Digital marketing: what are the challenges for 2023? – Doxee
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In digital marketing, innovation and adaptation are undoubtedly two essential drivers, especially in the face of the “seismic shocks” that have rocked the industry in recent years. It’s a somewhat permanent revolution that is driven by advances in technology and changing consumer behavior that has created not only opportunities that were previously unthinkable, but also new categories of problems. In this post, we will explore the major digital marketing challenges that are affecting the way companies connect with their target audiences. From the ambiguities of artificial intelligence (AI) that must grapple with the perhaps unsolvable issue of data quality, to the importance of personalization in building customer experiences, to the protection of privacy in an increasingly risky environment, challenges to digital marketing in 2023 and the near future will require innovative solutions and strategic approaches.
There are three areas where practitioners are focusing their attention: engagement, budget optimization, and artificial intelligence. In this post, we will identify the most important challenges that digital marketers are facing and try to give some insight into the tools and techniques that can be employed to overcome these challenges.
Engagement is a top priority and a challenge that marketers in businesses of all industries and sizes cannot escape. The concept of engagement itself continues to evolve as consumer preferences and behaviors change.
The abundance of content and the sheer volume of promotional ads are making it even more difficult to capture and hold the attention of consumers who have now become extremely adept at juggling different channels (which they use and abandon again and again, depending on the timeliness and completeness of the answers they can find in them). To address digital marketing challenges that hinder value generation, marketers must take an omnichannel approach, diversify their strategies across multiple platforms, and deliver a consistent and meaningful brand message at the same time.
While reaching and engaging audiences is becoming more complex every day, at the same time, with the rise of social media, influencer marketing, and increasingly present and articulate digital communication, new opportunities to meet and engage with users are opening up. For example: collaboration with influencers and micro influencers can amplify the ability to penetrate different audiences and, at the same time, foster more authentic connections with target audiences. Interactive tools such as mini sites, apps, and videos offer consumers opportunities for empowerment that were previously impossible.
According to every prediction, improving engagement levels in the immediate future will primarily be the use of data, which will enable a deeper understanding of the preferences of different audiences and the creation of targeted and relevant content for each of them.
Analyzing customer data can generate immense business value: according to the McKinsey Global Institute, organizations that adopt a data-driven approach are 23 times more likely to acquire customers, 6 times more likely to retain them, and 19 times more likely to increase their profitability.
In an era when consumers expect increasingly tailored experiences, personalization and customer experience remain firmly at the top of digital marketers’ agendas. However, achieving effective personalization at scale is by no means a trivial matter. Marketers must address ethical access to and use of first-party data and behave in compliance with privacy regulations (they must comply with GDPR, the General Data Protection Regulation). Balancing the need for personalization with concerns about protecting consumer information is a delicate task that requires transparency and a robust data governance framework.
To address this challenge, marketers must invest in initiatives that are designed to strengthen the bond of trust with their customers by clearly communicating how their data will be used and implementing robust security measures. Leveraging customer consent and providing tangible value through personalized experiences contributes to mutually beneficial relationships. In this context, first-party data is probably the most promising resource.
First-party data (also known as proprietary data) is the data that an organization has collected from its target audience: information from CRM systems or gleaned directly from interactions on various touch points, such as websites, social media, newsletter and email marketing, transaction records, and phone calls.
Since first-party data is essentially raw data, you can choose how it is collected, stored, managed, and protected. By controlling all of these parameters, it’s easier to ensure their accuracy and integrity. First-party data are the exclusive property of the organization that collects them and therefore offer a great competitive advantage.
First-party data is also more relevant and accurate than third-party data because it was voluntarily provided by potential and existing customers. Finally, first-party data complies with new privacy regulations that are making the acquisition of third-party data extremely complicated and risky.
Although more and more organizations are taking data-driven approaches—from predictive systems integration to AI-driven automation—change rarely involves the entire organization, resulting in lower-than-expected productivity and inefficiencies that are difficult to eliminate because they are caused by information silos, for example. In other words: critical business issues are still often taken care of through traditional approaches and therefore could take months or years to resolve.
According to Accenture, by 2025 organizations will be able to automate basic daily tasks and routine decision-making processes. Employees will be able to focus on more typically “human” processes, such as innovation, collaboration, and communication. Therefore, itt seems clear creating truly differentiated experiences for both employees and, more importantly, customers, requires data-driven culture at all levels.
Data-driven marketing has solutions and methodologies at its disposal that allow the entire customer journey to be tracked in real time. Big data makes it possible to capture current trends, predict future behaviors and, based on these, create personalized experiences. Once collected, the data is analyzed to identify buying and consumption patterns and habits. Thanks to new technologies, it is possible to extract useful insights from the often chaotic information flow to guide marketing decisions.
A great way to simplify this process is to use advanced analytics platforms that connect first-, second-, and third-party data and make the results accessible and usable to everyone involved in the project. Machine learning algorithms and artificial intelligence applications are currently being used to automate data analysis and gain useful insights more easily.
As we can easily guess by putting together the observations we have made so far, the knowledge of one’s target audience, which companies gain through data analysis activities, largely influences engagement: more accurate biographical profiles and detailed descriptions of the consumer’s actual emotional landscape (desires, needs, preferences, in a single word: sentiment) are the necessary prerequisite for developing personalized initiatives and more relevant proposals.
Redirecting budgets (even reducing them) and still reaching set goals is a digital marketing challenge that will continue to engage companies for years to come. Economic uncertainties and changing market dynamics can push organizations toward measures that serve to achieve greater savings, for example by cutting spending on marketing activities. At a time when consumer priorities are geared toward basic needs (food and utilities among them), it is understandable that companies want to cut costs. However, this is a short-term strategy that could prove counterproductive.
In fact, companies that continue to invest in their marketing strategies would be more likely to improve profitability in the long run, at least according to the thesis supported by Harvard Business Review. The article, published a few years ago but still relevant today, suggests that in times of uncertainty and crisis, marketing tactics should still be targeted, particularly those that offer a vantage point on the criteria that customers use to re-evaluate priorities, reallocate their budgets, change brands, and redefine the value and thus the usefulness of entire product categories.
Again, the most effective way to address the challenge of optimizing budgets is to leverage data analytics, which allows for objective assessment of campaign results and the performance of individual marketing channels. Investing in marketing technology and automation tools also can streamline processes and reduce manual effort, leading to greater operational efficiency. Adopting a test-and-learn approach, along with a focus on ROI, will enable marketers to identify the most promising initiatives and, as a result, reallocate their resources more efficiently.
For some years now, artificial intelligence has been providing companies with quality information, automating processes and enabling advanced personalization. However, the very rapid pace at which AI advances has given rise to a number of challenges for digital marketing: how to stay up to date with the latest AI developments? How to effectively integrate AI into strategies?
The answer to these questions lies in finding the right balance between human creativity and AI-driven automation.
Marketers should employ artificial intelligence tools to augment their capabilities rather than replace them, for example by using artificial intelligence applications for data analysis, predictive modeling, chatbots for customer service, and personalized recommendations. Once again, this is first and foremost a cultural shift: to take full advantage of the potential of artificial intelligence, digital marketing teams will need to be trained and equipped with the skills necessary not only to understand how it works but to integrate AI systems into their daily activities so they can leverage its technical capabilities to enhance typically human creative qualities.
Leveraging artificial intelligence in marketing requires specialized knowledge: CMOs and business decision makers in general must be willing to invest in continuous learning programs so that digital marketers acquire up-to-date skills. Not only that, they will also need to ensure that artificial intelligence initiatives are fully aligned with overall goals.
In an age when consumers are increasingly concerned about how their data is handled, one of the main challenges concerns privacy. Marketing, through the automation of repetitive tasks, the analysis of large volumes of data, and the creation of large-scale personalized experiences, accumulates a wealth of knowledge, which also includes large amounts of sensitive information.
Companies cannot shirk a responsibility that is regulated by law nor any responsibility that underpins any relationship of trust: as AI-based tools record interactions along consumers’ pathways to purchase, they must ensure that reliable and shared procedures are built to guarantee customer privacy and the responsible use of data.
Another of the challenges for digital marketing concerns data quality. If poor data quality is the largest obstacle to implementing and adopting AI and machine learning , we can understand why organizations expend enormous resources to ensure the quality of their data.
The point is that data quality can always be compromised in a variety of situations: users can enter data incorrectly, a system setting might assign the wrong code to certain actions, or a typo might end up in a script developed to facilitate data transformation. The data quality problem will never be “solved,” regardless of the budget allocated to correcting system failures or the willingness to take definitive action. In an ever-changing business environment, new data quality issues can arise at any time and come from unspecified sources.
However, marketers can use artificial intelligence to resist data contamination, for example, by drawing from new sources; by training teams who research and implement advanced approaches to quality control; by adopting up-to-date, tested, and reliable monitoring and selection features; by spending more time observing the performance of those artificial intelligence models that are able to detect patterns related to misclassifications and errors, fine-tuning algorithms to better handle problematic situations as they emerge.
Creating engagement, optimizing budgets, harnessing the potential of artificial intelligence while always respecting privacy and ensuring the protection of customers’ personal data: For each of these key areas, we have identified the digital marketing challenges by which digital marketers will be measured in the coming months. To address the obstacles we have described, digital marketers will need to adapt technologies and methods to their specific business needs. That is, they will have to master the solutions made available by MarTech. Among them, Doxee’s interactive experience offers exceptional tools that they can use to increase the efficiency and productivity of processes and improve the customer experience through hyper-personalized initiatives.
Copywriter for television and online, she has been creating and managing editorial content for more than 15 years for multiple formats, including marketing and television.
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