AI tools like chatGPT, openAI, and DALL-E have dominated marketers’ news feeds for the past few months. But how much is mere talk compared to the business opportunities that are viable today?
Personally, I see this as just the beginning of a major shift in how digital marketing strategies are implemented across many channels. Further experimentation and technology overlays will bring even more use cases out of woodwork. In a few months or weeks, I hope to look back at this list and consider it narrow and elementary.
But for now, here are seven tangible ways brands can leverage these tools today to improve quality and efficiency on Amazon.
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1. Keyword generation.
The retail media buying team at my agency Acadia started using chatGPT for this purpose. The prompt should read something like “Enter his 10 non-branded keywords for Amazon ppc campaigns related to the product ‘YOUR PRODUCT NAME’.” But don’t stop there. Ask the AI to be more specific, more detailed, or even broader in parameters. ‘Keep it short’ or ‘Use more professional language’ to get more results and more relevant results.
This makes the keyword research stage much more efficient and not relegated to one-off projects. Consumer trends and the competitive landscape change over time, so it’s important to review your keywords regularly.
2. Create a headline for your ad.
Ad types like Amazon’s Sponsored Brands ads rely on merit-driven headlines. Amazon has some requirements for this ad type, such as the copy being less than 50 characters for him. Providing these parameters to the AI provides a great starting point for testing new messages within retail media.
3. Image generation.
AI imagination tools like DALL-E can be used to generate custom images for product pages, brand storefronts, Amazon Posts, and more.
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Russ Dieringer, a retail industry analyst at research firm Stratably, said DALL-E could be the starting point for an image. “It allows you to create things you might not have thought of. Then your team can download the image and make any necessary edits from there.” One is that AI can most effectively represent items and concepts that are already widely represented on the Internet. This means DALL-E is currently best suited for general lifestyle content rather than capturing images of specific products.
There are also editing features that allow you to replace parts of the image, but they are quite limited. Personally, I’m excited to see AI do more powerful image editing in the future. “These he combines three images to create a single hero image that is 3,000 pixels by 1,500 pixels” or ” [X product] Next to case packs” or “Create variations of this image for Amazon Posts” are all struggling to keep up with the sheer amount of content required to keep product and brand content fresh. It’s an amazing shortcut for any brand.
4. GENERATION OF PRODUCT COPIES.
Use AI as an entry point for product content that drives trends and market differentiators. Here are three approaches.
- Ask the AI for trends in product categories, then use the AI as a cue to develop the most relevant points. Another query my team uses is “What are the growth trends in YOUR PRODUCT CATEGORY market?” Mention these trends in your product content and advertising messages as well.
- Address competitor weaknesses. Profasee founder and CEO Chad Rubin used a process to export his Amazon reviews of competitors, have the AI summarize the negative reviews, and then have the AI take the negative reviews. , ask them to write a merit statement for each negative review. These can be used in your own product listings to address concerns of customers shopping in your category. “Potential customers reading negative information about your competitors are compelled to take the ‘add to cart’ action,” says Rubin. He can also use the reverse approach. Use positive reviews to create copy that addresses the attributes of your product that customers like.
- Organize your key marketing messages using various copywriting frameworks that AI knows about. Ask the AI to generate content that uses the “attention-interest-desire-action” framework, the “pre-bridge-post-bridge”, or the “problem-incitement-solution” framework, and provide long-form product descriptions. Make a copy. It really tells your brand story.
What are the downsides?
Relying too much on AI-driven content runs the risk of making everything look the same. For example, if all cat food brands started using the same query to generate headlines for their ads, they might start using the same copy for those ads. A lot of similar-sounding product and brand content can repeat the same trends and customer use cases.
But that’s the point of AI and where brands need to measure and test the effectiveness of their efforts. These prompts should be viewed as starting points, not shortcuts. There has to be someone behind the wheel, reading the dashboard and adjusting the course.
To stand out from the crowd, brands should view AI as a tool in their toolkit, not the creator of their positioning on Amazon.
