"In the age of information overload, it is refreshing to have a publication whose primary purpose is to serve and educate its readership. A publication that is governed by the people in the industry is always the most compelling and the quality of experience leveraged and information gathered is immediately evident. This journal serves as an industry standard for excellence in reporting on the many facets of our profession, as well as showing where it is going."
Volume 10 (2024-25)
Each volume of Applied Marketing Analytics consists of FOUR 100-page issues, published in print and online. Articles published in Volume 10 include:
Volume 10 Number 1
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Editorial
Simpler is better
Denis Malin, Editorial Board Member -
Practice Papers
The evolution of digital marketing in the era of AI
Kelly Cutler, Business Leader, Author and Educator, Northwestern University
This paper explores the far-reaching impact of artificial intelligence (AI) on the evolving digital marketing landscape. In the digital age, marketing is undergoing a profound transformation led by data privacy and changing technology, with a focus on the benefits and the risks of adopting AI. Increased productivity and the value of AI must be balanced with careful ethical, legal and privacy considerations. Technologies like third party cookies, once the cornerstone of digital advertising, are being retired and replaced. Simultaneously, ad blockers, wielded by users seeking respite from intrusive advertisements, are reshaping the marketing paradigm. Marketers are at a crossroads. Insights and ideas will be explored pertaining to how AI is poised to reshape the way that marketers do business. While emphasising the role of AI in aspects of marketing such as customer engagement, personalisation, marketing automation, content curation, predictive analytics, campaign creation and more, there is also the need for oversight, management and responsible deployment. This can be accomplished by combining automation and technology with human intervention and direction. Careful examination of the potential benefits and the multifaceted risks posed by AI will define how marketers move into a privacy-centric digital future. This paper delves into the rapidly evolving digital marketing ecosystem as it adapts to changing user behaviours, tightening regulations and new technologies.
Keywords: digital marketing; data privacy; transparency; artificial intelligence and marketing; generative AI; AI and digital marketing -
Customising generative AI: Harnessing document retrieval and fine-tuning alternatives for dynamic marketing insights
Dakota Crisp, Senior Manager of Data Science, Jacob Newsted Data Engineer and Data Scientist, Brendon Kirouac, Data Scientist, Danielle Barnes Senior Director of Data Science, Catherine Hayes Senior Director of IT, and Jonathan Prantner Chief Analytics Officer, OneMagnify
This study delves into the transformative impact of leveraging large language models (LLMs) in marketing analytics, particularly emphasising a paradigm shift from fine-tuning models to the strategic application of document retrieval techniques and more. Focusing on innovative methods, such as retrieval augmented generation and low-rank adaptation, the paper explores how marketers can now activate against vast and unstructured datasets, such as call centre transcripts, unlocking valuable insights that were previously overlooked. By harnessing the power of document retrieval and adaptation, marketers can bring their data to life, enabling a more nuanced and adaptive approach to understanding consumer behaviour and preferences. This research contributes to the evolving landscape of applied marketing analytics by demonstrating the efficacy of document retrieval in enhancing the utilisation of LLMs for dynamic and data-driven marketing strategies.
Keywords: generative AI; marketing analytics; call centre; natural language processing; document retrieval techniques; retrieval augmented generation; low-rank adaptation -
Improving voice of the customer analysis with generative AI
Jim Sterne, Applied Technology Evangelist and Thomas H. Davenport, Professor, President’s Distinguished Professor of Information Technology, Babson College
This paper explores the integration of generative artificial intelligence (GenAI) in voice of the customer (VoC) analysis to provide deeper understanding of prospects and customers. GenAI has enormous potential to enhance customer satisfaction, refine products and services and improve the customer experience. This speculative paper illustrates how GenAI can keep pace with increasing customer expectations and the volume of feedback by uncovering nuanced sentiments, trends and customer needs through context comprehension and its conversational query capabilities. The paper explores the power of GenAI in VoC analysis for improving customer satisfaction, accelerating troubleshooting and resolution and upgrading products and services. Additionally, this paper addresses the role of GenAI in advanced communication routing, agent support, multilingual support and sentiment analysis, showcasing its ability to provide comprehensive and context-aware insights.
Keywords: generative AI; GenAI; voice of the customer; VoC; customer satisfaction; sentiment analysis; customer feedback analysis -
The recipe for success in creating frictionless customer journeys
Stephanie Burton, Expert Solution Consultant, Data & Insights, Adobe
Customers now demand seamless experiences across all points of contact. To deliver on this expectation, organisations need a reliable marketing architecture for accurate omnichannel customer journey analysis. This allows them to identify and eliminate friction points that hinder customer satisfaction. However, disparate channels of data often utilise different customer identifiers, creating a challenge in unifying data for comprehensive analysis. By focusing on a strong customer identity strategy and leveraging technological advancements to seamlessly combine data at the individual level, organisations gain an advantage in crafting consistent and frictionless customer experiences. This approach follows a specific recipe, with key steps to identify and remove friction points. Professionals involved in creating or measuring omnichannel experiences will find valuable insights and practical tips within this paper, along with learnings from renowned customer-centric companies like Uber, Netflix and Amazon. Delivering consistently exceptional customer experiences allows companies to command premium prices, build long-term brand loyalty and solidify their reputation as truly customer-focused organisations.
Keywords: customer experience; customer journey; customer identity; frictionless; seamless; omnichannel analysis; cross-channel analysis -
The power of clarity: Understanding how the effective use of data storytelling can improve the decision-making process
Giovanna Fischer, Independent Consultant and Founder, Escola de Insights
This paper discusses the importance of communication in the data field and its potential to improve decision-making processes in corporations. This study adopts an approach intended to understand the current state of the art in the field. The paper proposes a framework for developing data storytelling as a tool that supports data-driven decisions.
Keywords: data; communication; storytelling; business intelligence; data storytelling; data-driven decisions; management -
Strategies to mitigate hallucinations in large language models
Ranjeeta Bhattacharya, Senior Data Scientist, BNY Mellon, AI Hub
In the world of enterprise-level applications, the construction and utilisation of large language models (LLMs) carry a paramount significance, accompanied by the crucial task of mitigating hallucinations. These instances of generating factually inaccurate information pose challenges during both the initial development phase of LLMs and the subsequent refinement process through prompt engineering. This paper delves into a variety of approaches such as retrieval augmented generation, advanced prompting methodologies, harnessing the power of knowledge graphs, construction of entirely new LLMs from scratch etc, aimed at alleviating these challenges. The paper also underscores the indispensable role of human oversight and user education in addressing this evolving issue. As the field continues to evolve, the importance of continuous vigilance and adaptation cannot be overstated, with a focus on refining strategies to effectively combat hallucinations within LLMs.
Keywords: LLM; large language model; hallucination; prompt engineering; RAG -
Research Papers
Predicting maintenance costs of an IT system using AI models
Nathan Bosch, Machine Learning Engineer, Lyft, Emmanuel Okafor, Postdoctoral Researcher, SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals, Marco Vriens, CEO, Kwantum and Lambert Schomaker, Professor in Artificial Intelligence, University of Groningen
Predictive maintenance is a maintenance policy where the goal is to detect potential future maintenance risks in a system so that the maintenance process can be optimised before system faults occur. This paper describes a deep learning model that does not require domain expertise. Deep learning approaches have several benefits over explicit statistical modelling: (1) they require far less domain-specific knowledge; (2) if the underlying data-generating mechanism of assets changes, a deep learning model would only need to be retrained to learn these new changes; (3) they can capture non-linear and complex multidimensional relationships; and (4) they may outperform rule-based or statistical methods. The paper describes how the model predicts maintenance-relevant events, along with the cost of the upcoming event and the time when it will happen. The paper describes the use of a long short-term memory architecture for our deep learning model. By doing so, the cost values represent a real, quantitative value of the potential maintenance costs in the future of an asset. Event, cost and time prediction are all achieved with high accuracy. This allows for the development of maintenance solutions without the initial high degree of domain process knowledge required. The artificial intelligence model can be used to raise an alarm when the cost values exceed some threshold, when the frequency of high-cost events increases significantly over the lifetime of an asset, or when the expected cost exceeds the cost of maintenance.
Keywords: predictive maintenance; deep learning; long short-term memory; LSTM; cost prediction; time prediction -
Navigating compliance and regulations in marketing analytics: Upholding ethical standards and consumer trust
Animesh Kumar Sharma, Research Scholar, and Rahul Sharma, Professor, Lovely Professional University
This paper delves into the multifaceted marketing analytics compliance and regulation landscape across diverse business sectors and legal frameworks. It discusses a spectrum of norms with respect to overseeing data collection, processing and utilisation in marketing endeavours. Stringent global laws govern the handling of personal data, necessitating strict adherence. The paper scrutinises pivotal compliance elements like consent, transparency and data security alongside pivotal legislation like the California Consumer Privacy Act and the General Data Protection Regulation. It assesses the implications for marketing analytics, emphasising rights regarding personal data access, erasure anonymisation methods and ethical data use. Non-compliance repercussions, encompassing legal and financial risks and reputational harm, are highlighted, as many industries are facing distinct regulatory challenges. The paper details the essential components of policies, training, monitoring and enforcement that are crucial to ensuring marketing compliance. It stresses the role of technology, advocating for marketing compliance software to streamline processes, monitor compliance and adapt swiftly to regulatory shifts. It elucidates the collaborative nature needed within marketing teams to achieve effective compliance management. The conclusion highlights how compliance software helps with regulatory updates, data privacy, monitoring and content assessment. This paper emphasises the dynamic nature of marketing analytics compliance, urging vigilance with regard to legislative alterations and technological advancements. The paper provides a comprehensive insight into managing compliance challenges in this evolving field while upholding ethical standards and fostering consumer trust.
Keywords: marketing analytics; compliance and regulation; data protection legislation; non-compliance consequences; data compliance; risk management -
Book Review
Search Marketing: A Strategic Approach to SEO and SEM, by Kelly Cutler
Reviewed by Shashi Bellamkonda, Principal Research Director, Info-Tech Research Group, and Adjunct Professor, Digital Marketing, Georgetown University