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  1. ESPAR - AnalystEvaluation ofSites andPosterAudienceResearchCredential Presentation March 2007

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

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

  4. OOH ad volumes Travel Surveys OOH formats locations data Poster awareness research GRP, Reach, Frequency, etc. Visibility factors modeling Mathematical modeling of OOH campaigns evaluation OOH sites scoring (ratings) Monthly monitoring of OOH Traffic modeling per cities Traffic and Pedestrians flows measurement Population density data Electronic maps of cities (GIS) ESPAR outdoor research concept

  5. I. OOH monitoring on GIS basis • Dec 1996 - Moscow • Aug 1997 - St. Petersburg • Jul 1999 - other 1mln+cities (12) • Dec 2000 - 32 cities • Jul 2001 – 50 cities • 180 000 ad faces are covered (sizes 1.2x1.8+) • Represent about 80% of all OOH sites in Russia

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

  7. 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)

  8. Collecting information: routes planning

  9. Information gathering: maps preparation for inspection

  10. Information gathering: maps preparation

  11. OOH sites in Moscow

  12. OOH sites in Moscow

  13. OOH sites in Moscow

  14. OOH sites in Moscow

  15. Library of posters

  16. 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

  17. ODA-Stat Program

  18. ODA-Stat:selectionof cities and period for analysis

  19. ODA-Stat:statistical analysis (address programs)

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

  21. ODA-Stat: selection of criteria and symbols for mapping

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

  23. Detailed map

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

  25. II. OOH potential audience measurement (Site Evaluation)

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

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

  28. Traffic counts • 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 • 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)

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

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

  31. Model of Pedestrian Flows: Moscow

  32. Public Transit Routes

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

  34. GIS Capabilities: overlaying geocoded databases

  35. OTS estimation • 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 • Use of visibility factors for coefficients, decreasing OTS (similar to OSCAR system in UK)

  36. Use of modeling for geometric visibility parameters

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

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

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

  40. ODA-View Daily audience (000) Monthly audience GRP (18+) Site owner Format type Number of faces Size Face Transport position Direct road segment Cost per month

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

  42. GRP, Reach, Frequency • Basic formula GRP = Reach (1+) * Frequency • 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 • Development of transportation simulation models for major cities to evaluate duplication of contacts

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

  44. Transportation network (graph) and residential areasNewtonian gravity models for evaluating daily travel Simulation modeling of Origin and Destination of daily trips

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

  46. ODA-Plan. Address program creation

  47. 25 faces: evenly distributed campaign throughout a city

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

  49. 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 • 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

  50. Reach and frequency - 25 evenly distributed ad faces