Food Compass, final report for Opportunity Analysis Project. Venture Lab / Technology Entrepreneur Class from Stanford.

1. Final Report for OAP
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2. Elevator Pitch
The Food Compass allows users to find restaurants or
specific menu items based on personal food preferences (i.e.
low-sugar options) and share those food preferences (via
their profile) with meal planners and reservation makers.
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3. USE CASES
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4. Use Case #1
Julie Smith is always looking for healthy foods. She registers a profile on The
Food Compass, indicating her food preferences, allergies, and dietetic needs in
order to find nearby restaurants that match her profile. She agrees to receive
(free) text alerts asking for her feedback after dining (in exchange for a 10%
off coupon on next visit) where she’ll rate her overall food experience. If she
signed up for the premium “Burn It” Index, she’ll also have the option of
entering meal details (ie. lasagna, chocolate cake) in exchange for preferred
activity recommendations.
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5. Use Case #2
John Smith, an overweight sales manager in a big corporation, registers a
profile on The Food Compass, indicating his food allergies and preferences
(dietary needs). He attends a vendor luncheon where the lunch organizer
requested access to his food profile to learn his preferences (ie. allergic to
shellfish); he accepted her share request. He also recently shared his food
profile with the airline and travel agent (in-flight meal preference) as well as
his hotel concierge so recommended restaurants will meet his dietary needs
(low sugar) while traveling. Finally, his personal trainer was granted access to
his food profile in order to analyze his eating and/or dining habits.
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6. FEATURES /
HYPOTHESES
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7. Feature Description #1
Configure your profile based on food preferences, allergies and
dietetic needs and enjoy the search results which are
automatically tailored to your personal, pre-set preferences. For
example, the Food Compass filters out high-calorie, high-sugar
entrées if you're watching your diet or leaves out expensive meals
when watching your wallet.
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8. Hypothesis #1
User want a service that helps them find foods nearby based
on personal preferences (ie. food allergies, price, healthy
options, ratings, deals, etc.).
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9. Results #1
Although 57.8% (11/19) respondents indicated they would find searching for foods
based on dietary restrictions to be useful, dietary restrictions and concerns are not a
significant primary factor. Other important considerations are taste, price, location,
etc. The survey results indicate that (when combined with social sharing options)
respondents are more likely to submit their dietary preferences in exchange for faster
response times, restaurant incentives or special accommodations. Therefore, dietary
restrictions form a valid secondary factor for biasing search results (based on custom
user profiles, but not necessarily as a primary search criterion).
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10. Feature Description #2
Once you have created a Food Compass profile unique to your
preferences, easily share it with meal planners (party hosts /
caterers) and reservation makers (concierges, travel agents,
office assistants, airlines, event planners, healthcare aides,
personal trainers, friends and family) so they can easily plan
meals that match your profile.
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11. Hypothesis #2
Users will want to fill out / share a personal food profile
with meal planners and reservation makers (concierges,
travel agents, office assistants, event planners, healthcare
aides, caterers, friends and family) so they can easily plan
meals that consider their food preferences, allergies or
dietetic needs.
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12. Experiment #2
We posed this hypothesis to 31 respondents via
one (1) online survey and several face-to-face
interviews.
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13. Results #2
Although the survey questions centered on using the Food Compass “while traveling,”
the hypothesis has been validated via face-to-face interviews as well as survey results.
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14. Feature Description #3
Users will be incented to rate and recommend meals and
restaurants in exchange for coupons / deals and vice versa.
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15. Hypothesis #3
Deals and coupons are an essential part of the Food
Compass solution.
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16. Experiment #3
We posed this hypothesis to multiple respondents via
two (2) online surveys and via a few face-to-face
interviews.
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17. Results #3
As of now following points came out after the survey
1. Offering coupons is good for attracting users; given deals must be good (obviously)
and UI must be neat n clean.
2. Coupons will also help restaurants with lower ratings to improve their quality or to
survive.
3. Users need an incentive to interact with the Food Compass while dining.
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18. Hypothesis #4
Restaurants require customer profile information and
statistical data to participate/share menu data.
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19. Experiment #4
We posed this hypothesis to 19 respondents
via one online survey.
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20. Results #4
The majority of respondents (15/19) are willing to share their profile data with
restaurants / businesses, based on certain limitations or pre-conditions (ie. speeding up
orders, to receive incentives, to have their dietary needs met, etc.)
The survey results validate the hypothesis that customer profile data will be available
as an incentive for restaurants to participate and share menu data.
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21. Feature Description #5
After consumption, users can burn off excess calories with our
Burn It index. When users activate this Burn It premium feature,
they’ll receive a text alert offering user-preferred activities to help
you burn the calories consumed. Example alert: It’s time to burn
it. You consumed chocolate cake for dessert two hours ago! Walk
two miles, do the laundry and dance with your partner for 30
minutes.
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22. Hypothesis #5
Users are interested in having an activity ("Burn It Off“
feature) recommendation integrated into the Food Compass.
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23. Experiment #5
We posed this hypothesis to several respondents
via one online survey and several face-to-face
interviews.
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24. Results #5
Respondents were more interested in calorie spend than calorie intake.
Because activity tracking is a related, but not vital, part of the Food Compass, we feel
the activity feature should be a premium one. We plan to offer users access to the
"Burn It" Index where they can find fitness activities that will compensate for the
calories expended.
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25. Market Analysis
Total Addressable Market (TAM):
The typical American aged 8+ consumes an avg. of 4.2 commercially prepared meals per
week, according to Meal Consumption Behavior — 2000 [5]. That figure translates to more
than 1 billion commercially prepared meals consumed per week and 53.5 billion per year
(Year 2000 numbers)
The typical person consumes an average of 2.1 commercially prepared lunches per week,
resulting in total annual consumption of 26.7 billion commercially prepared lunches
The US has an adult population (age 15-65) of 206.8 million. 69.5 million people in the
U.S. owned smart phones during the 3 months ending in February 2011 .
Projecting from 2000-2001 numbers, estimated TAM for the Food Compass app is 207
million users, with estimated total usage of 1.1 billion searches per week (combined
smartphone and web application usage).
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26. Market Analysis
Market Statistics [1]:
Number of fast food restaurants: 72,221
Number of bars/clubs/drinking establishments: 72,241
Number of full-service restaurant businesses: 214,147
Full-service Restaurants Revenue (2010): $178.1 billion
Full-service Restaurants Profit (2010): $10.7 billion
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27. Market Analysis
Barrier to entry for a technology change is low.
Households (HH) with a pre-tax income of $70,000+ spent a total of 50.5% of total food
expenditure away from home, even though they comprised 17.0% of all HH [3]
20.0% of industry revenue is estimated to be derived from business and business travelers,
as well as from tourists, including international visitors.
HH with income of more than $50,000 account for about 70.0% of the total personal
expenditure on food eaten away from the home. Of this group, those HH in the highest
income quintile provide about 30.0% of the total away from home food expenditure [4].
There have been market changes and some demand for healthy foods and choices, away
from high fat, high salt and super-sizes meals, as the obesity epidemic continues to be raised
and recognized.
There is currently still a growing number of households with high disposable income of
$50,000 or above in the key groups 35- to 55-year-olds and baby boomers. Many have only
limited time to cook and so are searching for good value and quality meals, and convenience
and service in a hospitable and friendly environment.
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28. Market Analysis
Findings [2]:
Mature (age 61+) diners are more likely than others to try a new restaurant or dish for a
weekday meal. The restaurant experience is also seen more as a social and family occasion,
and like to share with family and friends, or coworkers
28.4% of Generation Y diners prefer to use technology to place orders and pay for meals
without interacting with counter staff (18.1% for Generation X, 12.1% for baby boomers,
7.5% for matures)
29% of Generation Y consumers indicated that they like to go to restaurants where the chef
interacts with guests (compared with 17.8% of baby boomers)
Most Generation X’s and Y’s like to check a restaurants menu prior to making a reservation
25% of Generation X's and Y's said they like to make changes to the menu items (compared
to 14.5% of mature diners)
[2] 2008 Market Research Study by Restaurants and Institutions Magazine
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29. Market Analysis
ONLINE RESTAURANT SERVICES [6]
Zagat.com: 333,962 / rank is 5,994
Dine.com: 8,738 / rank is 90,031
Opentable.com: 499,809 / rank is 4,029
Menupages.com: 499,810
Fodors.com: 633,170 / rank is 3,149
TOTAL 1,975,489
(x 10% capture of online restaurant locator industry = 197,448 prospective users)
(Source: Compete.com)
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30. Market Analysis
DIET INDUSTRY [6]
Weight Watchers: 4,715,662 unique visitors per month / rank is 337
JennyCraig.com: 210,800 / rank is 9,565
Nutrisystem.com: 810,334 / rank is 2,470
TOTAL: 5,736,796
(x 10% of dieting industry = 573,679)
(Source: Compete.com)
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31. Market Analysis
Sources:
[1] http://www.katzvaluation.com/documents/Full-Service-Restaurant.pdf
[2] 2008 Market Research Study by Restaurants and Institutions Magazine
[3] US Census Bureau Data
[4] US Census Household Expenditure Data
[5] http://www.restaurant.org/tools/magazines/rusa/magarchive/year/article/?articleid=138
[6] Compete.com
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32. Market Analysis
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33. Revenue Streams
• Subscription Fees (individuals)
• Licensing Fees (hotels, travel agents, event planners, corporations)
• Corporate Sales / Membership Fees: HR departments for consistent company-
wide F&B guidelines & reducing costs (employees use Food Compass to find pre-
approved restaurants that meet their preferences).
• Advertising Sales (restaurants, travel agencies, health care providers, dieticians,
health clubs, personal trainers)
• Sponsorships (National Restaurant Chains, Weight Watchers, evite, etc.)
• Deals / Coupons percentage
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34. Final Thoughts
We plan to continue forward with the Food Compass for the next phase of our class;
more research is needed regarding:
• The viability of the different revenue streams.
• More in-field testing / face-to-face interviews, specifically targeting end users
(corporate travel agents, hotel concierges, personal trainers, health clubs, etc.)
• Après-meal alerts / feedback feature. We think there’s more to this feature than
meets the eye.
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