• “Cow-free burgers simulate meat products like chicken and beef sausages and pork.” — repetitive use of “and”

Kaity Vermillion MOT5053 - Target Potential Assignment

A reminder of OrderEnvy OrderEnvy is the crowdsourced menu recommendation app for the modern restaurant-goer. The app includes full menus of restaurants with pictures, reviews, and health information on each item. It gives the user the ability to search and filter the menu by a number of options including price and dietary restriction, and users are encouraged to post their own reviews and pictures as well.

Scenario Building

Winners & Losers

The zero-sum game in OrderEnvy’s industry is the competition for customer reviews. Since it is unlikely that a customer would want to post a review of a meal in two different places, much less four or five when you consider the abundance of competitors like Yelp, TripAdvisor, Google Reviews, and Facebook reviews, it is reasonably the case that if one review app earns a consumer’s post, the competitors do not get that post. Since OrderEnvy and these competitors operate based off of user reviews, obtaining these posts is vital. As OrderEnvy gains traction and popularity, perhaps in as quick as under a year if the early adopters are generous with their

word of mouth recommendations, there is a fair chance that OrderEnvy could emerge as the winner for food reviews in this zero-sum game. Undoubtedly, OrderEnvy is the easiest to use and most informative app when it comes to choosing an item on a restaurant menu. Thus, I anticipate that even if a consumer uses a competitor to decide upon a restaurant to go to, they will prefer to use OrderEnvy to make a menu choice. Then, when the consumer is through with their meal, OrderEnvy will be the most recent app on their mind, perhaps even still opened up as they check their phone at the end of their dining experience. Thus, OrderEnvy is more likely to grab the review in this scenario, which should occur often, since no other review app is relevant in the time between ordering a meal and finishing it. Additionally, for another point in OrderEnvy’s favor, dining out is usually a group rather than an individual experience: Gather reports that “the average party-size for a full-service restaurant is 3.7 guests.” Since it only takes one person to choose a restaurant for the group using a competitor’s app, but every person in the party needs to decide on something to eat, I foresee a scenario in which all diners in a party are excited to use OrderEnvy to find and then review their meal, but only one per group may feel compelled to review on the competitor’s app where they found the restaurant. While the ever-present societal desires of ​relevance​ and​ convenience​ do seem to be in OrderEnvy’s favor in this scenario, the social sense of​ righteousness​ could result in OrderEnvy being the loser rather than the winner in this game. By a sense of righteousness, I mean that people feel the need to share their story when their experience has been very good or very bad. Imagine a consumer has been looking forward to going to a certain restaurant, only to find that the waitstaff was very rude and rushed them out of their table. The consumer’s sense of injustice is likely to compel them to leave a review about their whole restaurant experience, not just their food - resulting in the use of a competitor’s app, since OrderEnvy specializes in food reviews only. Since all of the social behaviors explored in this scenario, including a preference for convenience and an entitlement to fairness, have existed for a long time and will likely still be around in one or five or ten years, either of these scenarios could be very likely to occur, and very soon. Overall, I think that the scenario in which OrderEnvy is the winner is more likely, because the scenario in which the competitor is the winner relies on the restaurant experience being notably above or below average, when in fact the average restaurant experience is exactly that - average.

Revolution

In considering ​all​ potential scenarios, we must include the unlikely and the unpredictable. For our business, the “worse” and “different” future would definitely be one where consumers move away from mobile apps like OrderEnvy. With 77% of Americans today owning smartphones and 70% using social media, it is clear why a sharp move away from these trends would classify as a revolution. Nevertheless, with examples like Cambridge Analytica’s access to Facebook’s private data in the news recently, concerns about online privacy have been on the rise. A survey conducted in 2016 by Pew Research Center found that about half of Americans “do not trust the federal government or social media sites to protect their data.” Though these concerns do not seem to have majorly affected social media use yet, imagine a situation in which there is a sudden major privacy breach that users do not just hear about through the news, but feel truly affected by. Perhaps they see their personal, private photos and memories being used generously in public marketing, or information about their frequent locations and close family members are published in an online encyclopedia. The abrupt, overwhelming feeling of betrayal and loss of safety drives the majority of social media users to delete their social apps and refuse to download any new ones. In this revolution scenario, OrderEnvy is affected dramatically, being a very social app in itself. OrderEnvy’s entire functionality is reliant upon users identifying themselves with certain “Foodie Badges” and posting reviews and pictures of their meals. Geolocation is used to verify a user’s presence at a restaurant before posting in the app, to ensure trustworthy reviews. These features, though meant to be beneficial, actually deter potential users in this scenario of a sudden societal attitude change. Users are afraid to give any personal information or participate as an identifiable online presence - so OrderEnvy must react accordingly. OrderEnvy can prepare for this situation, which could occur in 1 year or in 10, by ensuring anonymous reviews

can be left, without having to create a login. Although this is counteractive to the socially interactive feel that OrderEnvy is striving toward prior to this potential revolution, the developers can at least ensure that this easy functionality is built, without actually employing it until necessary. Additionally, anticipating this scenario can allow the opportunity for OrderEnvy to explore alternate verification methods to ensure trustworthy reviews other than geolocation. For instance, perhaps image recognition technology could be used to verify that the picture being posted does indeed look like food, and specifically looks like the type of food that is being reviewed. Although this revolution scenario is daunting and rather unlikely given current trends in social media usage, small-effort preparations can lead to a better likelihood of survival in case of the worst.

My Generation

The restaurant business’s growth rate has slowed in the past three years - and while historically slow growth in this industry has been attributed to economic factors, experts believe that the economy is not to blame this time. Indeed, this is not a “Cycles” scenario but rather a “My Generation” scenario: as baby boomers reach an age where they eat out less, millenials are not filling their shoes. In fact, out of all demographics, millennials’ growth pace of restaurant spending has dropped most dramatically in the past few years. As baby boomers exit the market and millennials start families and share their habits with their children, this could result in a scenario in 5-10 years where restaurants are thought of in a much stricter “special occasion” mindset. Thus, many restaurants lose business and are forced to close, resulting in less restaurants altogether - and the less restaurants there are, the less likely consumers are to drive by one and decide to sit down to eat there on a whim, resulting in even less restaurant spending. Given that this anti-restaurant trend is still on the rise, is not due the economy, and is most observed in millenials, who are not going anywhere any time soon, this scenario seems quite likely to play out.

While review-system competitors like Yelp and Google Reviews include multiple industries, OrderEnvy is directly tied to the restaurant business. Less spending in restaurants means less need for our app, and a fall in sales for our business. If the current trends continue, this could mean the obsoletion of OrderEnvy in as little as five years. Therefore, it is critical that OrderEnvy adapt its functionality to meet the changing market preference. In this future scenario, what takes the place of restaurant eating? “Fast casual dining” like Chipotle and Panera Bread is growing 5 times faster than the restaurant industry overall. In a Reuters/Ipsos poll of 4,200 Americans, 55% of respondents said “they eat at restaurants because it is convenient,” giving more weight to the success of fast casual dining. Accordingly, let us adjust the future scenario to account for restaurant replacement: imagine an average American is driving home from the mall on a Saturday evening, and feeling hungry. Instead of deciding to devote a chunk of their evening to a full restaurant experience, they head across the street to the latest fast dining chain to grab dinner to go. Or perhaps, they get on their favorite food delivery app and order meals for the entire family. OrderEnvy has the potential to be there in both situations. When that consumer walks into Chipotle, OrderEnvy can pop up with a notification: “We see you’re at Chipotle. Would you like to see the top-rated menu items?” When that consumer opens their food delivery app, OrderEnvy can be present as an integration, allowing consumers to see real user pictures of menu items. Though without adaptation this “My Generation” scenario could be devastating, if OrderEnvy focuses on our core value proposition and not just our original functionality, we can find great opportunity to grow with the changing market.

Evolution

The evolution of technology, and in particular, the place of smartphones in the technology system, has a direct effect upon OrderEnvy, because OrderEnvy, is, of course, an app, and apps only exist on smartphones. Just as telegraphs and corded phones have come and gone, the smartphone is not going to play such an important role in our lives forever, and though it

may be a scenario that is at least a decade or so away, all businesses that produce apps need to consider a future without smartphones. In this future, augmented reality wearables dismiss the need for handheld devices: all the information a user needs can be right in front of their eyes in a moment. Smartphones are outdated, slow, and inconvenient, and augmented reality has replaced the need for essentially everything that used a screen, including television and personal computers. If users want the functionality of OrderEnvy, they will need it delivered through their augmented reality device, not an old smartphone. The good news for OrderEnvy is that even in this evolved technological future scenario, the general need for OrderEnvy’s business will likely still be in place: even with augmented reality, consumers will still want to eat good food with friends, and they will still need to decide what to eat. Thus, as long as OrderEnvy evolves along with technology, business can remain open in the form of whatever an augmented reality “app” will be called. Evidence of this evolution already exists today, with smart speakers like the Echo and the Google Home integrating with “skills” that are very similar to apps on a phone. Accordingly, OrderEnvy has the opportunity to begin this evolutionary adaptation now, by integrating with smart speakers to allow users to ask, for instance, about the top-rated dishes at certain restaurants. Though the complete death of the smartphone is likely still 10+ years off, the likelihood of this future scenario is still very good, considering the rapid advancements in technology and the historical trends of adopting new technologies. All smartphone-related businesses, including OrderEnvy, should review this scenario often.

Building Forecasts with Bass Model

Choosing p and q Finding historical base model coefficient data for any sort of mobile app comparison was tricky, as much of the “p” and “q” examples available were for outdated products, such as the color TV. Nevertheless, I did find a few digital-age studies on innovation and imitation coefficients which together, I think can provide an accurate estimation for OrderEnvy’s bass model parameters. In a 2017 study from MATEC Web of Conferences titled “An innovation diffusion model for new mobile technologies acceptance,” the authors estimated the values of p and q for men and women aged 22 - 30 based on answers to a survey that asked about adoption behaviors for “mobile phone products”. Though the details of “mobile phone products” is not specified, based on language used in the study, we can assume they are alluding to mobile phones themselves, and not apps or accessories that could be considered products associated with mobile phones. Though mobile phones may not have quite the same internal and external influences as apps, there should be a relation, since users who adopt mobile phones almost always go on to adopt apps, as long as it is a smartphone that they are adopting (which is likely, given the recent year of the study and the demographic of the survey respondents). Since women and men are being

equally targeted in OrderEnvy’s target market, they receive an equal weight. The age range given by this study, though restrictive, does fit well into the prime social-media and restaurant-goers demographic that OrderEnvy is targeting. The second study I used in choosing a “p” and “q” for OrderEnvy is a 2006 study from Bled eConference entitled “Adoption and Diffusion of Digital Information Goods: An Empirical Analysis of the German Paid Content Market.” This study calculated p and q values for 17 suppliers of digital paid content - and though this study is from a few years before the first app launched, apps are essentially just digital paid content on your phone. The authors broke down the suppliers between those who have a non-digital counterpart to their content, and those who don’t. The “p” and “q” values I use come from their look at the companies without a non-digital counterpart, since most mobile app businesses do not duplicate their content outside of a digital space. I ranked the relevancy of these coefficients slightly higher than the first two, because I believe digital content is a bit closer to the idea of a mobile app than the phone itself. Finally, I used the average values of the coefficients to ensure that there is a solid grounding to the calculations. Using each of these “p” and “q” values, and assigning them a relevancy weight, I calculated a weighted average value for “p” and “q”.

Product p q Relevancy weight

Women 22 - 30 for mobile phone products

0.09 0.50 .2

Men 22 - 30 for mobile phone products

0.15 0.37 .2

Paid content (product only available online)

0.0783 0.1870 .3

Average value 0.036 0.395 .3

Weighted Average (sum of [each coefficient value *

each relevancy weight])

0.082 0.349 -

Though I am happy with this weighted average of ​p = 0.082​​ and​ q = 0.349​​, I am curious about the high p and q values for the mobile phone product and the lower p and q values for the paid content. I will use these in forecasting as well to see how they might affect sales.

Values to be Forecasted p q

Weighted Average 0.082 0.349

High (based on phone product example)

(0.09 + 0.15)/2 = 0.12 (0.5 + 0.37)/2 = 0.435

Low (based on paid content example)

0.0783 0.1870

Determining the Potential Market Size To determine the potential market size for OrderEnvy, I first took a look at the current market size for one of its direct competitors. Yelp, in their quarterly shareholder letter, does not disclose their number of app downloads, but does provide data on the number of unique devices that access their app per quarter. In 2017, the average was 28,205,000 unique devices per quarter, which I take to mean Yelp has approximately 28 million unique active app users, on average. While this number is definitely a good start, Yelp does have an advantages over OrderEnvy’s potential market size in that Yelp provides a place to review not just food, but hotels, attractions, and experiences. Since OrderEnvy’s focus is more specific, our target market will also be more refined, and probably a smaller size. Those who use Yelp primarily to review hotels may not be interested in OrderEnvy’s functionality. To account for this gap, let’s look at the popular app OpenTable. Though it is not a review system, it is an app solely for restaurant-goers who are looking to make reservations, so it may align more to OrderEnvy’s food-based target market. Crunchbase reports OpenTable’s app downloads in the last 30 days as 254,794. If we assume the last 30 days have been average, we can say that in an average year, OpenTable gets 12 times that many downloads, equaling 3,057,528 downloads per year. OpenTable has been an app since 2008, but it’s likely they were not getting the same amount of downloads in their early years as they are getting today. Since I don’t have access to their earlier adoption data, let’s take a guess that over 10 years, OpenTable has gotten about 70% of their 2018 downloads per year. 3 million * 10 * 70% = about 21 million downloads, a market size that is appropriately smaller than Yelp’s. With this research done, I am happy comparing OrderEnvy’s market potential with OpenTable’s market, since both are mobile apps for restaurant-goers. I will reason N = 21 million for my bass model.

“Weighted Average” Bass Model p = 0.082 q = 0.349 N = 21,000,000 Year Sales Cum. Sales

2018 0 0

2019 1,722,000 1,722,000

2020 2,132,494 3,854,494

2021 2,504,239 6,358,732

2022 2,747,816 9,106,548

2023 2,775,244 11,881,792

2024 2,548,212 14,430,003

2025 2,114,310 16,544,313

2026 1,590,461 18,134,774

2027 1,098,478 19,233,252

“High” Bass Model p = 0.12 q = 0.435 N = 21,000,000 Year Sales Cum. Sales

2018 0 0

2019 2,520,000 2,520,000

2020 3,182,256 5,702,256

2021 3,642,671 9,344,927

2022 3,654,722 12,999,649

2023 3,114,364 16,114,013

2024 2,217,213 18,331,226

2025 1,333,635 19,664,861

2026 704,077 20,368,938

2027 341,990 20,710,928

“Low” Bass Model p = 0.0783 q = 0.1870 N = 21,000,000 Year Sales Cum. Sales

2018 0 0

2019 1,644,300 1,644,300

2020 1,798,959 3,443,259

2021 1,913,007 5,356,267

2022 1,971,052 7,327,319

2023 1,962,686 9,290,005

2024 1,885,605 11,175,610

2025 1,746,935 12,922,546

2026 1,561,955 14,484,501

2027 1,350,539 15,835,040

While the “high” forecast reaches the highest sales point for a single year - 3,654,722 in the year 2022 - it is also the forecast that has the biggest crash, dropping to just 341,990 sales in the year 2027. The “low” forecast is more balanced, peaking at just 1,971,052 but only trailing off to 1,350,539 by the year 2022. As a direct comparison, the “high” forecast’s sales drop 91% in 5 years, while the “low” forecast’s sales drop only 31% in 5 years. The total cumulative sales by 2027 for the “high” forecast does surpass the “low” forecast’s total cumulative sales by about 5 million, but this may not be desired if it comes with such a drastic drop in the span of a few years. The “weighted average” forecast does seem to be a good balance between the two, having total cumulative sales of just 1 million below the “high” forecast, but not having quite as drastic of a sales per year drop - only 60% over 5 years rather than 90%. Since the “weighted average” forecast is likely to be most accurate based on my research, I think the 10-year forecast for OrderEnvy looks generally good. I am happy to see a strong sales start for my product in the first couple of years, and to not see any immediate crashes.

References Adoption and Diffusion of Digital Information Goods: An Empirical Analysis of the German Paid Content Market http://iss.uni-saarland.de/workspace/documents/adoption-and-diffusion-of-digital-information-go ods-an-empirical-analysis-of-the-german-paid-content-market.pdf Americans Are Eating Out a Lot Less and It's Hurting Restaurants http://fortune.com/2017/02/22/restaurants-eating-out-americans/ Americans’ complicated feelings about social media in an era of privacy concerns http://www.pewresearch.org/fact-tank/2018/03/27/americans-complicated-feelings-about-social- media-in-an-era-of-privacy-concerns/ An innovation diffusion model for new mobile technologies acceptance https://www.matec-conferences.org/articles/matecconf/pdf/2017/51/matecconf_mtem2017_0700 1.pdf From boomers to millennials, people aren't eating out as much https://www.nbcnews.com/business/consumer/boomers-millennials-people-aren-t-eating-out-mu ch-n920646 Millennials lose taste for dining out, get blamed for puzzling restaurant trend https://www.cnbc.com/2017/11/14/millennials-lose-taste-for-dining-out.html Mobile Fact Sheet http://www.pewinternet.org/fact-sheet/mobile/ OpenTable https://www.crunchbase.com/apptopia_app/d4ebc359-f194-4950-9846-75dbb7811ce5 Social Media Fact Sheet http://www.pewinternet.org/fact-sheet/social-media/ The smartphone is eventually going to die, and then things are going to get really crazy https://www.businessinsider.com/death-of-the-smartphone-and-what-comes-after-2017-3 Why Events are a Huge Opportunity for your Restaurant https://www.gatherhere.com/why-events-are-a-huge-opportunity-for-your-restaurant/ Yelp Q2 2018 Shareholder Letter http://www.yelp-ir.com/static-files/73d9c17f-0936-4ab3-ad98-3a0ab329a5b6