7. CAPÍTULO 3 COMPARACIÓN DE LOS PATRONES DE RIQUEZA DE
7.6.2. Valores de riqueza potencial modelada y evaluación de los algoritmos
This chapter has examined the research question and the associated propositions. It has found that that in the aggregate the evidence supported each proposition although there were some deviations. These deviations were thought to be statistical anomalies due to small sample sizes or due to a specific format targeting listeners of a religious persuasion. Nonetheless this study has confirmed the broad patterns of the generalisations, and identified submarkets using duplication analysis. However, when it comes to the fit of the Dirichlet model, methodological issues with diary data collection seem to be introducing a bias that leads to a poor fit. This section will next briefly summarise each proposition and its apparent support.
Proposition 1 claimed that each radio station’s cumulative audience will vary greatly but that the variation would be in line with each station’s market share. This was found to be the case with a very strong correlation for not only ‘All Stations’ but also for the ‘Top Twelve’ stations.
Proposition 2 claimed the average time spent listening would not only vary with market share but would be similar from brand to brand and that there would be a clear DJ effect in line with market share The correlation between market share and average time spent listening for ‘All Stations’ was 0.75, and for the ‘Top Twelve’ Stations the correlation was 0.86, indicating a very good fit confirming the second proposition. As well as proposing that each station’s SCR would be low, Proposition 3 stated that share of category requirement would vary with market share. The correlation between market share and share of category requirement for ‘All Stations’ was 0.79, and for the ‘Top Twelve Stations’ the correlation was 0.89. These correlations support the proposition. It was also shown that the average time spent listening to the radio is typically far higher than the average time spent listening to a particular station thus giving each station a low SCR. Not only did all stations have a low SCR they were also close to the average, with a slight double jeopardy effect. Whilst these low SCR’s are reflective of the fact that the exclusive audience are light listeners, they also highlight that most listeners are multi-station listeners.
Proposition 4 claimed that each radio station would have a low exclusive audience and that those listeners would be light radio consumers. This proposition was supported with ‘All Stations’ having an average exclusive audience of 6.3% and an exclusive audience average time spent listening of 11.8 hours – less than half the average time spent listening for all listeners of 23.7 hours. However, it was noted that the sample sizes for the exclusive audiences were low with an average of just 10 listeners per station. These low sample sizes impacted on the correlations and the deviations were thought accounted for as a sampling variation. Nonetheless, while the correlation between market share and exclusive audience for ‘All Stations’ was 0.31, indicating a poor fit, the correlation for the ‘Top Twelve’ Stations the correlation was 0.83, – indicating a good fit.
Proposition 5 stated that, as with the Duplication of Purchase Law, stations would share their listeners in line with the other station’s penetrations. It was expected that a switching between stations would follow a similar pattern. The correlation between the average duplication and the expected duplication was 0.95, a very good fit. Although the correlations supported the proposition, there was evidence of a possible market partition based on format. In considering the stations, grouped as either music or a talk station, it was shown that music station listeners were more likely to switch to a music station than a talk station demonstrating a partition in the market place. Overall, each of the first five propositions was shown to be supported with strong correlations between each station’s market share and their cumulative audience, average time spent listening and share of category requirement. However, this thesis also proposed that the Dirichlet model of consumer behaviour could be used to describe radio listening behaviour.’ Having found that the market regularities that support the Dirichlet model do apply to radio listening within reasonable parameters, this thesis examined the Dirichlet model’s predictions.
Section 6.5 specifically looked at the Dirichlet’s predictions and whether they were a good fit when compared with the actual observations. Overall there was a strong correlation between the observed and theoretical values for the cumulative audience (r = 0.94), average time spent listening to a station (r = 0.85), share of category requirement (r = 0.89) and duplication of listening (r = 0.86) variables. However,
there was not a good fit with the average time spent listening to the radio (r = 0.51), exclusive audience (r = 0.63) and the exclusive audience’s average time spent listening (r = 0.44). Also, there was a systematic bias in that the theoretical average was lower than the observed values confirming the boundary condition that the Dirichlet under-predicts the purchase rates of solely loyal buyers.
Whilst it cannot be proven conclusively within the context of this study, it appears from the Arbitron and BBM Canada studies that methodological issues may account for the differences. As highlighted in Chapter 5 the BBM Canada study showed that average time spent listening to the radio was over-reported in diary methodology by up to 30% and listener repertoire sizes were under-reported by almost 50%. These findings indicate that both the actual exclusive audience levels and the average time spent listening to the radio might well be closer to the Dirichlet’s predictions than the actual diary observations. This implies that within the New Zealand radio environment where diary methodology is used the Dirichlet’s predictions may be a better benchmark than the observed values as they are not subject to the method bias of the empirical results. This is an area for further research.
Nonetheless, while, in the aggregate, the Dirichlet has provided a reasonable overall fit the question needs to be asked whether the model has any practical application within the New Zealand radio industry. The following chapter looks at how the Dirichlet generalisations can be applied and what the implications appear to be for radio station managers.