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Rotuaari field experiments ‐ history
• University of Oulu’s multidisciplinary research project which
studied mobile services
• 6 mobile services field trials implemented during 2003‐2006
SmartRotuaari1, 2 &3, TiernaJack, MobileKärpät,
SmartCampus
• Almost 2000 consumers/end users surveyed
• Hundreds of interviews
• The main result ‐ Consumers valued:
– Location‐based, context‐specific services
– Multichannel services
– Community services
Case Kärkkäinen
•Campaign objectives
– To collect mobile marketing permission
database
• Customer data
• Interests
• Customer satisfaction
•Campaign logic
– Several advertisements in company’s own
magazine Isomagneetti during November‐
December 2004
– WWW‐advertising and branch advertising
Mobile SMS
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And others driving traffic
• SEA +SEO= SEM
• Newsletters
Newsletter ‐ Path to purchase example from
our research
• 9 companies provided access to their live e‐newsletters account
• Some companies had thousands of e‐newsletters and majority have been
using service for several years
• 6 months period was selected and the e‐newsletters were analyzed
• Four regions are used to depict the visual scene of e‐newsletters
• The average click‐through rate of email is 16% with an open rate of almost
20%.
• The un‐subscription rate is low at 0.22%.
• We analyzed number of links, number of words in subject line and body,
number of pics etc. impact to CTR.
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Store
• Mobile,
• Smart displays
• Real‐time category management
Context / location awereness
• Apple's iBeacon technology ‐ in‐store
Bluetooth location trackers designed to
interact with smartphones ‐ enables retailers
and app publishers to identify people
individually the moment they enter a shop
• iBeacon available with many vendors
• Similar to iJack service developed by
Teliasonera in early 2000 which they
abandoned in 2004
Sales/deals
80%
Content relevant to
interest/
Location 62%
Swirl 2013
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Data rich environment
• Big data computing power & storage
• Big data SW and experienced staff (data based programming
skills Python etc.)
• Time‐series analysis / econometric modelling researchers
• Data visualization techniques
• Data rich environment analytics SW (Campalyst, Klout, SAS,
SAP, IBM etc.)
• Managerially interesting and relevant questions? Who wants
to consumer what, where and when ?
Customer value / experience
management
• Revenue performance management – marketing automation
• Big data, bigger customer data – new segments, price – dynamically
• From transactional data towards linking data towards more compelling
customer experience based on customers behavior understanding (+social
behavior data)