www.london-calling.org.uk current and future, business and consumer applications of lbs technology...
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www.London-Calling.org.ukwww.London-Calling.org.uk
Current and future, business and consumer applications of LBS technologyAndrew Grill, Mobile Advertising EvangelistBritish Computer Society, September 2008
www.London-Calling.org.uk
What we’ll cover
• The location challenge
• Why the Starbucks example will never happen
• Current LBS applications
• Future LBS applications
• Location’s place in the mobile advertising value chain
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Let’s kill off the “Starbucks Example”
Assume the following campaign
50,000 opt-in users
2 week campaign (Mon-Fri) = 10 days
8 hour window (8am – 4pm)
Check location every 5 minutes by operator cell-ID
Assume 10p per poll
What would this cost?
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The cost breakdown…
50,000 users10 days8 hours12 polls/hr10p per poll£ 4.8m
ALMOST5 MILLION Pounds!
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Current LBS applications
• Navigation & Mapping
• Where am I?
• Buddy Finder (eg Buddyping)
• Local search
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Google Mobile Maps
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Google Mobile Maps
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Google Maps – now with sponsored links
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Nokia Maps 2.0
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Fire Eagle from Yahoo!
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Microsoft LIVE (UK)
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Blogloc – post your location on a blog
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Shozu + Flickr = geotagged photos
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Locatik
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WiFi location – Skyhook Wireless
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Other examples
Gypsii Brightkite
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Brightkite feeds twitter
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Sniff on Facebook
Sniff Sniff on Facebook
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Mobile LBS Landscape
• Significant growth in LBS, fuelled by convergence of:– Device capabilities (larger - sharper displays , 3G data rates & GPS)– Quality content (maps, POI, navigation engines)– Attractive data tariffs
• Current focus around navigation & search
• Future growth expected in location based advertising & social networking
• Existing MNO monopoly on mobile location based services under threat from OTT players
• Within MNO community, growing consideration of X-network operation for services such as advertising
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What does this all mean for proximity based advertising?
• Technical developments in place
• Operator vs operator independent debate wide open
• Consumers starting to use GPS and operator independent location services
• Mobile advertising ramping up (Google vs operators)
• Flat rate data plans driving usage
• Location can become inventory
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The Mobile Advertising Value Chain
Advertisers need…Advertisers need…Advertisers need…Advertisers need…
Measurability • Ability to track consumer engagement by counting clicks vs. displays • Ability to track consumer engagement by counting clicks vs. displays
Targeting • Aggregated and specific customer information (e.g. age, gender, segment, location)
• Aggregated and specific customer information (e.g. age, gender, segment, location)
Relevance • Ads must be conveyed to the precise target in the right context …• Ads must be conveyed to the precise target in the right context …
Location • Information on the consumer location to increase relevance of ads • Information on the consumer location to increase relevance of ads
Advertisers/ Brands
Advertisers/ Brands
Marketing Agencies &
Media Buyers
Marketing Agencies &
Media Buyers
EnablersEnablers Content ProvidersContent
ProvidersAggregator
sAggregator
sDevice MakersDevice Makers
Wireless OperatorsWireless
Operators SubscribersSubscribers
Traditional Promotional Channels
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Zone detection for location based advertising
Fixed GeoZones
• Locations with high footfall and/or high value consumers
• Airports, railway stations, city centres, shopping malls, sports stadiums
• Provisioned by the operator and changed infrequently
Brand Defined Zones
• Zones related to specific brand locations
• Store locations; restaurants; special promotions
• Respond rapidly to brand campaigns
User Defined Zones
• Places/contexts that are important to an individual user
• My home, my office, my pub
• Provisioned by the user
• Further contextual advertising opportunities
Concept of “zone detection”Concept of “zone detection”
Highly scalable• Zone detection performed
at handset level• Notify on user entry/exit• No need to poll user’s
location• Accommodates network
changes
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Intelligent user profiling – can benefit from location
• Location is only part of the story• Who can profile best?• User initiated or captured by device• Gender• Phone type• Postcode• Segmentation• Age• Gender• Spending pattern• Credit worthiness• Location
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