Artificial Intelligence – Enabler or an impediment for Marketers?

‘Marketing’ and ‘ROI’ are two words that are intrinsically linked. Marketing budgets are always under pressure and therefore, marketers are challenged to drive efficiency in targeting the right customer, at the right time, at the right place and with the right message – a task which is certainly easier said than done. With Marketing Technologies (Martech) taking center stage for brands, Artificial Intelligence (AI) – Generative and Predictive, is playing a crucial role in marketing automation to drive efficient communication and customer experience.

In recent times Martech, such as Customer Data Platform (CDP), is transforming the way fast paced organizations are automating their marketing. Such technologies are also helping brands drive customer lifetime value (CLTV). With such technologies taking center stage, it is critical to acknowledge the role of Artificial Intelligence (AI).

AI has multiple use cases across different industry verticals and, in marketing especially, can enable brands to accelerate the delivery of personalized marketing content while adhering to the brand's writing style and tone.

AI has various cases across different industries and verticals enabling marketing teams to drive efficiencies across personalisation, analysing customer sentiments and predicting customer behaviour. AI can assist the brands in three types of use cases: 1) Analysis, 2) Creation and 3) Forecast

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Analysis:

Brands historically work closely with market research companies and spends significant share of their marketing budget on primary and qualitative research via surveys, focus groups and consumer panels. Such exercises can take weeks, if not months. AI is now able to perform such analysis at scale using 1) Image, 2) Text and 3) Video AI

1. Image: there are technology providers that have tens of billions of images with contextual tagging. AI is designed to train and then retrain itself to refine its contextual understanding of images and their significance.
2. Text: we have seen a significant evolution of this technology in recent times. In early days, AI determined certain meaning from particular words. AI has now been evolved with the ability to generate contextual understanding of texts. As an example, a phrase like 'no sweat' could previously be understood as a statement related to hot weather. However, with the current evolution of Text AI, contextual understanding would be applied to this phrase as it refers to something that is ‘not difficult’.
3. Video: similar to Image, Video AI has now the ability to extract semiotics frame by frame by identifying emotions. With the current generation of Video AI, it can also detect objects and places around the world.

AI is now operating on an enormous amount of data gathered over the years. For marketers, AI now is able to create meaningful insights about the brand/product perceptions, customer sentiments by collecting live data about their brands/product(s) at scale. Where traditional market research can be done based on hundreds of people, AI is now able to collect the same feedback at much larger scale and in real-time, rather than waiting weeks or even months to collect such insights. Apart from the accuracy and frequency of such insights, the other significant advantage that this use case is providing is cost in comparison to the traditional research methods.

Using CDP as an example, AI plays a significant role to analyse brand’s first party data. From data integration, enrichment (creating a one-customer-view), or customer segmentation, AI provides a strong platform before CDP engages in customer communication.

Creation:

Ideation is arguably one of the toughest tasks for Marketers. Understanding whether a concept would appeal to the target audience is only done based on small-scale primary research before going into production/execution.

AI is now approaching a stage where it will be able to reliably create brand ideas relating to product, proposition, packaging and even content as per the brand guidelines in relations to the audience it wants to target. Using the same data that has been collected at scale for analysis, AI is able to create a campaign direction once the objectives have been finalised by the brand teams. The creatives that are designed or shot (in the case of video) can also be tested on AI technologies to determine whether they serve the objective before going live. AI is also able to create multiple different types of ads from one creative. Google Machine Learning is great example that is already available free of cost to all marketers where it uses a long-form video for YouTube to create multiple short 6-seconds videos based on its ABCD ad creation principles.

Forecast:

Future customer behaviour can now be determined using Predictive AI. Whether to test a campaign brief or to flag a particular customer whether he/she is about to be a lapsed customer, AI plays a significant role. One particular use case from a brand operating within a fast paced consumer category can be mentioned here: transactional analysis and prediction. Most mainstream brand have significant first party data that AI can analyse to create multiple customer segments. Such customer segmentation can be categorised in hierarchy of CLTV. Of course, in an ideal world all brands would want all of their customers to be in the top bracket. However, the reality is that brands will have a number of different customer segments; for example: high ticket value customers, value customers, lapsed customers and about to be lapsed customers. AI is now able to anticipate customer behaviours, preferences, and potential churn. This enables proactive decision-making and targeted marketing efforts.

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AI (generative or predictive) is not limited to above use cases. With the rate of its evolution that we are currently observing, there will be many amendments and additions to how AI can further transform the way marketers use drive efficient growth of their brands.

Can AI become an impediment to marketing functions? Unlikely; however, it will bring other challenges related to the possibility of certain job roles being negatively impacted, specifically due to the rate at which generative AI is growing. Though, there is also an argument that AI will become an enabler for marketing professionals allowing them to make better and quicker decisions – ultimately the ownership of work and deliverables will need to be placed on a human being, not AI. Furthermore, there is also a belief that AI will free up significant amount of time for marketers, allowing them to focus on analytical and strategic tasks. According to McKinsey , generative AI is poised to transform marketing roles, which will unlock significant revenues for organisations. To ensure AI can have an overall positive impact on marketing operations, the onus is now on organisations to ensure their marketing employees are upskilled to embrace AI.