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

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