blackberry market research project
DESCRIPTION
Research project for BA Marketing Managment courseTRANSCRIPT
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Bridging the gap of innovationDion McKenzieRobert Shumbusho 12’
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Background information
Research problem & research strategy
Research method and results
Data Analysis
Recommendations & Limitations
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• Founded in 1989• First phone launched 1999• 2011 Revenue: $19.9 billion• 2011 Net Income: £3.4
billion
BUSINESS INFORMATION
19.6%
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TARGET MARKET
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COMPETITORS
46.0% 27.8%
• First iPhone launched 2007• 2011 Revenue: $108 billion• 2011 Net Income: $25.9• billion
• Nexus One launched 2010• Sold 190m • 2011 Net Income:
$2.5billion
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Advantages:• Brand awareness, On going
innovation, Prestige
Disadvantages: • Price point and lack of choice
Advantages: • Cheap, multi platform, variety of
smartphones
Disadvantages: • Still behind IOS in terms of apps, Too
many handsets
COMPETITORS
46.0% 27.8%
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COMPETITORS CONT.
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Background information
Research problem & research strategy
Research method and results
Data Analysis & Findings
Recommendations & Limitations
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• Convergence of business users > prosumer users
• Innovation
• Bad publicity
RESEARCH PROBLEM
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RESEARCH STRATEGY
OBJECTIVES• Customers existing mobile phone characteristics
• Customer preferences of mobile phone choice
• Which consumers Blackberry should focus their marketing efforts on
PRIMARY RESEARCH• Questionnaire: “How clever is your mobile”
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STRATIFIED RANDOM SAMPLING
• Age 18 and over
• Location Highcross Shopping Centre
(Leicester)
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Background information
Research problem & research strategy
Research method and results
Data Analysis
Recommendations & Limitations
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86%
5%
9%
Collected Survey Data
CompletedDidn't qualify Incomplete
RESEARCH METHOD
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DATA COLLECTED: SURVEY STRUCTURE
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SAMPLE DEMOGRAPHIC PROFILE
GENDER
Male 51%
Female 49%
AGE
15-20 25%
21-25 26%
26-30 20%
31-35 8%
36-40 6%
41-45 6%
46+ 8%
OCCUPATION
FT Student 40%
Financial services 6%
Sales/Marketing 5%
Engineering 4%
IT 1%
Government/Legal 2%
Medical/ Health Service 3%
Education 8%
Retail 17%
Self-employed 3%
Media Journalism 2%
Performing Arts/Designer 1%
Non-profit/Charity 2%
Retired 2%
Unemployed 3%
EDUCATION
Less than secondary school
4%
GCSE 9%
A-Level 47%
BA/BSc 26%
MBA 6%
PhD 6%
Other 2%
CHILDREN
Yes 40%
No 60%
INCOME PER ANNUM
0-12 38%
13-25 20%
26-38 9%
39-51 3%
52+ 2%
Prefer not to say
27%
MARITAL STATUS
Single 57%
Co-hab 20%
Married 28%
Divorced 5%
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Background information
Research problem & research strategy
Research method and results
Data Analysis
Recommendations & Limitations
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• Multiple regression Identify factors that significantly
affect smartphone choice
• Cluster analysis Identify meaningful customer
segments (unknown variables)
• Discriminant analysis Profile each segment
demographically
ANALYSIS METHODS
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REGRESSION ANALYSIS
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Features BETA Tolerance Price .113 .670Voice clarity .059 .519QWERTY .185 .731Apps .218 .345
Features BETA Tolerance SMS -.236 .611
MMS .208 .504Web browsing .247 .132Video .104 .340Music .204 .688
Features BETA Tolerance Social networking .119 .144Video .113 .202
Features BETA Tolerance Magazine- Lifestyle .150 .689In Store Reps .206 .702Television .053 .661
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Summary of the important values
Model R Square1 .080
2 .169
3 .240
4 .250
R-Square- tells us how much of the variance in the dependent variable (like hood of purchase BB) is explained by the model (which includes all the features and characteristics)
In our case the total of values are 25%. Which means that the features and characteristics determine 25% of likelihood of purchasing a Blackberry
It is highly unlikely to get 100% prediction from the variables, therefore the other 75% could be influenced by many other things such as demographic and psychographic behaviours etc.
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1 2 3 4 5
0
5
10
15
20
25
30
Clusters
Wei
ght (
%)
CLUSTER ANALYSIS
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Cluster Summary
Cluster Frequency Proximity(how close each person in that cluster Is to each other)
Nearest cluster
1 25 1.51 4
2 31 1.89 3
3 17 1.88 2
4 43 1.7 1
5 28 2.04 2
CLUSTER ANALYSIS
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Series10
5
10
15
20
25
30
Cluster 1 Cluster 2Cluster 4
Clusters
Wei
ght (
%)
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CLUSTER ANALYSIS: NARROWING FOCUS
CLUSTER MEAN
1 4.76
4 4.58
5 4.70
CLUSTER MEAN
1 3.57
4 3.75
5 3.60
How likely are you to choose a ‘smartphone the next time you buy a mobile phone (continuous scale 1-5)
How likely are you to make your next phone a Blackberry (continuous scale 1-5) DV
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Features Cluster1 2 4
Price* M M H
Voice Clarity H H H
Email H H H
Ease of use H H H
Battery Life H H H
Touch screen* H M M
QWERTY Keyboard* M M M
Internet H H H
GPRS H M H
Style/Attractiveness H H H
Brand Reputation H H H
Camera H H H
Apps H M H
Gaming M L H
CLUSTER ANALYSIS - PREFERENCES
Rate the following features in order of importance in your initial choice of mobile phone
(Continuous scale 1-7)
1-3 (Low), 3-5 (Moderate, 5-7 (High)
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Features Cluster
1 2 4
SMS H H H
MMS H L M
Email H H H
Web Browsing H H H
Social Networking H L H
Purchased Apps* M M H
FRRE Apps H M H
Video H M H
Music H M H
#Q16 Please choose how often you currently use the following feature on your CURRENT MOBILE PHONE(Continuous scale 1-7)
1-3 (Low), 3-5 (Moderate, 5-7 (High)
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Features Cluster
1 2 4
SMS H H H
MMS* H M M
Email H H H
Web Browsing H H H
Social Networking H H H
Purchased Apps M L H
FRRE Apps H M H
Video H M H
Music H H H
How frequently do you intend to use the following features on your NEXT MOBILE PHONE (Continuous scale 1-7)
1-3 (Low), 3-5 (Moderate, 5-7 (High)
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#Q18 Generally, where do you look for information when purchasing technology products? (Binary variable)
Features Cluster
1 2 4
In store Sales Rep H H M
Magazines – High Tech L L L
Magazines – Lifestyle L L M
Internet – retailers website L M H
Internet – review websites L L L
Newspapers M H M
TV L M H
Word of mouth L L L
Trade Fairs L L L
1-3 (Low), 3-5 (Moderate, 5-7 (High)
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DISCRIMINANT ANALYSIS
Demo/psychographics Cluster
1 2 4
Age: 1(15-20) 7(46+) 3.16 2.32 1.66
Male? 0.52 0.39 0.6
Education: 1(HS) – 6(PhD) 3.48 3.35 3.3
Married? 0.16 0.19 0.21
Children? 0.24 0.39 0.4
Annual income: 1(0-12k) – 5(52k+) 2.23 3.16 2.89
Price sensitivity 0.24 0.55 0.47
Mac 0.4 0.35 0.15
PC 0.6 0.61 0.58
CLUSTER #1• More likely older (late 20s)• Less likely married, children• More likely a PC owner
CLUSTER #2• More likely female• Higher income• More price sensitive
CLUSTER #4• Male• Youngest – students
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Occupation: 0(N) - 1(Y) Cluster
1 2 4
Full-time student 0 0.68 0.77
Financial services 0 0.1 0.02
Sales/Marketing 0 0.1 0.07
Engineering/ Construction
0 0.06 0.05
Information Technology 0.04 0 0.02
Government/Legal 0.08 0 0
Medical/Health Services 0.04 0 0
Education 0.12 0.06 0
Retail 0.36 0 0
Self Employed 0.04 0 0
Media/Journalism 0.08 0 0
Performing Arts/ Designer
0.04 0 0
Non-profit/Charity 0 0 0
Retired 0.08 0 0
Unemployed 0.12 0 0
CLUSTER #1• Most diverse• Somewhat Retail and
Education
CLUSTER #2• Engineering/ Construction
and Education
CLUSTER #4• Mostly Student• Somewhat Sales/Marketing
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Background information
Research problem & research strategy
Research method and results
Data Analysis & Findings
Recommendations & Limitations
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TARGET MARKET SUMMARY
KEY DEMOGRAPHICS
Age Late 20’s
Gender 50/50, M/F (mixed)
Education A Levels or Bachelors Degree
Annual income £25-£40K
Married/Children No
Occupation Sales/Marketing, IT, Education, Retail,
Computer PC
Current Phone Blackberry or iPhone
Price Sensitivity Balanced
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High Importance Low ImportanceMobile phone use/preferences
Voice clarity Purchased Apps
Ease of use Gaming
Battery MMS
Style/Attractive
Camera
Intend to use Email Purchased Apps
Web browsing
Social networking
Music
Marketing Channels In-store sales reps Magazines – High Tech
Newspapers (advertising) Internet – review websites
TARGET MARKET SUMMARY
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MARKETING CHANNEL
• With smartphone technology becoming more advance, consumers need more explanation on key features and how to use phone to full capabilities. In store sales reps will play a big part in phone choice
• Newspaper ads also very significant with target market
• Ineffective: Word of mouth, internet – review sites, magazines
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PRODUCT
Improved OS (i.e. ease of use and speed) Improve voice clarity Improve web browsing experience Longer battery life
Minor focus: Social networking (integrate better)
Twitter is a trademark of Twitter, Inc.
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• Time constraints– Our respondents could have been more representative– Sample size could have been larger
• Questionnaire– Would of liked to gather more information, however with the
survey being 5 pages and 21 questions this could of affected participation
– Specialist terminology (isolated non-smartphone users)
LIMITATIONS
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