will begin banning AI generated content in the future, which would certainly dampen the technology’s potential. As of now though, no ban exists for this emerging technology, and platforms like Google are unable to detect if ads were AI generated or human created. Another concern is that AI generation tools might lead to a stall in the advertising job market, as many traditional roles are replaced with AI counterparts. However, it is also possible that freeing advertisers from the more tedious aspects of ad creation will have a positive effect, allowing them to spend more time and resources on more creative pursuits. This could lead to higher quality advertising for consumers, and more high-level positions for prospective advertisers. A more pressing fear is that AI generated content might lead to empty, uncreative, repetitive advertising. This could further crowd an already crowded market, and could erode brand trust with consumers, if the technology is used to generate low-quality content. 

Despite these potential pitfalls though, the future of AI generated content is going to be too lucrative to ignore. Advertisers that learn how to put these new tools to work for them will enjoy an advantage over brands who aren’t able to capitalize on the power of generated content. And as this technology matures, that will only increasingly be the case. Even as it stands now, if some parts of this paper were AI generated using the tools currently available, would you be able to tell?  

Machine Learning in Advertising

Posted on by Austin

Innovations in Machine Learning are having a radical effect on how consumers and brands interact. Machine Learning also greatly expands the toolkit companies have, both to gather analytics, and to use that data to craft a deeper understanding of their consumers. Better customer profiles lead to better sales and happier customers, but there are pitfalls to be wary of too.

Machine Learning, a subfield of artificial intelligence, consists of algorithms that can iterate through large sets of data and then make smart conclusions and helpful correlations that would be difficult for a real person to detect. They can also learn over time to become better at their given task and improve their results with more exposure to data. These algorithms can digitally replicate the mind of a consumer, or the mind of an advertising researcher, or they can be utilized to communicate with consumers on behalf of brands. 

Gone are the days when armies of support staff wearing phone headsets are the only option companies have to communicate with inquiring customers. Now chatbots are the norm for many companies; Machine Learning allows chatbots to converse with human beings in a natural, human-like way that doesn’t frustrate users. The algorithms used by chatbots are able to pull bits and pieces from previous interactions and use them to infer answers to future questions, so they only get more effective at communicating over time. Chatbots are attractive to brands because they greatly reduce the overall costs they spend on customer service. Chatbots provide fast answers to the day-to-day queries of customers, resolving their queries immediately and simplifying the user decision process. Suppose a customer comes to your site and has trouble locating a certain product. In such a scenario, a chatbot on your site will solve the customer’s dilemma quickly. And unlike human agents, chatbots provide round the clock services.

Machine Learning can be used for a lot more than just chatbots though. Predictive targeting is a marketing technique that uses Machine Learning to predict customer decisions based on behavior patterns. Algorithms iterate through large data sets to predict the probability that a customer will take a certain action. They can help answer questions like: will a customer likely purchase this item or that? Will they be interested in engaging with a certain campaign, or would they react negatively to an advertisement? Machine Learning tools can help advertisers analyze the performance of ads, or help optimize marketing content, or they can analyze images posted on social media.

Social Media platforms can be an incredible source of data for advertisers. People tend to use Social Media to talk about their interests, comment on the places they have visited, and share their personal experiences with products and services. For example, a system that uses Machine Learning might conclude, after analyzing some social data, that young women who like a particular tv show and who are also interested in a certain celebrity are statistically more likely to purchase tickets for a certain vacation package. That could be very handy information for a brand. Suppose instead that you wanted to search through photos posted on Instagram to find images that contain your branded products: Machine Learning based tools could do this, and then analyze the content of those images to form a complex customer profile by observing characteristics about the people and environment in them. Using these customer profiles, a company could then launch new advertising campaigns that are more persuasive to this finely targeted audience.

There are however some potential downsides of using Machine Learning in advertising. For instance, Machine Learning only works with very large data sets, which will have to be harvested before any analyzing can take place. Machine Learning also struggles to reliably analyze sentiment because, despite advancements, algorithms simply aren’t capable of human intuition. Some tasks that are simple for humans are very difficult for a program to replicate. Sometimes AI is not a human enough replacement for an actual person. There are also companies who use machine learning in less-than-ethical ways. For instance, by sending out bots that can post deceptive content on social media platforms to falsely promote their brand, or falsely besmirch a competitor.

Despite these challenges though, there are many tasks that better suited for a Machine Learning based approach, and there is great potential in this emerging field. As the technology advances, there will be lots of new creative ways for advertisers to capitalize on the unique benefits of Machine Learning. Companies will need to adapt to these new methods if they are to stay competitive in the marketplace.  

Virtual Reality Advertising

Posted on by Austin

Virtual Reality (VR), often the plot device of futuristic science-fiction stories, has actually been around for some time now. Both YouTube and Facebook have been supporting the adoption of 360-degree videos since 2015, Nintendo’s ill-fated “Virtual Boy” was released way back in 1995, and Oculus (a company Facebook would later purchase for $2 billion) debuted their popular Rift VR headset on Kickstarter in 2012. Even so, VR should be considered an emerging market, as only now has the technology finally caught up to the demand. And as consumers are introduced to this new medium, there will be a virtual gold rush as advertisers seek out new ways of engaging with users.

VR content can be viewed in multiple ways. Smartphone users can view 360-degree content (content that has been recorded on omnidirectional cameras) by moving their phones around in real space, allowing them to view videos from different angles. The effect can also be accomplished on a computer by moving a mouse around. While using “low-cost” options like smartphones and computers can often be a fun way to interact with content (and has helped spark interest in VR), these options only offer users a limited VR experience. The driving appeal of VR is in its ability to create fully immersive experiences that don’t need to be grounded in reality, but in order to experience the full extent of what VR has to offer, users need to use a Head-mounted Display (HMD). These headsets combine many advanced technologies, like gyroscopes, motion sensors, spatial audio headphones, HD screens, and fast computer processors. The gyroscopes and sensors allow very sensitive tracking of the head, body, and hands. This allows the crisp HD screens to show a simulated world that responds to the user’s physical movements, just like the real world. Headphones with spatial audio capability allow directional sounds that envelope users. These components are important for creating immersive experiences that feel “real”; however, they have historically also raised the price of HMDs out of reach for the average consumer. Even though VR enjoyed lots of early hype, by 2017 reports showed consumer interest in VR was declining as a result of the high cost of the technology. Since then though, companies have been able to lower the cost of adoption drastically, while at the same time improving VR hardware and software significantly. This has caused a renewed spike in consumer interest as the hardware has now become affordable and accessible. According to Grand View Research, the global VR market will grow to 62.1 billion dollars by 2027. Currently, VR is most commonly used for entertainment applications such as video games, 3D movies, and social worlds. According to a study from Ericsson Consumer Lab however, shopping was the top reason users were interested in VR. Interestingly, 64% of respondents stated that their interest was in “seeing items in real size and form when shopping online”. That should be a call-to-action for advertisers.

We live in a distracted world. For example, an in-home eye-tracking study Facebook IQ commissioned revealed that 94 percent of participants kept a smartphone on hand while watching TV. This can make it difficult for advertisers to reach consumers, as their content often gets tuned out. However, VR creates a new world around users. A world free of distractions, with a tailored focus of attention and obligatory user interaction. More than that though, VR promises to re-define the advertiser-consumer relationship. In essence, VR could mean an end to the enraging ads of the past. Lithium Technologies recently polled 2000 customers aged 16-39, and the results showed that 74% of the interviewees found the advertisements in their social media feeds intrusive and irritating. Intrusive advertising alienates customers, however in the modern landscape it can be difficult to attract attention without becoming intrusive.

Unity believes the answer to intrusive ads is a “virtual room” users would be transported to for brief periods of time. This would allow users to experience advertising without it becoming a distraction to games or movies. While inside this room, users would be immersed in a fully interactive branded environment where they could engage with products or services. These ads could be targeted, allowing users the chance to interact with the ads they would likely be the most interested in. And creating in-person experiences will drive empathy, allowing advertisements to have a deeper impact. This won’t be the only method for advertising with VR, however. Product placement will allow advertisers to seamlessly embed ads within VR gameplay. For example, game characters could wear branded apparel, and in-game posters or billboards could portray real-world brands. VR could potentially change the hospitality industry as well by allowing hospitality brands to recreate aspects of their travel experiences in VR. Imagine being on the fence about what your next vacation should be, and then spending a few minutes enjoying a virtual luxury cruise: enjoying the sun, waves, entertainment, and service. This might be just the push a consumer needed to go ahead and book the cruise, and how convenient that they can order the tickets without even taking their headsets off!

VR Advertising is not without its limitations, however. Laws tend to lag behind technological advancement, but eventually they do catch up. We know already that the ASA treats video content with greater sensitivity than still images when it comes to rules on “harm and offense” or “social responsibility”. We should expect that VR ads will be scrutinized more severely do to their more immersive design. Precautions will need to be taken by advertisers to make sure ads are VR appropriate, as well as age appropriate. Another challenge is that the act of using HMDs is often culturally viewed as “dorky” due to how large and clunky the headsets tend to be, and how clumsy users can appear as they play virtual games. In order to achieve widespread VR adoption, advertisers will need to market the technology with positive traits like intellect, youth, and success. The focus will need to be on the content and experience of VR, and not how users look while they are wearing the tech. Also, as with any connected technology, it is only a matter of time before cybersecurity issues are raised. Up until now, VR hasn’t been widespread enough to attract the attention of hackers, but as adoption rates grow that will likely change. New technologies will require new best practices to be formed around cybersecurity, and users will have to choose how much risk they are willing to accept.

Even with these challenges, the benefits of creating immersive advertising for attentive users is too good an opportunity for advertisers to miss out on. Brands that are able to capitalize on this new medium will set themselves apart from their competitors. They will also have the opportunity to re-imagine the relationship between advertisers and consumers moving forward. 

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