CV1-I2-4-MEDIA PLANNING

Author: Prasun A Acharyya

Abstract: Outdoor Advertising media planning has been driven by the location of the billboard, size of billboard and a rough indication of the traffic flow on the incidental road on which the billboard is located. This article

Outdoor advertising is e one of the catalyst for increasing brand awareness, re-positioning brands positively and creating a lasting positive brand image. It reinforces the Brand’s message in the mind of consumers and helps businesses in acquiring consumer mindshare for the business. Many Businesses have harnessed the power of outdoor advertising over the years and have testified of its prowess in significantly achieving their marketing communication goals and objectives.

The daily commute represents the greatest amount of opportunity on most days, with public transit riders being virtually a captive audience. Corporations with big brands and the advertising companies are aware of this and take good advantage of the opportunity to bombard senses of the audience with powerful messages on strategically located signs and billboards.

Media planning is based on two pivots – Reach and Frequency. Reach is the proportion of the target group which is exposed at least once to the communication during the campaign period, and Frequency which is the number of times the Target Group is exposed to the communication. Objective of media planning process revolves around the optimization of, the Reach, Frequency and Opportunity to see, and all this in the constraint of budget, availability of the medium and the duration of the campaign.

The TV ratings system from TAM Media Research/BARC and the readership data from readership surveys NRS/IRS are well known. They provide various metrics required for an effective media planning exercise.

However, the same crucial data may not be completely available for the outdoor medium. There may not be a scientific technique to helped the media professional select his / her bouquet of hoardings and further a lack of comprehensive methodology which provided the media planner to estimate the two mantras of media planning i.e. reach and frequency. Delving into this aspect a simple pragmatic approach to eradicate these lacunae, could be

Stage 1- Gradation-To develop a selection tool that would enable a media planner to assess, grade and evaluate hoardings, thus enabling them to select the best bouquet of billboards/hoardings based on certain parameters.

Stage2- Estimation-Developing a comprehensive methodology which would estimate the number of people who would be exposed to the billboards/hoarding and hence the ad campaign, the profile of the people reached by the billboards/hoardings, number of times people would get exposed to the billboards/hoardings and finally the duplication across the various billboards/hoardings.

Stage 3- The Model- The third and most intricate step of the exercise could be to combine the data obtained through the earlier two steps to arrive at the Reach and Frequency Model for this medium. This aspect from the perspective of reach and frequency calculation of the other media as the audience deliveries is automatically factored with a qualitative assessment of the media vehicle.

To arrive at the set of parameters, a review of the existing literature and practices across the world if put across in a nut shell reveals the factors can be classified into three categories –

• Hoarding characteristics, such as size of the hoarding, angle to the road and so on.
• Road characteristics, such as road speed, road type traffic lights and
• Neighborhood (Catchment) characteristics such as clutter, competition weight and so on.

Various scaling methods such as nominal, ordinal, interval and ratio scales were evaluated. The comparison across most of the methodologies followed shows that the Ordinal scale is widely used for gradation of each parameter.

Let us take a look at a few parameters in detail – Road Type – post observation this parameter was classified into three segments Single Road, Crossroad and T-Junction. The scaling for this parameter was 1-3 where 1 was assigned to a single road while 3 was assigned a T-Junction. This is because a hoarding located at a T junction gets exposed to a higher volume of traffic as compared to a single road.

Let’s examine another parameter – competition weight i.e. the presence of other hoardings nearby. If there is no hoarding in the vicinity the score assigned is higher as compared to the case of other hoardings being present. This is because a solus hoarding has a greater chance of being noticed as compared to a non solus hoarding.

If one had to select only one billboard/hoarding, conventional gutfeel would tell us to choose the one on your right i.e. the red hoarding. Why because the hoarding on the right which is a 60x20 is much bigger than that on the Left which is a 15x15. In other words, the red hoarding would be selected as its surface area is much larger than that of the blue hoarding.

However, if one selects the hoarding based on the visibility scores generated then one would select the Blue hoarding. Listed below the hoardings are the grading parameters along with their assigned scores. The scores for the parameter marked in Blue are similar across both the hoardings. They are road type, visibility, clutter and traffic lights. However, the parameters marked in red are the ones in which the blue hoarding has outscored the red hoarding. They are road speed, competition weight, traffic weight and angle to the road. Let us now analyze why the Blue hoarding has a higher visibility score as compared to its red counterpart.

If one analyses the angle to the road parameter, we see that the blue hoarding scores higher as it is head-on to the traffic passing it and hence the chance of not seeing the hoarding is minimal. However, in the case of the red hoarding it is parallel to the road. Hence it is only visible to that traffic which is looking to the side of the road. Let us take another parameter competition weight. As the blue hoarding is the only hoarding at that site, the probability of it being noticed is much higher as compared to the red hoarding as there is are other hoarding which are competing for the travelers’ attention. Similarly road speed and, Traffic weight also favours the blue hoarding

Let’s now take a look at the second module of our journey i.e. the estimation of the audience being exposed to the hoarding. Traffic Counts is a means of quantitatively assessing the traffic on relevant roads, As the audience for this medium is basically the pedestrians walking on the road and the people who are driving past, we need to separately estimate the number of pedestrians as well as the number of people driving by in any vehicle be it a car, bus or even an auto. As traffic patterns vary across the day it is essential to estimate the traffic at various times of the day, namely early and late morning, noon, late afternoon and evening.

To maximize efficiency and minimize costs each monitored hour is divided into five slots of 10 minutes each. During each 10 minute module only one type of vehicle would be monitored. For example in the first ten minutes cars would be monitored, in the next ten minutes two wheelers and so on. It Is essential that all types of vehicles are monitored in this manner as the number of people in a car would be very different from the number in a bus. This would obviously have a major impact on the final estimate of people exposed to the hoarding.

Let us see how traffic counts help

If we compare two hoardings with similar visibility scores, without traffic counts we would assume that the two hoarding would be seen by the same number of people.

The reason for hoarding X getting a higher traffic score is that hoarding X is exposed to the entire volume of traffic passing over the bridge whereas hoarding Y is exposed to only a proportion of this volume as the traffic bifurcates at the end of the bridge.

reviewed the existing literature across the world and adapted / combined the various algorithms such that we could estimate the reach and frequency using the Visibility score and the Traffic Counts. As you can see from the slide, the algorithm is based on three parameters viz., time period of the campaign M is the Gross OTS and A is a constant which varies across centers and is a function of population and visibility scores.

To illustrate the effectiveness of the tool a comparison of the deliveries of some media plans. As can be seen in the illustrated case, The brand CBD used the tools (mentioned above) selected mere 12 hoardings but, achieved a reach of 84% with each respondent seeing the campaign 28 times on average and this at a cost much lesser than the other brand campaign  during the same period. Hence, the other brand planned their campaign based on ‘Gut feel’ used more number of hoardings, spent more but delivered much lower levels of reach and frequency.

Thus it can be seen that media planning on the basis of the Visibility Score, Traffic counts have led to much more efficient buying. Thus, the impact of planning based on metrics yielding better results.