Blog
Benchmarking against competitors is nothing revolutionary. We’ve all been doing it in one form or another for as long as humans have been around – comparing ourselves (or our business) to others.
Though not new, benchmarking is critical to any successful retailer’s process. We look at what’s working for other companies, what’s not, and where there might be whitespace to fill.
Without benchmarking, you’re making decisions without context. However, external context should not be the only factor. What about YOU?
When you stop at market data, you only see part of the story, and therefore make decisions that might not be as profitable as they seem. The real magic happens when you seamlessly integrate your own performance data, and amplify it with additional enterprise data such as inventory and profitability metrics.
Let’s follow Sarah, a Category Manager, as she plans her assortment, revealing how each layer of data dramatically changes her view and ultimately, her outcomes.
Sarah is the Category Manager at a mid-sized fashion retailer. She’s working on building her assortment for the upcoming season, and, like many, turns to market data as her first stop.
With information about competitors, trending styles, and category performance across the industry, Sarah spots an undeniable opportunity: wide-leg trousers.
Confident in her analysis, Sarah allocates a significant portion of the budget to various wide-leg trouser styles, anticipating similar success.
She feels good about leveraging market trends to stay competitive – oh, yikes. Spoke too soon. It turns out her initial optimism might be a bit premature…
What Sarah hadn’t seen when identifying the wide-leg trend was how her own customers were interacting with her existing products and similar styles.
Let’s give her another shot. Having discovered a wide-leg trouser trend, Sarah decides to integrate web analytics to see her performance data.
She asks herself, “How are our current wide-leg trousers actually selling?”
Looking at metrics such as product views, add-to-cart rate, and product conversion for her current bottom-wear categories, she notices that while wide-leg styles are popular elsewhere, her existing similar styles have a surprisingly low add-to-cart rate and high abandonment rate. She also sees a low placed order value per view for these products.
This new lens tells Sarah that while the market trend for wide-leg is strong, her own customers aren’t converting on similar styles on her site as readily as she expected.
Finally, Sarah considers: “Do we have existing inventory? Are customers returning these styles? This is where adding enterprise metrics like inventory and cost/profit bring everything together.
By integrating market, performance, and enterprise data, Sarah gets a truly holistic view.
This complete view allows Sarah to pivot. She applies this multi-layered data approach to find a truly lucrative opportunity: a classic straight-leg pant.
She sees that this style has a strong product profit per unit, a high full-price sell-through rate, and high inventory not viewed — a clear internal opportunity.
When she cross-references with market data, she sees that this classic style maintains consistent demand.
By integrating your own web analytics and enterprise data on top of market data, you move beyond reacting to the market, and gain the power to truly understand your unique customer and optimize operations for maximum profitability.
Don’t stop at benchmarking. Connect your performance and enterprise data with EDITED’s comprehensive market data and get holistic insights in one place to transform your retail game.
