Using Customer Information and Bayesian Techniques to Enhance Persistence Modelling

Mark Johnman, Adrian Gepp, Bruce J Vanstone

Research output: Contribution to conferenceAbstractResearchpeer-review


Measuring the effectiveness of media channels and optimizing the resources invested in them is critical to a firm in obtaining a competitive advantage. While persistence modelling is a well-established approach to this problem, the lack of data often available to marketing practitioners to build these models can reduce their performance. This occurs when a model has too many variables to data points, reducing its generalizability, or when important variables are excluded from the model, reducing its accuracy. Previous research shows that it is important for marketing effectiveness models to capture the complexities of the consumer path to purchase, as well as the direct and indirect effects of media channels. However, it is also important for marketing effectiveness models to be generalizable to new data. We firstly establish a baseline by comparing the differences between persistence models with and without consumer activity data (e.g. Facebook post likes, display advertising clicks), which can help create a more accurate picture of the consumer path to purchase and the relationships between media channels. We subsequently investigate how Bayesian persistence models and networks can improve upon normal persistence models, particularly through the inclusion of an informative prior based on customer information. Overall, we contribute to the literature by measuring marketing effectiveness using a modelling approach that handles practical data limitations, while still allowing for the inclusion of consumer activity data. Additionally, our approach replicates how marketers operate in real-life by starting with a prior set of beliefs about media channel effectiveness, based on customer information, and combining this with historical and ongoing marketing data.
Original languageEnglish
Publication statusPublished - Jun 2019
Event41st Annual ISMS Marketing Science Conference - University of Roma Tre, Rome, Rome, Italy
Duration: 20 Jun 201922 Jun 2019
Conference number: 41st


Conference41st Annual ISMS Marketing Science Conference
Abbreviated titleISMS
OtherThe ISMS Marketing Science Conference is an annual event that brings together leading marketing scholars, practitioners, and policymakers with a shared interest in rigorous scientific research on marketing problems. Topics include but are not restricted to branding, segmentation, consumer choice, competition, strategy, advertising, pricing, product, innovation, distribution, retailing, social media, internet marketing, global marketing, marketing & society, big data, mobile targeting analytics, machine learning and algorithm, artificial intelligence, choice models, game theory, structural models, randomized control trials. The ISMS Marketing Science Conference will also feature a dedicated BEHAVIORAL TRACK. The consumer behavior track will feature research in consumer psychology that sheds light on substantial marketing problems. This track will feature a diverse set of approaches and research methodologies that are relevant to the study of consumer psychology, including experimental research, survey research, or conceptual research. The conference begins Thursday morning on June 20, 2019, and closes on Saturday afternoon, June 22, 2019. Multiple concurrent sessions are planned during the conference days. Parallel sessions run from 9:00 AM to 5:30 PM on June 20th to June 22nd. Receptions or dinner will be held in the evening from 8 PM to about 11 PM. Breakfast on June 20th to 22th runs from 8:00 AM to 9:00 AM in the lobby of the University of Roma Tre– Via Silvio D’Amicco 77.
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