I have talked abut the ‘Grand Retail Unified Theory‘ in detail and very strongly believe this is where the future of eCommerce lies. So far most eCommerce strategy has been focused on building a stores and jamming traffic through SEM/SEO. This is redundant because it leads to a 4% conversion rate. At best. Back in the day when paid advertising was cheap no one really cared if click-throughs were unqualified, but now, advertising costs have rationalized and the glory days of blanket advertising are numbered.
But this is good because it has re-highlighted leading edge thinkers like Avinash Kaushik and Eric T. Peterson who rightly believe that the true power of the online medium lies in its ability to reveal ‘intent’. What they are saying, in effect, is that the online experience should be a close approximation of customer aspirations.
Another idea that further reinforces the grand retail unified theory is presented below:
Retail supply chain is hugely sophisticated. Most retailers know exactly where a specific product is: store, warehouse, transit etc. So with such high levels of sophistication why not bring some of that magic online. While browsing Macys.com every non first time visitor should be able to see which SKU’s are available at the local Macy’s store. In fact the site should have a feature that allows browsing of only store specific inventory.
Talbots.com attempts this by placing a tag called ‘find this item in a store’ but this is a global tag and does not filter by store.
There has been a lively discussion on the true identity of the online shopper. One of the most prevalent beliefs is that if all the potential shoppers were grouped they could be neatly divided into two groups, one that shopped online and one that didn’t (with little or no overlap). I have a problem with this theory for two reasons: one is that it forces marketers to deploy two different strategies because the assumption is online shoppers have a different set of motivations. The second reason I am in opposition to this dichotomized view is that it’s flat out wrong.
There is significant overlap between online and offline shoppers. I’d go further and say that the ‘overlap’ is the fastest growing segment. I’d go even further and describe this ‘overlapped’ demographic: I believe these customers first shop offline and then (gradually) migrate online. This migration represents a significant strategic opportunity for retailers not only because it’s a more efficient channel but also because it is completely measurable. Therefore, I would invest a big chunk of my budget toward facilitating this migration. Defection at this stage is the most dangerous kind because it represents a permanent change in behavior. Of all the metrics available to retailers the “% catalogers that transitioned online” and “% store shoppers that transitioned online” is the most important.
While trying to find the nearest store location for a footwear company I was given the following options:
Are you selling magic shoes?
Traditional retailers (across every single product category) have done so poorly compared to their younger online cousins that it’s not even worth discussing. The domination is so complete many seem to have stopped trying altogether. But in the retail world ‘real experiences’ still matter and retailers own the ‘real’ part of the equation. Multichannel retailers with online stores need to look at it as a way to both preserve existing customers and attract new ones. The objective is to create widgets that play to the retailer’s strengths. Retailers like Circuit City have features like buy-online-pickup-in-store and and it would be hard for a pure-play online retailer like newegg.com (which is an Internet Retailer top 10 company) to steal a customer accustomed to Circuit City’s innovative widget. This is how all retailers need to think.
Many people buy offline and then do in-store alterations. If this data were captured at the store level it could be presented when customers purchase/browse online. Additionally, integrating offline purchase history will allow browsers to see a list of products they have already bought; both online and offline. This is especially useful when someone is trying to buy a shirt and is not sure if they already have a similar color at home. This feature could also be used to make online accessory recommendations for clothes that were bought offline.