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Date: oldest first
Simulation gives insight into long-term advertising effects
Robert Stratton, Admap, July/August 2009, Issue 507, pp. 35-37
This article discusses how long-term effects from advertising can be modelled. Customers and markets differ, and aggregate data conceal many varieties of detailed behaviour, so that advertising effects are hard to isolate.
This article discusses how long-term effects from advertising can be modelled. Customers and markets differ, and aggregate data conceal many varieties of detailed behaviour, so that advertising effects are hard to isolate. For building hypotheses of how long-term effects form in particular cases, the author proposes agent-based simulation modelling (described), based on real transaction level data from individual buyers and users. Having access to every transaction a customer makes, along with details of the customer and the usage event, it is possible to bring the behaviours that make up aggregate usage into much sharper focus. All that is needed to achieve this is a database of usage events and an identifier that links the transactions. Two broad types of effect are identified, which combine in various ways: prolonged or delayed recruitment, and continued repeat usage through habit, brand loyalty etc. The shape of the medium- and long term effect can depend a great deal on the interaction between recruitment and repeat usage processes, usage cycle at market level, and on differences between the individuals within the market. The simulation modelling constructs a hypothesis of what a long-term effect would look like (discussed), with which actual sales can be compared.
Model-building for better media deployment
Robert Stratton, Admap, November 2008, Issue 499, pp. 51-53
Agent-based modelling is essential to measure advertising effects in today's environment of fragmented media channels, interactive and social media.
Agent-based modelling is essential to measure advertising effects in today's environment of fragmented media channels, interactive and social media. Traditional causal models will not work with interactive media and social networks, as the effects they provide is simply too complex to grasp from aggregated data. The process of interaction is a multiple probability chain, perhaps combining TV and online influences which are interdependent; this can only be dealt with by using individual-level data. Agent-based modelling works at the individual level, with individuals given "lifestyles" and characteristics based on survey data. The data set is analogous to single-source data, and has been found to correspond exactly to reach and frequency builds in the real world. A simulated social network has also been created to represent word-of-mouth and other interactive behaviours, since the process is so different from that which creates an advertising effect.
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Simulation, agent-based modelling
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