The impact of different touchpoints on brand consideration

Marketers face the challenge of resource allocation across a range of touchpoints. Hence understanding their relative impact is important, but previous research tends to examine brand advertising, retailer touchpoints, word-of-mouth, and traditional earned touchpoints separately. This article presents an approach to understanding the relative impact of multiple touchpoints. It exemplifies this approach with six touchpoint types: brand advertising, retailer advertising, in-store communications, word-of-mouth, peer observation (seeing other customers), and traditional earned media such as editorial. Using the real-time experience tracking (RET) method by which respondents report on touchpoints by contemporaneous text message, the impact of touchpoints on change in brand consideration is studied in four consumer categories: electrical goods, technology products, mobile handsets, and soft drinks. Both touchpoint frequency and touchpoint positivity, the valence of the customer's affective response to the touchpoint, are modeled. While relative touchpoint effects vary somewhat by category, a pooled model suggests the positivity of in-store communication is in general more influential than that of other touchpoints including brand advertising. An almost entirely neglected touchpoint, peer observation, is consistently significant. Overall, findings evidence the relative impact of retailers, social effects and third party endorsement in addition to brand advertising. Touchpoint positivity adds explanatory power to the prediction of change in consideration as compared with touchpoint frequency alone. This suggests the importance of methods that track touchpoint perceptual response as well as frequency, to complement current analytic approaches such as media mix modeling based on media spend or exposure alone

Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2

What can AI do for you?

Simply put, most organizations do not know how to approach the incorporation of AI into their businesses, and few are knowledgeable enough to understand which concepts are applicable to their business models. Doing nothing and waiting is not an option: Mahidar and Davenport (2018) argue that companies that try to play catch-up will ultimately lose to those who invested and began learning early. But how do we bridge the gap between skepticism and adoption? We propose a toolkit, inclusive of people, processes, and technologies, to help companies with discovery and readiness to start their AI journey. Our toolkit will deliver specific and actionable answers to the operative question: What can AI do for you

Road User Charging – Pricing Structures.

This project considers the extent to which the public could cope with complex price or tariff structures such as those that might be considered in the context of a national congestion pricing scheme. The key elements of the brief were: • to review existing studies of road pricing schemes to assess what information and evidence already exists on the key issues; • to identify what can be learned about pricing structures from other transport modes and other industries and in particular what issues and conclusions might be transferable; • to improve the general understanding of the relationship between information and people’s ability to respond; and • to recommend what further research would be most valuable to fill evidence gaps and enable conclusions to be drawn about an effective structure

Design of Multichannel Retailing Pet Shop

Purpose of this paper is to decide the pet shop channel combination that gives the fastest payback period or need less capital. There are 3 channels from preliminary survey. These channels are the physical store (Pet Shop), mobile store (Mobile Pet Salon) and Internet store (E-Pet). A questionnaire was developed to understand customer needs and there are more than 100 dog owners in Surabaya as the respondent, who were selected from some existing pet shops visitor. A total of 100 usable questionnaires were returned. The results are important factors for each channel, which will be a base to prepare the business development from marketing, technical, management and financial factor. Limitations of the study are minimum number of sample and a lack of theory to develop questionnaire. This paper will provides guidance for entrepreneurs who have a plan to running pet shop with multichannel retailing strategies. From all the factors, it was known that all of the combinations were feasible, but if the purpose is using minimum capital, it was better to choose Mobile Pet Salon and E-Pet, eventhough it takes longer pay back period. If the purpose is getting a minimum return period, it is better to choose Pet Shop and Mobile Pet Salon combination, with higher capital needed and shorter discounted pay back