iab canada metrics 2015 - the art and science of hyper local - dilshan kathriarachchi
TRANSCRIPT
The art and science of hyper local metrics. insights. results.
3rd of December, 2015
Thank you for being here today
Toronto
Presenter:
Dilshan Kathriarachchi CTO, EQ Works
Dilshan Kathriarachchi
LOCATION. SO WHAT?should marketers be excited about second gen. hyper-local
Hyper-Local. So What? Should marketers me excited about second gen hyper-local?
WHY?
OFFLINETO
ONLINE
RE-ENGAGE
RELEVANCE
AUDIENCE
HYPER-LOCAL AUDIENCEcore components of a hyper-local campaign
Hyper-Local. So What? Should marketers me excited about second gen hyper-local?
Time + Place
Capture your audience at the right moment and at
the right time
Behaviour
Target audiences that exhibit unique behaviour
important to you
Apps + Content
Find your audience next to mobile content that is
contextually relevant to you
GEO FRAUDfraudulent geo-coordinates being generated
Pitfalls of Location Data Is all location data the same? Of course not!
21% Geo Fraud
12% Centroids
5% Randomized
3% Complex
1% Other
Geo Fraud Awarenessgreatest challenge with hyper-local
GEO FRAUDfraudulent geo-coordinates being generated
Pitfalls of Location Data Is all location data the same? Of course not!
CLEAN UPhow to eliminate geo fraud
Pitfalls of Location Data Is all location data the same? Of course not!
2% RANDOMIZEDDEVICE
devices with historically fraud behaviour
HYPER-LOCAL AD first contact
3%RANDOMIZED
PUBLISHER is this a fraudulent publisher?
12%CENTROID
DETECTION obvious fraud
2%WATER-BODY
CHECK is the bid request over water?
1% INACCURATE GPS historically accurate device
POINTS OF INTERESTphysical locations as behavioural beacons
Quick ServiceRestaurant
Coffee Shops
Public Transit
Retail Shops Banks Sports Venues Tourist Movie Theatres Fine Dining Fitness
CAPTURE AD OPPORTUNITIES AROUND POINTS OF INTEREST
PROXIMITY & AUDIENCEtarget frequent visitors to a location or chain
100 meters
25% OF STORE VISITS
Can be targeted with Proximity
Emergent Behaviour Exploring hidden behavioural trends in location data
CROSS BORDER TRAVELLERSidentify frequent travellers to the US
Emergent Behaviour Exploring hidden behavioural trends in location data
INTERNATIONAL TRAVELLERidentifying globe trotters
+
Emergent Behaviour Exploring hidden behavioural trends in location data
SMALL BUSINESS OWNERSreach small business owners with hyper-local
Emergent Behaviour Exploring hidden behavioural trends in location data
100+ visits 20+ visits 1 - 5 visits
Business Owners Regulars Walk-ins
BEHAVIOURAL INCOMEPersonalized messaging around proximity and context
Groceries
Where you buy your groceries is a great
indicator of household income
DiscretionarySpending
How you choose to spend your disposable
income
Dining
The price tiers associated with the restaurants you
frequent
Emergent Behaviour Exploring hidden behavioural trends in location data
BEHAVIOURAL INCOMEPersonalized messaging around proximity and context
Emergent Behaviour Exploring hidden behavioural trends in location data
AT HOMEactivity footprint for a home location
15Strategies Using data to solve advertiser problems.
mon
tue
wed
thu
fri
sat
sun
AT WORKactivity footprint for a place of work
mon
tue
wed
thu
fri
sat
sun
Strategies Using data to solve advertiser problems.
MALL TRAFFICneighbourhood aware reporting
Strategies Using data to solve advertiser problems.
PROXIMITY DRIVEN MESSAGINGPersonalized messaging around proximity and context
Messaging to book an appointmentrich creative for in-‐ad appointments
>7 kilometers
>2 kilometers
<2 kilometers
* distance to nearest relevant location from user
Awareness messaging for Productsresearch tools like mortgage calculators
Drive users towards walk insdirections and opening hours
Strategies Using data to solve advertiser problems.
HYPER-LOCAL METRICShow to measure your hyper-local campaigns
Optimizing for Local Closed-loop optimization with powerful Hyper-Local targeting
STORE VISITSLOCATION AFFINITY
POST-ENGAGEMENT
BEHAVIOURAUDIENCE
MULTI-LAYERED FILTERtrickle-down filters for hyper-local
Optimizing for Local Closed-loop optimization with powerful Hyper-Local targeting
Geo Fraud elimination
Apps & Viewability
Type of Location
Time of Day and Weather
Audience & Behaviour
OPTIMIZING LOCALdetecting pockets of performance
Optimizing for Local Closed-loop optimization with powerful Hyper-Local targeting
1. Learning Each campaign begins life by going through a controlled learning period.
2. Audience Building Learning data is mined to iden=fy ac=onable audiences and key POIs
4. Prospec=ng Based on user behaviour, iden=fy users with the highest
probability of engaging and most ac=ve loca=ons..
5. Retarge=ng Using semi-‐persistent DeviceIDs and device fingerprints, we retarget prospec=ve users.
6. Engagement Drive users towards conversions and re-‐
engagements with trends passed to learning.
3. Op=miza=on Mul=-‐layered op=miza=on trims audiences down to their most effec=ve.
Thank You
for your precious time and attention
Please don’t hesitate
Questions