Our results show initial promise of the value of the AI relationship for those who work in industries that are not TV focused and/or those outside CPG categories. Where the detailed industry data are not available, the prior studies can be used as a starting point to drive some of the right discussions around budgeting (e.g., to what point can we cut spending without affecting market share?). For those who make budgeting decisions in practice we recommend that the AI relationship not be used in isolation to determine a budget, which is consistent with the forecasting literature (given that budgeting is an implicit forecasting decision). The forecasting literature recommends combining forecasts from a number of different simple, evidence-based approaches, each of which has been validated for the given problem (Graefe et al. 2014; Green and Armstrong 2015). A
further complementary approach is to incorporate a profitoptimizing
algorithm based around elasticity knowledge
(Rasmussen 1952; Wright 2009), requiring the use of the
average elasticity (easily derived from a recent meta-analysis)
and a brand-specific gross profit assumption. We
encourage further testing of such approaches across conditions.
Another evidence-based perspective that should be
used to triangulate budgetary recommendations is objective
and task planning (Barnes, Moscove, and Rassouli 1982).
Given that these will give different budget recommendations,
there is a need for further research to better understand
the conditions where they intersect and diverge and
where one method (or combination of methods) may be
more or less appropriate.
Our results show initial promise of the value of the AI relationship for those who work in industries that are not TV focused and/or those outside CPG categories. Where the detailed industry data are not available, the prior studies can be used as a starting point to drive some of the right discussions around budgeting (e.g., to what point can we cut spending without affecting market share?). For those who make budgeting decisions in practice we recommend that the AI relationship not be used in isolation to determine a budget, which is consistent with the forecasting literature (given that budgeting is an implicit forecasting decision). The forecasting literature recommends combining forecasts from a number of different simple, evidence-based approaches, each of which has been validated for the given problem (Graefe et al. 2014; Green and Armstrong 2015). Afurther complementary approach is to incorporate a profitoptimizingalgorithm based around elasticity knowledge(Rasmussen 1952; Wright 2009), requiring the use of theaverage elasticity (easily derived from a recent meta-analysis)and a brand-specific gross profit assumption. Weencourage further testing of such approaches across conditions.Another evidence-based perspective that should beused to triangulate budgetary recommendations is objectiveand task planning (Barnes, Moscove, and Rassouli 1982).Given that these will give different budget recommendations,there is a need for further research to better understandthe conditions where they intersect and diverge andwhere one method (or combination of methods) may bemore or less appropriate.
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