How Ecommerce Teams are Using AI

Ever since we first heard of ChatGPT, marketers and ecommerce professionals have been inundated with news of how their woes will be solved. According to emarketer’s “Role of Today’s CMO” report, nearly 60% of organizations worldwide are incorporating GenAI into their marketing and go-to-market strategies. For those in ecommerce, the question is not whether AI will change the game, but how teams can effectively leverage AI to drive better ecommerce performance.  Let’s look at how teams are using AI today to power their ecommerce execution.

Leveraging GenAI for Ecommerce Performance

•  Extracting Data Trends

GenAI excels at summarizing data sets, making it an invaluable tool for  identifying trends in ecommerce data. For instance, it can analyze SEO performance—both onsite and offsite—to highlight where your investments are paying off and where they need improvement. However, to extract meaningful insights, the quality of your data is crucial. Organizations managing their own data are best served by creating purpose-built ‘agents’, or if you are using GPT, specialized GPTs.  These can be fed rules and examples of data interpretation to teach the GenAI context.  These will also be more useful to identify dataset issues.

The key to remember is that GenAI can interpret data it has – so like any data analysis, treat this as the beginning of a root cause analysis.

• Content Development & Optimization

Content development is perhaps the most prevalent use of GenAI in ecommerce, and its importance is only growing. Smaller teams today can use off-the-shelf tools like ChatGPT to generate content quickly, feeding it product information to produce relevant, formatted text.  Until extensive experience is under their belt, and a customized GPT or other GenAI system is built, the output should be considered a draft. In addition to drafting new content, GenAI can be leveraged for the regular content optimization teams must engage in to remain relevant.  

For larger portfolios, even AI support cannot address the content maintenance required to maximize sales performance.  Working with providers to leverage AI for content optimization is critical. Platforms like CommerceIQ’s digital shelf system use AI to analyze metrics such as keyword frequency, share of voice, and market share, recommending content updates to enhance visibility and performance. This capability is evolving towards automated optimization, enabling brands to set parameters and allow the AI to maintain and grow their organic share of voice autonomously.

• Reporting

AI-driven reporting is set to take off in 2024, with systems generating detailed reports and business review presentations for ecommerce accounts across retailers. While human expertise is still needed to derive actionable insights, AI can significantly reduce the time spent on data analysis and report generation. This is particularly beneficial for handling ad hoc requests from senior stakeholders, allowing teams to deliver timely and accurate reports.

To take advantage of this today, you can manually create a specialized GPT to output a regular report.  However, the work may be of questionable value between the human inputs to GPT and the generated output, which will require manual editing.  Customers of CommerceIQ and peer companies, however, can ask what options exist to automate reporting that includes initial insights generation.

Limitations of GenAI in Your Team

• Humans Are Still Smarter

GenAI should be seen as a tool for generating robust first drafts rather than final outputs. Content produced by AI will still require human review and editing. Although AI can identify trends in data, it often lacks the contextual understanding needed to interpret these trends effectively.

• GenAI is Only as Strong as the Data You Feed It

Good data hygiene is essential for maximizing the benefits of GenAI. Poor quality data will yield poor quality outputs. For content generation, merely using brand information is insufficient; keyword traffic and share of voice data are necessary to optimize content for better sales. Additionally, the granularity of AI outputs is limited by the granularity of the input data—category-level data cannot maximize meaningful SKU-level recommendations.

Can Limitations Be Overcome Today?

Advances in AI will continue to address these limitations. AI stacks, such as those developed by CommerceIQ, integrate AI into platforms to enhance output quality and reduce the need for human intervention. These stacks include context engines that refine AI responses based on specific ecommerce needs. For example, an ecommerce context engine can transform a request for a product title into one that is optimized for driving shopper views and improving organic share of voice.

Conclusion

The integration of AI, especially GenAI, is transforming marketing and ecommerce. From extracting data trends to optimizing content and enhancing reporting, AI is a powerful tool that can significantly improve performance. However, the effectiveness of AI is contingent on the quality of data, the application of AI, and the people involved with optimizing AI for your brand. Platforms like CommerceIQ are at the forefront, building AI stacks that save time, drive growth, and protect brands. To stay ahead in the evolving landscape, consider how AI can be strategically implemented in your ecommerce operations.