In today’s competitive ecommerce environment, understanding how your product range compares to competitors is critical to driving growth. Yet category analysis is often underutilised, largely due to the time and effort required to manually review competitor ranges, pricing structures and product depth.
Tools such as Claude AI are fundamentally changing this. By enabling fast, structured analysis of product datasets, AI allows ecommerce and trading teams to identify gaps, pricing opportunities and areas of competitive advantage in minutes rather than days. The role of the team then shifts from data gathering to decision-making—where the real commercial value lies.
Start with a Clean, Comparable Dataset
The process begins with assembling a simple but structured dataset that includes your product range alongside one to three key competitors. The focus should be on consistency rather than complexity, ensuring the data can be easily compared.
At a minimum, include:
Product name
Price
Category and subcategory
Key attributes (e.g. brand, size, features)
This data can typically be exported from your ecommerce platform and supplemented with competitor data collected manually or via scraping tools.
Use a Clear, Commercial Prompt
The quality of insight generated by Claude AI is directly linked to how the task is framed. A structured, commercially focused prompt ensures the output is actionable rather than generic. For example, you might ask the AI to:
Compare product range depth and coverage
Identify gaps in categories or price points
Highlight over- or under-representation in key areas
Recommend opportunities to improve performance
Sample Prompt: “Analyse the following product datasets comparing my range vs competitors.
Identify gaps in product range, pricing differences, over/under representation in categories, and key opportunities to improve commercial performance. Summarise findings into clear insights and prioritised actions.”
This step is critical in turning raw data into meaningful commercial insight.
Interpret the Output Through a Commercial Lens
Once processed, the AI will typically return a structured summary of insights. These often highlight where your range is misaligned with the market, whether through gaps, duplication or pricing inefficiencies.
Common themes include:
Missing subcategories or insufficient product depth
Overexposure in low-performing or low-margin areas
Gaps in entry-level or premium price bands
Competitor advantages in product variation or attributes
At this stage, the role of the ecommerce leader is to interpret and prioritise these findings based on business context.
Translate Insight into Action
The real value of this approach lies in execution. Insights generated through Claude AI can be directly translated into tangible commercial actions that drive growth.
Typical actions include:
Expanding the range in high-demand or underrepresented categories
Adjusting pricing architecture to improve competitiveness or margin
Refining onsite merchandising to prioritise high-performing products
Enhancing product content and filtering to better match customer intent
In many cases, relatively small changes can unlock significant improvements in conversion and revenue.
Embed into Ongoing Trading Strategy
Rather than treating this as a one-off exercise, leading ecommerce teams are embedding AI-driven analysis into their regular trading rhythm. This allows businesses to remain responsive to competitor changes and continuously optimise their category performance.
Over time, this approach can be used to:
Review multiple categories on a rolling basis
Combine external analysis with internal performance data
Build a prioritised roadmap of growth opportunities
Support more agile and data-driven decision making
Final Thoughts
AI tools like Claude AI are not simply about efficiency—they represent a step change in how ecommerce teams approach analysis and decision-making. By removing the manual effort traditionally associated with category reviews, they enable faster insight generation and more focused execution.