ESPAR - Analyst Evaluation of Sites and Poster Audience Research Credential Presentation March 2007

ESPAR-Analyst Established in 1992 by a group of Moscow State University geographers and cartographers (“Analyst”) Established in 1992 by a group of Moscow State University geographers and cartographers (“Analyst”) Since 1996 – “ESPAR-Analyst” - specializing in outdoor research Since 1996 – “ESPAR-Analyst” - specializing in outdoor research

Types of research provided Outdoor advertising monthly monitoring Outdoor advertising monthly monitoring Based on geographical-informational systems (GIS)Based on geographical-informational systems (GIS) OOH potential audience measurement (evaluation) for individual sites OOH potential audience measurement (evaluation) for individual sites Estimation of key media indicators (reach / frequency) of advertising campaigns Estimation of key media indicators (reach / frequency) of advertising campaigns OOH posters awareness research OOH posters awareness research

ESPAR outdoor research concept Monthly monitoring of OOH Electronic maps of cities (GIS) Traffic and Pedestrians flows measurement OOH formats locations data Population density data Poster awareness research Mathematical modeling of OOH campaigns evaluation Traffic modeling per cities GRP, Reac h, Frequ ency, etc. Travel Surveys OOH sites scoring (ratings) OOH ad volumes Visibilit y factors modelin g

I. OOH monitoring on GIS basis I. OOH monitoring on GIS basis Dec Moscow Dec Moscow Aug St. Petersburg Aug St. Petersburg Jul other 1mln+cities (12) Jul other 1mln+cities (12) Dec cities Dec cities Jul 2001 – 50 cities Jul 2001 – 50 cities ad faces are covered (sizes 1.2x1.8+) ad faces are covered (sizes 1.2x1.8+) Represent about 80% of all OOH sites in RussiaRepresent about 80% of all OOH sites in Russia

Key monitoring objective – make OOH advertising transparent OOH ad volumes (ad spend, brands, advertisers, product categories) – together with TNS/Gallup AdFact OOH ad volumes (ad spend, brands, advertisers, product categories) – together with TNS/Gallup AdFact OOH media environment – classification of formats, locations, suppliers/sites owners OOH media environment – classification of formats, locations, suppliers/sites owners Creation of single database for media planning possibility (unification of all sites IDs) Creation of single database for media planning possibility (unification of all sites IDs) OOH media clutter analysis OOH media clutter analysis

Methodology 1. Development of detailed electronic maps of cities (GIS) - Exact link of a site to geo point within a city – basis for monitoring 2. Routes planning to cover city territory 3. Key data gathering method – visual monthly inspections of all site locations 4. Development of unique coding (IDs) system and site classification 5. Development of system of catalogs of brands, product categories, advertisers – joint database with TNS Gallup 6. Preparation of photo libraries of posters (Moscow, SPb) 7. Supply information in consumer required format – possibility for both statistical analysis and mapping capabilities (ODA-Stat)

Collecting information: routes planning

Information gathering: maps preparation for inspection

Information gathering: maps preparation

OOH sites in Moscow

Library of posters

Methodology: key indicators registered 1. Unique ID 2. Address 3. Type of display 4. Size 5. Site owner 6. Average estimated market price 7. Brand advertised 8. Product category / service type 9. Advertiser

ODA-Stat Program

ODA-Stat: selection of cities and period for analysis

ODA-Stat: statistical analysis (address programs)

ODA-Stat: creation of address program with given criteria and filters

ODA-Stat: selection of criteria and symbols for mapping

Ex.: Moscow, March 2004, 3 х 6 billboards Advertisers, selected for analysis (mobile operators)

Detailed map

Ex.: Chelyabinsk, March 2004, 3 х 6 billboards

II. OOH potential audience measurement (Site Evaluation)

“Of all the major media, Outdoor is by far the most difficult to research.” Chris Dickens, Former chairman, POSTAR

General approach to measurement: Vehicular and Pedestrians flows x Visibility factors of each ad face = Potential audience (OTS – Opportunity to See)

Combination of long-term and short-term measurements Combination of long-term and short-term measurements Long-term (during a day) at key spots – opportunity to identify typical daily curves of traffic flows Long-term (during a day) at key spots – opportunity to identify typical daily curves of traffic flows Short-term (10 min in rush hours) – opportunity to estimate flows for road segments Short-term (10 min in rush hours) – opportunity to estimate flows for road segments Recalculation of short-term counts into daily volumes, based on typical daily cycles (math coefficients recalculation system) Recalculation of short-term counts into daily volumes, based on typical daily cycles (math coefficients recalculation system) Traffic counts

Short-term into daily traffic flows recalculation system (coefficients)

Vehicular Traffic Volumes Estimation Identify segments of roads with constant traffic volumes (from cross road to cross road) Identify segments of roads with constant traffic volumes (from cross road to cross road) Classification, IDs and coding of road segments Classification, IDs and coding of road segments 10 min measurements for every flow direction 10 min measurements for every flow direction Data processing, recalculation into daily flows Data processing, recalculation into daily flows Traffic volumes mapping as a method of data control Traffic volumes mapping as a method of data control

Model of Pedestrian Flows: Moscow

Public Transit Routes

Potential audience measurement Audience composition: people in cars, public transport passengers, pedestrians Audience composition: people in cars, public transport passengers, pedestrians People in cars = number of cars x 1.5 (average car occupancy) People in cars = number of cars x 1.5 (average car occupancy) Public transport: official data on intervals, mapping of routes, x coefficient 20 Public transport: official data on intervals, mapping of routes, x coefficient 20 Pedestrians measurements (evaluations) for each site Pedestrians measurements (evaluations) for each site

GIS Capabilities: overlaying geocoded databases

OTS estimation Identification of “effective” traffic directions for every face of OOH site (up to 3 directions on a cross road) and traffic volumes Identification of “effective” traffic directions for every face of OOH site (up to 3 directions on a cross road) and traffic volumes Visibility factors estimation for every face, for every “effective” traffic direction Visibility factors estimation for every face, for every “effective” traffic direction Use of visibility factors for coefficients, decreasing OTS (similar to OSCAR system in UK) Use of visibility factors for coefficients, decreasing OTS (similar to OSCAR system in UK)

Use of modeling for geometric visibility parameters

Visibility factors and reduction coefficients (3 х 6m billboards) Visibility range Angle Accentricity Height Clutter (other faces in visibility range) Visibility obstacles Distance to street lights Illumination

Calculation of Rating for ad face Gross audience x visibility factors = effective potential daily audience (OTS) Gross audience x visibility factors = effective potential daily audience (OTS) Rating (GRP) = OTS / market population (18+) * 100 Rating (GRP) = OTS / market population (18+) * 100 Current ESPAR database has evaluations for over 100,000 3х6 m faces in 40 cities of Russia Current ESPAR database has evaluations for over 100,000 3х6 m faces in 40 cities of Russia

Software for providing of evaluation data – ODA-View Integration of maps, detailed plans, photos and evaluation data Integration of maps, detailed plans, photos and evaluation data Preparation of sample from evaluated address programs Preparation of sample from evaluated address programs Preparation of ad sites passports Preparation of ad sites passports Preparation of presentational materials Preparation of presentational materials

ODA-View Daily audience (000) Monthly audience GRP (18+) Site owner Format type SizeFace Number of faces Transport positionDirect road segment Cost per month

III. Evaluation of campaign distribution (R&F modeling)

GRP, Reach, Frequency Basic formula Basic formula GRP = Reach (1+) * Frequency Campaign GRP is a sum of ratings of all evaluated sites in address programs Campaign GRP is a sum of ratings of all evaluated sites in address programs Average frequency is calculated based on modeled daily movement of audience within a city Average frequency is calculated based on modeled daily movement of audience within a city Development of transportation simulation models for major cities to evaluate duplication of contacts Development of transportation simulation models for major cities to evaluate duplication of contacts

ESPAR-Analyst Research in Outdoor Concept Monthly Monitoring (ODA-Stat) Computer City Maps (GIS) Traffic and Pedestrian Counts Inventory Location Data Population Census Data Poster Recognition Tracking Math Models for OOH Campaigns (ODA-Plan) City Traffic Flows Models GRPs, Reach, Frequency etc. Travel Surveys Site Evaluation (Ratings) Competitive Advertising Volumes Data Visibilit y Factors Model

Transportation network (graph) and residential areas Newtonian gravity models for evaluating daily travel Simulation modeling of Origin and Destination of daily trips

Estimation of daily reach and frequency: ODA-Plan Program is based on traffic flows modeling Program is based on traffic flows modeling Objective: planning and evaluation of OOH campaigns Objective: planning and evaluation of OOH campaigns Daily reach / frequency measurements for OOH campaigns Daily reach / frequency measurements for OOH campaigns Work with evaluated individual sites Work with evaluated individual sites

ODA-Plan. Address program creation

25 faces: evenly distributed campaign throughout a city

Daily reach and frequency (even distribution, R(1+ ) = 20.3 F = 1.3)

Duration of OOH campaign factor evaluation Industrial standard in OOH in USA and Canada: Gallup Math Model – evaluation of reach and average frequency for campaign Industrial standard in OOH in USA and Canada: Gallup Math Model – evaluation of reach and average frequency for campaign Frequency = (sum of daily GRP’s x number of days)/100 + K (K = 2 to 6) Frequency = (sum of daily GRP’s x number of days)/100 + K (K = 2 to 6) Reach = (sum of daily GRP’s x number of days)/frequency Reach = (sum of daily GRP’s x number of days)/frequency

Reach and frequency - 25 evenly distributed ad faces

Additional functions of campaigns evaluaiton Analysis of address program split between municipality districts Analysis of address program split between municipality districts Proximity Analysis – targeting opportunities (HORECAs, schools, etc) Proximity Analysis – targeting opportunities (HORECAs, schools, etc) User-friendly interface, allowing to prepare presentation materials for each address program User-friendly interface, allowing to prepare presentation materials for each address program

Poster awareness studies (Poster Track)

Poster awareness research

Moscow “norms” for 3x6 campaigns

Google Earth space images and outdoor sites in Moscow

OOH sites in Moscow

Thank you for your time!