E-com metrics are not restricted to e-com

I have a passion for great fitting apparel and for over 20 years I have been helping brands fit apparel, understand sizing constructs and globalize fit offerings. 

Most of what I’ve written has addressed the complexities of creating, perfecting and executing fit across a diverse and changing consumer landscape. Today, I would like to dive deeper into e-commerce and, specifically, the quality of rich data one can derive from e-commerce. And then, onto how this data can help physical stores and the reciprocal process of seasonal product creation. 

In my experience retailers and brands with a heavy presence in both e-commerce and physical stores operate these channels in silos - both looking at the past to plan for the future independently. 

Here’s a fact that should be obvious:

The same consumer shops all channels.

We are human and we will take the path of least resistance, be that physical direct to consumer, be that e-commerce, be that partner stores etc. 

E-commerce and physical stores should be viewed as one entity, one representation of your brand with just a different path to purchase / logistical back end processes. 

There are of course differences: the ability to touch, try on at physical stores versus the ability to view / compare, contrast multiple products / brands very quickly online. 

E-commerce has its challenges - apparel fit and product returns rates as a result of fit being a huge one. There is a wealth of rich data that can be extracted from consumer behavior. 

As a comparison, it’s extremely difficult to track the behavior of one consumer in your store. What products do they look at? In what colors? What’s the dwell time per product? What complementing products does that consumer view? What are the patterns looking at this holistically? Then to scale this across all the consumers in your store every day of the week is nigh on impossible. 

However this is the equivalent of views / clicks on e-com. 

How often are heat maps of click versus purchase included in product creation briefs / merchant line plans?

As a former fit engineer / technical designer, a metric I have considered as the holly grail of retail is; what garments are taken into a fit room in what sizes versus what garments and sizes are actually purchased and by whom? E-commerce will now provide this data. 

Again product returns rate are unsustainably high; the more we learn about our consumers’ body shapes, sizes, proportions and therefore fit preferences the better we can serve the consumer. But that’s not, and shouldn’t be, limited to e-commerce. 

One of my former articles, “Why recreate a broken process”, ball parked the hit ratio of the fitting room experience. We are all consumers and are all disappointed more than we are pleased when it comes to the fitting room. This is due to taking the wrong garments, and in the wrong size into the fitting room to start with. Because there is no metric to measure garments in versus those purchased, because there is no metric to measure the pleasantness of the experience (or perhaps we have become accustomed to the fact it’s a trial and error process, based on more error than success) the fit issue has been masked for years. It’s only the continued growth of e-commerce and those product returns rates that are shining a spotlight on the issue. But again it’s not restricted to e-commence. 

Omni channel and how to operate across all channels to purchase, balancing inventory, etc has been a strategy over the last several years. I believe we need to enter the phase of coexistence.

E-commerce playing to its strengths:

• With the ability to story tell in detail about the products, be it aspirational end use or supply chain traceability;
• Individual data tracking, the aforementioned metrics of consumer search, selection, purchase, retain. 

Physical stores playing to their strengths:

• Tactile environment, the ability to touch, feel emotionally connect with the product. 

I believe the successful brands will be the ones looking across both platforms for the obvious then the not so obvious connections, to pay this data forward into future product creation and / or to validate the future products being designed / developed.