Leveraging Generative AI in your Digital Marketing

If you’re a marketer, you have likely seen a lot around AI recently. ChatGPT has massively accelerated the use of Generative AI.  It reached 100m users in 2 months.  By comparison, it took TikTok 9 months and Instagram 2.5 years to get to the same level. -reference

Sonar has been experimenting with Generative AI in a variety of ways. Mainly, ChatGPT to help create user flow, test scenarios, UX/UI mapping and researching. We also specifically use it as a chrome extension alongside Google for SEO. MidJourney and Dall-E2 for website and social media.  We are trialing using AI within digital performance campaigns and as part of keyword research.

We have also used ChatGPT for ‘fun’ – naming our fish, using it to come up with social ideas for the team, etc.

Digital marketers have found some serious use cases of AI to save a considerable amount of time, and enhance the end product.  

Some examples:

1. Asset creation: Creating content that is both high-quality and unique.  For example, creating bespoke imagery for social media that is still in line with the client style guide.
   
2. Scaling Content: Creating content at scale through generating multiple versions of a marketing message with slight variations.  For example, trialling multiple messages and formats within a performance campaign to see which variant was most effective.
   
3. Dynamic Content Creation: Generating content that is more likely to be engaging and relevant to users (by analysing data on user behaviour, preferences and interests).  For example, feeding in a number of images and correlating with user engagement data to deduce which content is more likely to (a) engage, vs (trigger a sale), etc.
   
4. Enhance personalisation and product recommendations: Similar to above, if we can combine user engagement and product information we can combine data sources to suggest what product might be the best match for the user.

But – it also has a number of drawbacks, largely based on the fact that it creates new data based on existing data.

1. It can create false or misleading information.  In this case, it can be a matter of ‘garbage in, garbage out’.  If the data being used is inaccurate, the output of generative AI will also be poor, misleading, or false.
2. It can be biased.  An example is when there was a prompt of Generative AI to show doctors.  The output was almost entirely white males.
3. It can be vanilla.  There are a variety of posts from copywriters illustrating how Generative AI results are quite bland and ineffective relative to copywriters with more of a grasp of the spoken language.

While it is very early days, it is clear that Generative AI is now a critical component in the digital marketers toolkit – and is a ‘partner’ in any discipline relating to digital marketing.  As a Sonar team member said – “I don’t think Generative AI will be taking our jobs, but digital marketers will need to know how to use it to keep our jobs”.