Ethan Lewis, CTO at Kochava, explores how generative AI can augment creativity rather than replace it.
Recent research from Digiday has found that 71% of marketers are already accessing the potential of AI for their consumer-facing copy generation. Alongside this, more than half (51%) of marketers are accessing chatbots and AI assistants in their work in some way.
It’s clear that GenAI and adtech go hand in hand. But in its current form, AI is still a long way from replacing the role of a marketer altogether and certainly far from replacing the process of making an ad. Instead, it’s excelling in supporting marketers to achieve better outcomes.
In short, GenAI is a valuable tool that can create outputs informed by all of the unstructured data that advertisers and publishers have. In many cases, it is being integrated into other tools marketers are using to ensure they are receiving actionable outputs that can shape their next campaigns.
For example, Apple reports that 70% of Apple App Store visitors use search to find their next app and there is plenty of data marketers have available to them that they can use to target their next user. GenAI can use data on features, language preferences, regional nuances, and already-established keywords to reveal new and better keywords that will entice your next customer. In more ways than one, it will enable marketers to work more efficiently and help them see the data more clearly, quickly providing creative ideas that are effective.
Application and solution development
Perhaps expectedly, AI is already becoming a handy tool for developers in speeding up processes, and it has been for some time. Leveraging AI for intelligent applications, democratising access to advanced technologies, and adapting to regulatory changes in the app store landscape for example are all ways that AI can help organisations rapidly transform and improve their business.
Going further though, accessing AI-driven insights and recommendations will allow developers to tailor apps to users and advance data-driven decision-making. Tools like Kochava AI prompt for example, uses AI technology to create accessible and digestible data-driven insights using natural and native language processing. This allows marketers to actively engage with their data and better understand it all while it remains secure. In turn, this means end-users are able to rapidly measure the success of their campaigns and gain a better understanding of their performance.
AI can also facilitate newfound levels of hyper-personalisation for audiences and ensure that end-users are receiving ads in their preferred way; a way that is most likely to convert them and meet their needs and wants. Previously, understanding this and being able to act on it has been a long and arduous process. For many developers, the biggest difference AI can make is to reduce the time it takes to structure and tag data for input into models. Now transformers and large language models (LLMs) are making this process easier and more efficient.
All of this will transform the experience for customers, users, product owners, architects, and developers.
AI and creativity
In a recent Harvard Business Review article on AI and creativity, surmised from a real-world experiment on best practices for developing better and more-creative ideas faster, GenAI was found to be best used as a partner in a structured conversation, in order to make sure that it isn’t limiting your team’s creativity.
More broadly speaking, finding the right blend of people and data is crucial. There’s still a lot of work to be done before a marketer’s years of experience and familiarity with their brand can be input into an AI-based model. Brand guidelines can on occasion be concepts that aren’t quite as tangible as an AI model would like and as such, people become crucial.
As GenAI’s presence in marketing grows there will no doubt be stronger calls for the introduction of citations and references in tools like LLMs to emphasize transparency and ensure that the value of data can be verified. Marketers need to ensure that the information they’re relying on from AI tools is accurate and not simply lifted from their competitors or peers. We’ll likely see a shift towards small language models that are built on data sets that marketers have more control over and can be curated.
Where technology can fit into the process however is in making suggestions based on data alone, positing a new viewpoint that is hard to see at first and free of the particular biases that may be influencing an experienced marketer. Above all, remember to take GenAI at face value and trust your experience and instincts too.
What’s next for marketers?
Historically, marketers have needed to understand every last detail about their customers going into an ad campaign, and use that knowledge to their advantage within the constraints of cost and time. Now, AI can do much of the heavy lifting and use the data that you already have to teach you previously unknown things about your customers and uncover what makes them tick.
The recent popularity around AI has also arguably granted permission for marketers to embrace AI to test new models and see how they can improve their existing processes, relying on the newfound trust, awareness and appetite for AI from people at most levels of business across a range of industries.
Moving forward, the biggest concerns for marketers in the near future will be ensuring that there is sufficient time to train new models, assess and validate their outputs, and prove they can meet the needs at hand.
For smaller players in the industry, it’s advisable to leverage the capabilities of partners and experts who can add these efficiencies and tools to your roster for you. For the bigger players, investing in research and development when it comes to AI (including giving internal teams time to explore tools and capabilities) may prove fruitful in the long-run and leave you with a crucial advantage over your competitors.
