Artificial Intelligence in Marketing: Vol 20

But AI can help you uncover new advertising channels that may not be used by your competition. Every day your team postpones using innovative AI-powered solutions in your content marketing, you’re losing competitive edge. We work with leading AI-first companies that are building the next generation of marketing technologies and services. Most applications of AI still require people to set the parameters to guide their learning and provide governance. Marketing leaders must consider the implications for their teams, particularly in the areas of data and asset management, skills development and capacity planning.

It is also common for firms to purchase third-party data and analysis, especially for external market and competitor analysis. Such analysis tends to be standardized across firms (with limited degree of customization), and thus the insights derived from it are less useful for deriving a unique value proposition. Firms also monitor and analyze first-party data, especially for firm marketing resource analysis and for existing customer analysis for which internal data are available. By contrast, emerging practices automate most of the data collection by connecting technology (e.g., IoT, social networking sites, mobile apps), sensor technology (e.g., remote sensing, detection), and wearable technology (e.g., smart watch, Fitbit).

What is AI (Artificial Intelligence) Marketing?

This paper synthesizes the mounting research areas of AI ethics and AI in marketing. By scrutinizing the validity of ethical principles related to AI in the marketing context and across stakeholders, we show that ethical principles interdepend and collide, partly, in dependence of the stakeholders concerned. Particularly, beneficence and non-maleficence cannot be taken for granted, since the advances of AI applications in marketing are likely to increase individual and aggregate consumption.

In the marketing context, personalization of marketing measures and customer privacy trade off (Rust, 2020). Accuracy of results predicted by AI applications also rests upon the quality and integrity of data. Besides, algorithmic decisions based on (potentially spurious) correlations found in large data sets could be problematic. That can be the case when causality is not established prior to actions, and actions are then directed to individuals, although the knowledge generated concerns populations (Mittelstadt et al., 2016).

Implications for marketing strategy (STP)

Not only does its AI-driven technology help give its customers a seamless shopping experience, but by automatically tracking stock and inventory in real-time, the store’s rolling kiosks provide its marketers with invaluable insights on consumer shopping trends. To date, Ada’s autonomous technology has helped brands including Air Asia, Indigo, and Zoom with their customer experience (CX) as well as social media marketing activities. While AI has been proven to enhance our daily consumer lives—with the adoption of autonomous vehicle technology, fitness devices, voice assistants, and smartphones—it has also served to improve the digital marketing efforts of businesses across many industries. AI marketing refers to all the ways AI technology can help you carry out successful marketing efforts. It mostly comprises the tools that use customer data to generate and deliver the best-targeted messages at the best times possible. But it also encompasses everything from AI creating blog outlines to chatbots that learn from their interactions.

Moreover, in social media campaigns, brands can automatically categorize and analyze images shared by users to measure campaign reach, sentiment, and effectiveness, thus optimizing future marketing efforts. For example, marketers may overly rely on such feeing AI to interact with customers, resulting in customer disengagement. Srinivasan et al. (2016) find that higher levels of customer activity on social media lead to disengagement (i.e., Facebook unlikes). Unlikes, as affective responses, may imply that customer responses may be polarized more easily if technologies (social media in their study) are not able to interact with customers appropriately. This is especially the case for emotional data because such data are about the individual in context, meaning modeling the emotional state of a consumer requires contextual and individual-specific data. For example, in a customer service interaction, the content and sentiment of the conversations are recorded, but not the context of the conversations.

How AI Can Help You Overcome Challenges Across Your Campaign Production Lifecycle

Also, the new AI marketing system boosts the performance of the previously established frameworks. Moreover, the platform is particularly beneficial for tracking digital campaigns and large corporations as it can track mentions in multiple languages, across various locations. It can effortlessly handle the vast amount of data generated by a big business, making it a robust AI tool for marketing.

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