THE RELATIVE ROLE OF IMPACT AND RESONANCE IN DETERMINING THE EFFECTIVENESS OF RADIO ADVERTISING. Steen Lundsteen, Copenhagen Business School, Denmark

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1 THE RELATIVE ROLE OF IMPACT AND RESONANCE IN DETERMINING THE EFFECTIVENESS OF RADIO ADVERTISING Steen Lundsteen, Copenhagen Business School, Denmark Flemming Hansen, Copenhagen Business School, Denmark Lars Grønholdt, Copenhagen Business School, Denmark ABSTRACT Data from 160 radio campaign tracking studies are available. With these the relative importance of impact (recall and recognition) and resonance (liking, emotions) are analysed. Also general models for the prediction of radio recall from data on spending and campaign liking are developed. Similarly a model that with high accuracy predicts purchase intention following campaign exposure is shown. INTRODUCTION The amount of research published concerned with the effectiveness of radio advertising is accumulating rapidly. In 2002 Radio Add Effectiveness Lab (RAEL) reviewed 47 studies published since The bulk of this material is from 1990 or later. In 2005 Roderick White summarises results from research published until then and in 2004 James Peacock reviews findings from RAEL project later than those studies included in the original RAEL project (2002). The reviewed studies fall into five categories: 1. Audience research (GRP, adspend, etc.) 2. Level of attention (or impact) 3. Categorisation of content 4. Resonance and 5. Sales influence in terms of purchase intention or actual sales In the present study we shall focus on the effectiveness of radio advertising as a function of spending, impact and resonance. Resonance has been studied partly by looking at the overall liking of the radio spot, partly by having specific aspects of the radio spot evaluated by respondents. Janssens and Pelsmacker (2005) look at four such factors: Emotional responses, understanding, information value, and attitude towards the brand. Other more specific factors studied are humour, eroticism and fear appeal. In a resent study Domke and Wild (2005) concern themselves with the relative importance of the evaluation of content (resonance) and the level of attention (impact). In the present study we shall try and look at radio campaign effectiveness as a function of spending, impact and resonance. THE PRESENT STUDY We have available campaign effect measures for approximately 160 campaigns run on leading Danish radio advertising channels in 2005 and the first half of 2006 (Figure 1). Figure 1 here 1

2 Data collection is carried out with a sample of internet users age The sample is 500 weekly interviews. The general design calls for measurement before, in campaign weeks, and in the week after the campaign. For various reasons different advertisers have not wanted all measurements carried out. Adspend is registered weekly for most of the brands. Impact is measured as general advertising recall with the question: Do you remember having seen or heard advertising for XX within the last month? Radio recall is measured by asking: Do you remember having heard radio advertisements for XX within the last month? Spot recognition is measured after the campaign has started by playing one or more of spots from the campaign for the respondent and asking if he/she recognises having heard this or similar spots on the radio within the last month. Resonance is measured partly as spot liking on a 5-point-scale together with the spot recognition. Emotions are measured by asking about which of 10 feeling words the respondent think apply in connection with the spot he has just heard. (Not reported here). Effect is measured on a scale from 1 to 5 with the question After having heard the spot for XX would you say you are more likely or less likely to purchase the brand advertised? Spending Data FINDINGS With the use of the conversion of GRP data it has been possible to arrive at a sensible estimate of campaign spending for practically all of the campaigns tested. We can subsequently relate these measures to the different effect measures. Impact Measures While spot recognition on the average is 26%, general advertising recognition is 21% and specific radio recall 10%. The relationships between spot recognition and radio recall, spot recognition and general recall, and general recall and radio recall are shown in the below three linear equations, where N represents number of observations. (I) Radio recall = General recall (sign. = 0.000, 0.000, R 2 = 0.554, N = 483) (II) Spot recognition = General recall (sign. = 0.000, 0.000, R 2 = 0.318, N = 394) (III) Spot recognition = Radio recall (sign. = 0.000, 0.000, R 2 = 0.224, N =373) All three relationships are highly significant the closest being the one between general recall and radio recall. The different number of observations here and in the following cases reflects the extent to which the measurements have been available for the particular calculation in the relationship studied. Below the analysis of the relationship between adspending and the impact measures are shown. Adspend is here, as in the rest of the paper adspend in DKK 100,000. (IV) Change in general recall = Adspend (sign. = 0.000, 0.000, R 2 = 0.175, N = 137) (V) Change in radio recall = Adspend (sign. = 0.000, 0.001, R 2 = 0.277, N = 134) (VI) Spot recall = Adspend (sign. = 0.000, 0.000, R 2 = 0.091, N = 274) Again all three relationships are significant with the highest coefficient of determination found between spending and radio recall. Similarly the relationship between spending and changes in radio recall is highly significant regardless of whether we compare first week with last week or first week to any week later in the campaign. Thus in our further analysis of the 2

3 effects of radio advertising we have chosen the radio recall as our primary measure of impact. Alternatively one could have used an average of the three measures obtained but that would reduce the number of observations available for further analysis, somewhat. Resonance As measurement of resonance we have adliking available. Average adliking scores increase as the campaign progress. The largest increase occurs in the first week. In the following five weeks liking still increases, but less. Saturation effects in terms of decreasing adliking are not observed at the average level with the length of the campaign studied here, although this can be identified in individual cases. Ad liking relates significantly to radio recall. (VIII) Radio recall = Adliking (sig. = 0.000, 0.132, R 2 = 0.041, N = 373) Purchase Intention The average self-rated purchase intention can be compared from the first week of the campaign to the last week of the campaign or between first week and the week after the campaign (Figure 2). Figure 2 here In both cases we find a small but highly significant increase in self-rated purchase intention. Whether the comparisons are made in the last week of the campaign or the week after the campaign, seems to have little importance. That impact relates to the self-rated change in purchase intention is evident from the following. (X) Purchase intention = General recall (β = 0.164, sig. = 0.001, 0.000, R 2 = 0.027, N = 394) (XI) Purchase intention = Radio recall (β = 0.154, sig. = 0.003, 0.000, R 2 = 0.024, N = 372) (XII) Purchase intention = Spot recognition (β = 0.267, sig. = 0.000, 0.000, R 2 = 0.071, N = 392) Though the coefficients of determination (R 2 ) are small, all three impact measures relate significantly to purchase intention. The beta parameters in all three equations are of the same magnitude, but the greatest is for spot recognition. Not surprisingly since this is the most sensitive to campaign variations. Also adliking relates to self-rated buying intention as it appears from the equation below. (XIII) Purchase intention = Adliking (sig. = 0.000, R 2 = 0.284, N = 394) Modelling Impact Impact measured as recall or recognition is a central variable in many advertisers planning process and plays a crucial role in their tracking of the effect of their advertising. It can therefore be of interest, to look at how impact depends upon adspend. (XIV) Radio recall = Radio recall before Adspend (sig. = 0.000, 0.000, 0.015; R 2 = 0.621, N = 195) In the regression analysis listed above, as well as in all analysis below, it was necessary to test for any auto-correllation as they are Koyck-type models. No problem with autocorrelation was found in any of the analysis. Likewise different attempts to find any wear-out effects 3

4 were tried, but no option gave a better fit than the linear one. Most likely the number of campaigns needs to be higher in order to find better non-linear relationships. When adliking is introduced in the analysis the result is shown in the equation below. (XV) Radio recall week = Other media recall before campaign Adspend Adliking week Radio recall before (sig.=0.019, 0.000, 0.003, 0.000, 0.012, R 2 = 0.653, N = 114) In this equation the base level of recall is picked up by the other media recall before ( Other media recall = General recall radio recall). This explanation is better than the one on radio recall and spending alone. Looking at the beta values it is also evident that spending (beta = 0.408) contributes much more to radio recall than liking (beta = 0.182). An observation explaining the popularity of ad recall in campaign evaluation conducted by media agencies, since ad recall is highly sensitive to spending. Modelling Self-Rated Change in Purchase Intention That impact influences changes in purchase intention is evident from (X) and (XII). It is also possible to look at the purchase intention in the last week as a function of adspend prior to that week together with adliking. The equation for this relationship is as shown in (XVI). 1 (XVI) Purchase intention last week = Adliking week Adspend after week (sig. = 0.000, 0.083, 0.720, R 2 = 0.359, N =37) It is remarkable to see that we get a coefficient of determination as high as of when explaining purchase intention with resonance and spending data alone. Of course purchase intention is most likely influenced by purchase intention prior to the start of the campaign. It is possible to analyse this also. Looking only in purchase intention in the first week of the campaign, adspend, adliking we get the following equation. (XVII) Purchase intention week = Purchase intention week Adspend after week Adliking week (sig. = 0.000, 0.037, 0.000, 0.099, R 2 = 0.911, N = 60) This model gives a remarkably good estimate of the change in purchase intention in any week of the campaign. If we only look at purchase intention in the last week of the campaign we can improve the model somewhat as shown in equation (XVIII). (XVIII) Purchase intention last week = Purchase intention week Adspend after week Adliking last week Other media recall (sig. = 0.000, 0.017, 0.055, 0.093, 0.279, R 2 = 0.982, N = 14) This model gives an even better estimate than the first. In both equations (XVII and XVIII) adliking has about the same effect on the purchase intention as the amount spend (XVII beta values: Adspend 0.113, Adliking 0.092; XVIII beta values: Adspend 0.137, Adliking 0.118), suggesting that whereas adspend has the strongest effect on impact its influence on purchase 1 As a variable here adliking in the first week is used. One could argue for using average adliking in all weeks prior to the purchase intention being explained, adliking does increase in the course of the campaign. Since this increase is relatively smaller than compared to the overall level of the adliking score it does however make little difference to introduce this more complicated adliking score. 4

5 intention is relatively small as compared with that of resonance (XVII and XVIII). Taken alone (Equation XVI) adliking has an even stronger influence than spending. CONCLUSIONS AND FUTURE RESEARCH QUESTIONS The findings from the present study strongly confirms the observations reported in previous research that impact and resonance are two factors of major importance for the effect of the campaigns in terms of generating sales (or here self-rated change in purchase intention). It is also evident that analysed with impact as dependent variable spending comes up as more important than adliking. However, when it comes to purchase intention, liking is as important as spending. Taken together the estimation of the combined effects of the different factors makes it possible for advertisers to estimate radio recall and expected changes in purchase intention scores based on different levels of initial purchase intention score, liking and spending. As it is evident from the discussion of the different cases analysed, the variables involved are interrelated in several ways. A structured equation model for the variables could look as illustrated in Figure 3. Figure 3 here The limited number of emotional responses available so far does not make it possible to estimate this model from the present data. However, as data collection is continued in six to twelve months, we should be able to do so with from cases. Also improvements in the modelling should result simply from concentrating on data starting by early Different experiences with the data collection were gained during the first year (2005) resulting in a modified design in the year starting Still as of now we find the results exciting and think they confirm that it is possible to model advertising impact and purchase intention effects meaningfully using data on spending, resonance and past purchase intention. REFERENCES Domke, U. & Wild, C. (2005): Radio Advertising Effectiveness, Admap December 2005, Issue 467 Hansen, F. & Hansen, L. Y. (2001): The Nature of Central and Peripheral Information Processing, Research paper, Copenhagen Business School, Department of Marketing Janssens, X. & Pelsmacker, X. (2005): Emotional or Informative? Creative or Boring? The Effectiveness of Different Types of Radio Commercial, International Journal of Advertising, Vol. 24, No. 3 Peacock, J. (2004): RAEL: A Unique Approach To Radio Research, Admap, December 2004, Issue 456 Radio Ad Effectiveness Lab (RAEL) (2002): Research Compendium, Radio Ad Effectiveness Lab Inc., New York, USA White, R. (2005): How To Make The Most Of Radio, Admap, January 2005, Issue 457 5

6 Figure 1: Data overview Ads Attention Communication quality Effect GRP (Radio 100 / DDR) General recall Adliking Purchase intention Adspend Radio recall Emotions (only 2006) Spot types Spot recognition Figure 2: Average purchase intention 2,53 2,52 2,51 Purchase intention (%) 2,523 2,522 2,5 2,498 2,49 2,48 First week Last week 1 week after Figure 3: A possible structural equation model for radio campaign effects Attitude towards spot (irritating) Liking Positive emotion Emotions Resonance Negative emotion Spending Purchase intention Other media recall Radio recall Impact Radio recognition 6