Measuring the Impact of Bicycle Marketing Messages Thomas Krag Mobility Advice Trafikdage i Aalborg, 27.08.2013
The challenge Compare
The pilot pictures
The choice
The survey technique Only one picture for each respondent Picture shown on top and button of each page Opinions on four transportation modes (car, bicycle, bus, train) VAS scales used for opinions
The questions Hvornår har du sidst brugt [kollektiv trafik, cykel, bil]? Har du en [bil, cykel], du kan bruge i hverdagen? Hvor stor risiko har [bilister, cyklister, buspassagerer, togpassagerer] for at komme til skade i bytrafikken? Hvordan er din oplevelse ved at bruge [bil, cykel, bus, tog] i bytrafik? Hvor godt ser [bilister, cyklister, buspassagerer, togpassagerer] ud i byens trafik? Hvor bange er du for at komme til skade, når du bruger [bil, cykel, bus, tog] i byen? Hvor godt passer [bil, cykel, bus, tog] til dit image? Hvilket af disse syv udsagn passer bedst på dig [syv udsagn om cykelvaner]? Hvad er dit/har du [køn, fødselsår, postnummer, hjemmeboende børn]?
Fingerprints
A few Facts and Statistical significance 2,379 responses received Carried out in April/May 2012 3M Research, sample only Average opinion scores calculated In many cases the average opinion score varies with picture in a statistical significant way (probability of null hypothesis less than 0.02)
The Seven Picture Survey Seven pictures used 3,674 responses received Carried out in May 2013 Sample only (Epinion)
The questions Hvornår har du sidst brugt [kollektiv trafik, cykel, bil]? Har du en [bil, cykel], du kan bruge i hverdagen? Hvor stor risiko har [bilister, cyklister, buspassagerer, togpassagerer] for at komme til skade i bytrafikken? Hvordan er din oplevelse ved at bruge [bil, cykel, bus, tog] i bytrafik? Hvor godt ser [bilister, cyklister, buspassagerer, togpassagerer] ud i byens trafik? Hvor bange er du for at komme til skade, når du bruger [bil, cykel, bus, tog] i byen? Hvor godt passer [bil, cykel, bus, tog] til dit image? Hvilket af disse syv udsagn passer bedst på dig [syv udsagn om cykelvaner]? Hvad er dit/har du [køn, fødselsår, postnummer, hjemmeboende børn]? Hvilke værdier knytter sig efter din mening til billedet vist foroven og forneden [sundhed, komfort, velvære, frihed, kontrol, hurtighed, livskvalitet]
Basics on Respondents
Basics on Respondents
Basics on Respondents
Basics on Respondents
Respondents Transport Access
Respondents Travel Habits
Pictures used Plus Neutral
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on General Risk
Opinions on Experienced Self-risk
Opinions on Experienced Self-risk
Risk Considerations Big difference between experienced self-risk and general risk. The impact of pictures are similar, except that the overall effect of the pictures is to lower the score for experienced self-risk. Results indicate a good reason for focusing on the cyclist as an individual and leaving out general references to the risks of cycling.
Opinions on Experience (enjoyment)
Opinions on Experience (enjoyment)
Considerations on Experience Central in marketing. Bicycle and car is surprisingly similar. Accident and helmet pictures raise experience of other modes than cycling. Safety messages may have an adverse effect on bicycle marketing, but promotes car driving.
Opinions on Appearage
Opinions on Appearage
Opinions on Image
Opinions on Image
On Appearance and Image Numerous protests from respondents: Not relevant. Cyclists look better than users of other modes, and the bicycle strengthens respondents image most. Positive impact from helmet picture, while the BMW picture strengthens car image the most.
Opinions on values related to pictures
A few points Notable difference between general risk and experienced self-risk. Focus on the individual in bicycle marketing and omit general references to the risk of cycling. The helmet-picture has a negative marketing effect on cycling, but the picture is associated with the most positive values. Any explanation? A lot (more) of interesting information can be extracted.
(The End)