1. iOrder.in
Justin Velthoen
Daniel Lagos
Katie Stevenson
Kara Meyer

2. Agenda
Introduction
Statement of business case questions
Research methodology
Statistical results and analysis
Marketing implications
Limitation and future research
Conclusions
Question session
3. Introduction
Smart phone application capabilities
Thesis:The objective of the present study is to determine if there is a demand in the market for a smartphone application to order take out; the study compares the qualities such as age, income level, phone-type, and technical ability against the propensity to order takeout and the inclusion of other peoples orders when ordering.
4. Statement of Business Case Questions
Daniel Lagos
5. Question:
Is there a market for an iPhone application which places take out orders and processes payment?
Further.
Is there a market for an interest in a place and pay application for restaurants?
6. Secondary Researchand Methodology
Kara Meyer
7. Age
Affect of Age on Take Out Ordering
Ho (1)
All age groups will have the same number of orders placed per week
Ho (2)
The number of people included in a given take out order is the same regardless of age
Regression
8. Age
By the end of 2007
Approximately 57% of iPhones purchased in July were by US customers 35 years of age or younger
(iSuppli, 2007)
9. Income
Affect of Income on Take Out Orders Placed
Ho (1)
Income has no affect on the number of orders placed in any given week
H0 (2)
Income has no affect on the number of people included in any given order
Regression
10. Income
Upon initial release of the iPhone in 2007
43% of users earned an income greater than $100,000
Upon initial release of the iPhone 3G in 2008
Use amongst individuals earning $25,000-50,000 increased by 48%
Use amongst individuals earning $50,000-$75,000 increased by 46%
(Branding, 2008)
11. Phone Style
Affect of particular brands on Take Out orders placed
H0 (1)
Brand has no particular impact on the number of orders placed in any given week
H0 (2)
Brand has no affect on the number of people included in any given order
Regression
12. Phone Style/Brand
In a recent nationwide poll of 1,479 responses:
43% owned an iPhone
29% owned an Android (431 respondents)
9% owned a Blackberry (131 respondents)
9% owned a Palm (128 votes)
5% owned a Symbian (74 votes)
5% Windows Mobile (73%)
(Mashable, 2009)
13. Technical Aptitude
Affect of an individuals technical abilities on take out orders placed
Ho (1)
The method used to place a take out order has no affect on the number of orders placed each week
Ho (2)
The method used to place orders has no affect on the number of people included in any given order
Tukey t-test
14. Technical Aptitude
An increasing number of individuals are turning to more innovative ways of accomplishing everyday tasks
Ordering food
Restaurant and factory supply chain management and inventory replacement systems
Online banking
Just in time inventory management practices
15. Population and Sampling Frame
Population:Social networkers who use smart phones
Sampling Frame:Convenience Sample
Social networks such as Facebook, MySpace, Twitter, LinkedIn, and regular email
16. Population and Sampling Frame
As of 2009:
Social networking usage on smart phones has skyrocketed 187% to 18.3 million unique users in July 2009
Social networking sites account for 32% of all smart phone activity
Facebook: 14.7 million users
MySpace:7.1 million users
Twitter:4.1 million users
(Media Week, 2009)
17. Methodology
Two forms of data collection were utilized
Posted survey
Self-initiated response
Social networks
Email survey
Prompted response
Regular email, forwards
*Generally only one method of data collection is utilized
18. Methodology
Selection
Out of 115 respondents
52 were deemed usable
All regular phone users, non-numeric responses, and obscene responses were eliminated
Usable responses were dummy coded
19. Methodology
Analytical tools used
MegaStat
Excel
Google Docs
20. Methodology
Statistical Tools used
Mean
Standard Deviation
Correlation
Regression analysis
T-test
21. Statistical Results and Discussion
Katie Stevenson
22. Research Framework
23. Cell Phone Data
Smartphone Platform:
Regular: 42.6%
iPhone: 32.2%
Blackberry: 15.7%
None: 6.1%
Android: .8%
Palm: .8%
HTC: .8%
smartest smartphone:.8%
24. After Eliminating Regular Phones and Outliers
Independent Variables:
Age:
Up to 18: 1 respondents: 1.9%
18-21: 0 respondents: 0%
21-30: 19 respondents: 36.5%
31-40: 19 respondents: 36.5%
41-50: 4 respondents: 7.7%
51 & up: 9 respondents: 17.3%
Income:
Up to $20,000: 4 respondents: 7.7%
$20,000-$40,000: 4 respondents: 7.7%
$40,000-$60,000: 10 respondents: 19.2%
$60,000-$80,000: 3 respondents: 5.8%
$80,000-$100,000: 12 respondents: 23%
$100,000-$120,000: 6 respondents: 11.5%
$120,000 & up: 13 respondents: 25%
Platform:
iPhone: 35 respondents: 67.3%
Blackberry: 16 respondents: 30.8%
HTC: 1 respondent: 1.9%
Palm: 0 respondents: 0%
Android: 0 respondents: 0%
Method:
No technical aptitude:
Call in: 30 respondents: 57.7%
Dont order: 2 respondents: 3.8%
Counter: 6 respondents: 11.5%
Total: 38 total respondents: 73.1%
Technical Aptitude:
Website: 13 respondents: 25%
3rd party Website: 1 respondent:1.9%
Total: 14 respondents: 26.9%
Dependent Variables:
Order Out:
Average: 1.62
People Ordered for :
1.71
25. Age vs. Order Out Regression and Scatter plot
26. Age vs. People Ordered Out For Regression and Scatter plot
27. Income vs. Order Out Regression and Scatter plot
28. Income vs. People Ordered Out For Regression and Scatter plot
29. Platform vs. Order Out Regression and Scatter plot
30. Platform vs. People Ordered Out For Regression and Scatter plot
31. Technical Aptitude vs. Order Out Two-Sampled Two-Tailed T-Test
32. Technical Aptitude vs. People Ordered For Two-Sampled Two-Tailed T-Test
33. Marketing Implications
Daniel Lagos
34. Marketing Implications
Determinants without correlation
Age vs inclusion of other people
Phone type vs inclusion of other people
Income level vs take out per week
Income vs smart phone application usage?
35. Marketing Implications
Phone type vs take out per week
Platform does not have an effect
Begin application with platform that facilitates application development
May require Anova test
36. Marketing Implications
ANOVA Results: no statistical significance between means
Non technical vs. technical aptitude for number of times ordered per week
Non technical vs. technical aptitude for inclusion of other people
Implication: target market becomes a matter of cost effectiveness
37. Limitations, Future Research& Conclusion
Justin Velthoen
38. Limitations / Future Research
Limit of the Demographic
Survey customers at Cellular dealers and Takeout restaurants
Use of other statistical tools
Google Docs too easy to cheat
Many preliminary questions led to regression analysis.
Increasing Sample Size
Total Surveys Taken: 115
Number Eliminated (non-smartphone): 49
Number Eliminated (Unusable): 14
Total Usable: 52
39. Conclusion
Age, Income, Phone-type, and Technical ability as they relate to Ordering Take Out and Number of People Ordered For
The best correlation: Age
What does this mean for marketing?
Where do we go from here?
What inhibits people from ordering take out?
Would people pay to make ordering more convenient?
Would people want to pay through a 2nd service for their order?
Would people be more likely to order take out if they could collaborate with their family or friends?
40. La fin