Zarnaz Arlia, CMO at Emplifi, says that although AI has been making lives easier for consumers and brands for some time, with new, more accessible developments like OpenAI’s ChatGPT growing in popularity, public awareness of the technology and its potential continues to rise.
Anyone who has worked in marketing will understand the challenge of maintaining content standards and velocity. Automating parts of the process – whether that’s simply to streamline the review process with applications like Grammarly, or to the extent of generating initial copy drafts with ChatGPT – is in many ways a godsend to teams that are already stretched to their max capacity and under pressure to deliver high-quality, consistent content.
Using AI effectively, marketers can easily generate ad copy variations for testing, plan content calendars, and use their newly earned time to invest in other areas where tools such as ChatGPT can’t lend a helping hand – such as brainstorming unique campaign ideas or getting stakeholder buy-in for a new brand positioning.
However, while the uptake of AI tools is booming across the sector, it’s important to remember that AI will not, and should not, replace marketers, and there are still many ‘no-go’ areas where using AI will hinder rather than help. Ultimately, the use of AI should only shift how teams function, creating more time for high-value tasks for which a human touch is vital. So, how can marketing teams leverage this technology to bring radical business benefits and what are the caveats to consider?
Developing content at scale
Automation and generative AI, specifically, have significant potential to affect every layer of digital marketing and work hand-in-hand with traditional marketing tasks. Generative AI, for example, can be used to generate copy and creative assets at scale, such as initial drafts of social copy that incorporate a brand’s tone and its preferred emojis, hashtags or questions. Businesses can also train generative AI to develop meta descriptions, optimise headline copy and summarise product descriptions for their website.
This is possible because AI marketing technologies work based on available data, rules and algorithms designed to perform tasks at scale. For example, Open AI’s GPT-3 framework is a state-of-the-art language-generation model trained on a massive amount of text data from the internet to generate human-like text based on a given prompt. The program can then be trained and calibrated with more information to produce copy in your brand’s voice.
However, within any of these tasks a human component remains essential. While social media copy generated by AI might be a useful starting point to speed up the drafting process, a human touch is vital to ensure the copy is factually correct and feels authentic. After all, AI models are only as accurate as the data they’re built on, which for ChatGPT stops in 2021.
Similarly, AI-generated content has the potential to negatively impact SEO and brand visibility. Without human oversight the content can be repetitive and lack originality, making it difficult for search engine algorithms to understand the relevance and therefore impacting its ranking. So, while generative AI can go a long way in helping to create a first draft of content, marketing teams shouldn’t rely on such technologies to develop content from scratch.
Balancing data privacy and marketing insights
AI monitoring can also be used to blend competitive intelligence, market trends and campaign performance at speeds no analyst can. While performance analysis isn’t simple, the more information an organisation has, the better for your brand – even more so if you have marketers with the bandwidth to act upon this intelligence.
However, we can’t ignore the concerns that have been raised over the security of the datasets that underpin AI models within technologies like ChatGPT. From a marketing point of view, the information put into AI systems can be regurgitated back out, which not only poses copyright issues but is also a threat to customer information. To mitigate privacy and copyright concerns, marketing teams need to train their AI models with strict guidelines, feeding them with data that is legally obtained and compliant with GDPR laws.
The potential use cases for generative AI in marketing teams are clear – from the automation of processes like content development, data collection and campaign analysis to the augmentation of marketing initiatives to create faster operations, improve decision-making and predict performance. Simply put, AI can help to increase speed, efficiency and insights for marketing teams.
However, it’s important to stress that, despite these capabilities, AI isn’t a replacement for people. Instead, it’s a people-enhancer – helping marketers to choose the right social strategy for their campaigns, track audience behaviours, analyse performance and carry out a host of other activities that would otherwise be manual, time-consuming and cumbersome. AI platforms like ChatGPT have the ability to help businesses scale and streamline, and that’s no different for marketing teams.
Ultimately, those that learn to harness the power of emerging AI technologies, embedding privacy measures at the same time, will be those that will succeed in the long term.
By Zarnaz Arlia
CMO
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