ENBIS Challenge 2009 Thomas Mühlenstädt Institut für Mathematische Statistik und
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ENBIS Challenge 2009 Thomas Mühlenstädt Institut für Mathematische Statistik und industrielle Anwendungen
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ENBIS Challenge 2009 Thomas Mühlenstädt Institut für Mathematische Statistik und industrielle Anwendungen. New Configurations: 4 GB RAM 2.4 GHz 17‘ Screen. Configuration:. Further comments on Configurations: No differences between stores - PowerPoint PPT Presentation
Transcript of ENBIS Challenge 2009 Thomas Mühlenstädt Institut für Mathematische Statistik und
ENBIS Challenge 2009
Thomas Mühlenstädt
Institut fürMathematische Statistik und industrielle Anwendungen
Time
Rev
enue
per
day
Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov
2000
0060
0000
1000
000
Further comments on Configurations:
• No differences between stores• 864 possible combinations• No empty class• Costumers prefer „medium“ configurations
Configuration:
possible specifications
Screen 15” 17” *
Battery 4 h 5 h 6 h
Memory 1 GB 2 GB 4 GB *
CPU 1.5 GHz 2GHz 2.4 GHz *
HD 40 GB 80 GB 120 GB 300 GB
W Lan No Yes
Bundled No Yes
New Configurations:
4 GB RAM 2.4 GHz 17‘ Screen
Price minimum requirement: £ 300 15“, 4h Battery, 1 GB RAM, 1.5GHz, 40 GB HD
Promotional sales activities:
August / September: Increase of daily sales volume: 100 %Increase of daily revenue: 86 %
December:Increase of daily sales volume: 78 %Increase of daily revenue: 70 %
Time
Rev
enue
per
day
Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov
2000
0060
0000
1000
000
Price / Revenue:
RAM: 2GB: £ 50 4GB: £ 150
HD: 80GB: £ 40 120GB: £ 60300GB: £ 120
Screen: 17”: £ 100CPU: 2GHz: £ 25 2.4GHz: £ 50
Battery: 5h: £ 20 6h: £ 100Wlan: Yes: £ 20
Bundled: Yes: £ 50
New Configurations:
4 GB RAM 2.4 GHz 17‘ Screen
Second and Third Jump:
Marketing, Price?
Store: No influenceTime: Decreasing trend,
depending on memory
Discount?: 5 stores granted discount of approx 30% duringMarch, June, September, December
Time
Re
ven
ue
pe
r st
ore
Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov Dec
05
00
00
15
00
00
25
00
00
Spatial topics:
Revenue per day in each store:Big differences between stores
Map of LondonLocation of Stores: „big“ stores „medium“ stores „small“ stores
population density plot
Conclusions:• Some stores might be better• Location not always good
Conclusions:
• Configurations:– Offer more hard ware– Also „smaller“ specifications
• Price / Revenue:– Discounts not effective,– Revenue increased two times
• Store locations:– Concentrate on „big“ shops– Some stores might perform better– Some stores are not located very good
• Use of data:– More connotation
