PhD Literature Review: AI-Enhanced Content Marketing: A Study of UK Consumers’ Engagement
1. Key Challenges of Consumer Engagement via Content Marketing
Content marketing is presently considered one of the most effective pull-based strategies involving the development of long-term relationships with customers and ensuring superior engagement rates (Petrescu et al., 2022; Petrescu & Krishen, 2023; Raghulan & Jayanthi, 2024). As opposed to push-based methods, it is focused on the continuous provision of value to existing and prospective customers, which increases perceived brand credibility and trustworthiness and creates new leads (Caruelle et al., 2022). With that being said, its effectiveness is limited by such factors as the cost of content creation, the consistency of uploads, the quality of the content, and the ability to effectively personalise content and target it towards the needs and expectations of certain audiences. As noted by Padigar et al. (2022) and Stone et al. (2020), small and medium brands can find it difficult to recruit top-level creators capable of producing unique and captivating materials on a regular basis. At the same time, the problem of content saturation implies that the increasing amount of content uploaded by modern firms makes it progressively more challenging to stand out in terms of quality and brand image (Jarek & Mazurek, 2019). If they fail to produce high-quality materials relevant to their targeted audiences, they can easily get lost in search outputs and social media newsfeeds and fail to reach their prospective audiences.
‘Banner blindness’ and other effects imply that brand-related content marketing has to follow the pull strategy by offering high-value recommendations, educational materials, and other publications that users can apply in their own lives (Hermann, 2021; Jabeen, 2022; Liu-Thompkins et al., 2022). This type of information is more difficult to create than traditional promotional videos and photos focusing on specific products and services. Additionally, such authors as Jeffrey (2021) and Perret and Heitkamp (2021) note that personalised communication targeting specific user groups of individual users requires nuanced metrics, big data analysis, and accurate engagement measurements. Such knowledge is crucial for understanding the changing needs and preferences of consumer audiences and seeing how different content strategies affect their conversion rate, time spent on individual pages, and interaction rates (Zhang & Song, 2022). The absence of such insights substantially reduces the capability of marketers to predict the consequences of their campaigns and develop realistic plans, which may be especially problematic for small and medium companies with limited resources.
Another major challenge related to consumer engagement is associated with audience segmentation. Since content marketing requires a certain degree of personalisation, while push-based promotion elements require paid ad impressions, conversion rates directly rely upon the quality of targeting and personalisation (Balamurugan, 2024; Golab-Andrzejak, 2022; Vlacic et al., 2021). Consumer engagement is closely related to feelings of appeal, interest, and relevance. In this aspect, key audiences have to emotionally respond to the stories told by brands and become intrigued by them (Verma et al., 2021). At the same time, this requires the use of specific topics and ideas relevant to customers within smaller niches. Excessively generic content may not produce an equal response, which means that firms have to consistently produce materials based on their own unique appeal and reflect the tastes of their audiences. From a production standpoint, this may have a two-fold effect on marketing costs (Chintalapati & Pandey, 2022). On the one hand, a smaller niche may be easier to target due to lower cost per click and cost per impression charges for specific keywords. This may be highly beneficial for reducing the expenses associated with brand promotion via Google and popular social media platforms.
On the other hand, such niche positioning raises the expected quality of the content that has to address the tastes of a particular audience (Hollebeek et al., 2021; Lim et al., 2022). In order to be effective, this format of information delivery must clearly reflect customer pains and provide value. From a marketer standpoint, this implies the need to explore the past history of interactions, investigate customer communication patterns, and produce insights and predictions regarding future developments and trends in a particular industry (Huang & Rust, 2021a). Moreover, these activities have to be aligned in terms of schedule to capitalise on the most optimal upload times ensuring the greatest reach possible. This requires the analysis of big data, as well as the application of advanced concepts such as integrated marketing communications across multiple channels (Cheng & Jiang, 2022). For smaller brands, such instruments were generally out of reach until the emergence of artificial intelligence (AI) that can be used as leverage to make them more accessible and affordable to them. Additionally, it can also remove the limitation of scalability, where firms have to employ more workers to handle the growth of their content marketing activities.
This problem overlaps with targeting choices that have to be based on thorough and longitudinal consumer engagement measurements (Chaisatitkul et al., 2024; Haleem et al., 2022; Kumar et al., 2024). If campaigns or advertisements are shown to the wrong audiences, they fail to generate leads and provide good return-on-investment (ROI) figures. The same applies to different time periods reflecting the optimal moments during which the majority of targeted customers across different population groups and/or time zones can consume the published content and engage with it (Kim et al., 2020). Moreover, adaptive adjustments may need to be introduced right in the middle of such marketing activities to better utilise promotional budgets. This flexibility is difficult to achieve without substantial resources, including a highly skilled workforce, big data capabilities, and strong marketing expertise (Agarwal et al., 2021). As discussed further in this thesis, the promise of AI is largely associated with the opportunity of delegating these operations to automated systems ‘doing the heavy lifting’ for small and medium companies while charging the prices they can afford.
2. AI-Enhanced Content Perspectives
The discussion of AI-enhanced content perspectives has to take into account the fact that this sphere is undergoing rapid development at the moment. Due to this consideration, some of the solutions promoted in academic studies may refer to prospective opportunities that may or may not create value in the future, as noted by Ameen et al. (2022) and Pagani et al. (2024). The first and most obvious AI-enhanced technology in this sphere is content generation (Gao et al., 2023). Such platforms as ChatGPT and Midjourney can instantly create text and visual information based on so-called prompts describing the topics, characteristics, and conditions of the required outputs. As noted by Mikalef and Gupta (2021) and. Verma et al. (2021), the quality of such content may still not be sufficient to be effectively used in high-quality content marketing campaigns without its revision by professional content creators. However, such authors as Davenport et al. (2020) and Kshetri et al. (2024) highlight the fact that prompts allow such persons to create semi-finished texts and images that can be improved afterwards with smaller effort in comparison with the creation of such materials from scratch. This allows firms to reduce the number of employees responsible for these activities and only employ a limited number of highly skilled professionals working with AI-generated content and revising it for the needs of specific targeting strategies (Huang & Rust, 2021b).
As noted by Ameen et al. (2021) and Ngai and Wu (2022), such systems are presently used as supporting instruments for several purposes. On the one hand, they allow firms to paraphrase old texts or re-write them in accordance with changing SEO recommendations (Mende et al., 2024). In the case of content dating back multiple years, this may represent a cost-efficient method of revising it for new audiences or making it compatible with best practices in keywords optimisation. The use of AI as leverage reduces the costs associated with this process and helps firms transform their website materials in a short period of time (Mikalef & Gupta, 2021). On the other hand, generated content can be used as a foundation for developing new publications in standardised content marketing plans (Manis & Madhavaram, 2023). While some revisions may be required to make such materials more unique in terms of style, the ease of their production can greatly facilitate promotional processes and make regular uploads more affordable to smaller firms (Liu-Thompkins et al., 2022). The same approach can also be applied to marketing messages sent to prospective customers as newsletters that are largely standardised. As noted by Chaisatitkul et al. (2024), a lot of materials within modern content plans have a certain share of repeatability, which potentially makes them compatible with the use of AI content generation.
Another perspective of AI-enhanced content utilisation is associated with hyper-personalisation trends (Chalmers et al., 2021; Davenport et al., 2020). Modern consumers are generally dissatisfied with pull-based marketing methods, as well as existing solutions based on big data analyses. More specifically, a number of high-profile scandals emerged in the 2020s that were related to ineffective use of background information, incorrect messages using wrong names or gender pronouns, and other mistakes (Kshetri et al., 2024). These problems demonstrate the threats of automated personalisation of marketing communication based on sub-optimal use of user data. While AI promises to substantially improve the quality of such processing instruments, its actual effectiveness in this sphere has not been reliably confirmed at the moment of this study (Masnita et al., 2024). With that being said, it has demonstrated good predictive capabilities suggesting its potential usefulness as a decision-support tool in content marketing. The analysis of past purchase history or past customer interactions occurring across multiple platforms may help practitioners gain deeper insights into the type of materials users expect from them (Eriksson et al., 2020).
From a personalisation standpoint, this may represent a ‘middle-of-the-road’ solution, where offerings reflect the unique needs and preferences of individual users but do not become excessively personalised (Mariani et al., 2022; Mustak et al., 2021; Peltier et al., 2024). The latter implies the degree where mistakes based on incorrect personal data processing become more possible or progress requires the use of personal data that may not be accepted by all users concerned with their privacy (Ameen et al., 2021; Davenport et al., 2020). Content marketing based on the data of individual customers may help marketers incorporate the bits of information demanded by individual users and create greater value and deeper relationships leading to enhanced engagement rates (Kshetri et al., 2024). The use of predictive analytics may also facilitate the development of long-term publication plans utilising unique consumption patterns demonstrated by some specific customer groups (Ngai & Wu, 2022). From an engagement standpoint, this may reflect the use of video content or longer posts during weekends, with shorter content being demonstrated during the days when users have limited time to browse through such content.
With that being said, multiple studies also imply that the use of AI for content personalisation and other content marketing instruments may be problematic from several standpoints (Haleem et al., 2022; Kim et al., 2020; Verma et al., 2021). On the one hand, artificial intelligence systems are not limited by ethical implications adhered to by human specialists. This may lead to improper use of customer data or the abuse of manipulative techniques in content marketing prioritising effectiveness over customer well-being (Kumar et al., 2024). Compliance with best practices in this sphere may be difficult to ensure due to the complexity of AI-enhanced instruments and the relative lack of practical evidence about the way they function and the potential problems associated with their utilisation (Agarwal et al., 2021). On the other hand, hyper-personalisation and other earlier discussed advanced instruments require the collection and processing of large volumes of data (Lim et al., 2022). From a firm standpoint, this creates the need to acquire powerful information analysis systems for in-house operations or the outsourcing of such activities to third parties (Hollebeek et al., 2021). In the first scenario, it is not clear whether substantial additional investments will produce comparable advantages. In the second one, the transfer of customer data to external organisations may be associated with additional compliance and reputational risks.
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