It can also enable marketers to anticipate customer needs with predictive analytics as well as personalise customer experiences and optimise strategies in real time. As importantly, AI-powered automation allows marketers to streamline mundane and routine tasks and processes, freeing up more time to be strategic. A new generation of generative AI tools is opening up even more possibilities.
1. Digital advertising
Marketers have tapped into predictive modelling, machine learning, and automated targeting capabilities on programmatic platforms like Google and Meta for many years. These features enable you to use real-time data to identify audience segments with a higher propensity to convert, target prospects and customers with highly relevant messaging and creative executions, and test different combinations of creative assets.
The ability to personalise customer engagements at scale is extremely powerful. But to harness this capability, marketers must ensure that these systems are fed with accurate, up-to-the-second data. You also need to be able to produce a variety of creative assets to enable AI to test different ad combinations. If these conditions are met, automated targeting and bidding tactics can free more time for you to be creative and strategic.
2. SEO optimisation
AI can automate a range of search engine optimisation (SEO) tasks, including enhancing headings, titles, meta descriptions and keyword choices to improve page rankings. It can also autogenerate SEO-rich articles, conduct keyword research, and provide real-time analysis of search engine results pages (SERPs). Furthermore, AI can simplify website audits by automatically optimising content and fixing duplicate content.
3. Customer relationship management (CRM)
In CRM, AI automates tasks and reduces human error. AI can be used to analyse behavioural data such as login frequency to predict when a customer is about to leave. Companies can act to address at-risk customers’ concerns and improve customer retention. AI can also create personalised email subject lines and custom-curated articles for each subscriber. Analytics enriched with AI can be used for leads and forecast customer lifetime value.
4. Content creation and design
With the rapid advances in generative AI tools like ChatGPT, DALL·E and Midjourney, marketing teams are using AI to support creative strategy and execution. Gen AI content creation tools can help time-pressured agencies and brands to increase content production and automate repetitive tasks, enabling them to generate high-quality text, graphics and even videos in double quick time.
Such tools are at their best when used to complement human creativity rather than attempt to replace it. They can help you produce content and test audience responses at speed and scale. Such tools are useful for automating aspects of content creation such as background removal, object recognition, image enhancement and tweaking colours and layouts.
You can provide an AI tool with your overarching creative concept and visual identity for a campaign, then allow it to quickly execute millions of different design options. The power of this technology is growing at a rate that is almost frightening, with the latest visual effects and AI deepfake technology capable of generating polished visuals and videos with a prompt.
There are many pitfalls in using Gen AI for content production—so human oversight is still essential. You will want to ensure, for example, that your tools are not using copyrighted material to produce your assets. Furthermore, it’s important to be mindful of issues such as stereotyped depictions of human figures in creative assets as well as quality control issues, like AI’s well-documented struggles to depict people with the correct number of fingers.
5. Marketing strategy and ideation
One powerful way that nearly any team can use AI is to support brainstorming and ideation. Gen AI-powered Chatbots like ChatGPT or a custom GPT can help you to generate new ideas and consider problems and opportunities from different perspectives. You can, for example, prompt the AI to provide ideas about how you could refine a piece of content, novel uses for a product, or new ways to reach your audience.
There are a range of custom GPTs that are designed to support marketing tasks like search, social media and content creation. These tools can deliver better and more tailored results than a broad focused tool like Bard or Chat GPT. If you’re really ambitious, you can even build a custom GPT of your own and train on skills, information, and behaviours that make it more helpful, efficient and effective for your own needs.
For example, you could create a custom GPT preloaded with industry knowledge, brand guidelines, content templates, and analytics to help generate high-quality marketing copy. By continuously updating and refining the knowledge within your custom GPT, you can keep enhancing its relevance and effectiveness. The concept of building a custom GPT can be applied to various industries, not just marketing.
6. Research on consumers and customers
Brands, agencies and market researchers have used AI for a while already to analyse vast quantities of customer data, buying behaviour, preferences, and spending habits, and predict future actions accurately. To achieve accurate predictions, you need to have the right data and capabilities to train the AI tool. This demands an investment in data gathering, data processing, and analytical skills. Gen AI can complement your quantitative research with qualitative insight into consumer motivations and pain points.
7. Customer service chatbots
Companies have experimented with chatbots for a while already. Chatbots are quick and easy to deploy, they’re always on duty, and they can answer queries faster than a contact centre. They can also provide multilingual support and use conversation history to provide a more personalised experience. But they have struggled with more complex queries and with nuances of human emotion and expression. Gen AI can be expected to help companies deploy chatbots that can handle more complex tasks as well as respond to customers with more personalisation and empathy.
As the examples above illustrate, AI has become an essential part of every marketer’s toolbox. But it also brings new challenges to the fore. To make the most of AI, your systems need to be trained on massive amounts of data. That data needs to be up-to-date, consistent, accurate and representative to generate reliable answers and positive outcomes. It must be gathered with consumer consent and in compliance with data privacy laws.
If your data quality is not up to scratch, you could encounter issues such as unfair discrimination against some customers or poorly targeted and ineffective advertising. Most organisations will need to invest in technology and data science skills to get the best outcomes. Transparency is another challenge. You need to be able to explain how your systems reach a decision to ensure accountability.
