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GUEST COMMENT Ecommerce – it’s only a channel

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It’s time for high street retailers to stop making us versus them comparisons
It’s time for high street retailers to stop making us versus them comparisons

The tremendous growth of ecommerce companies like Amazon and Asos, when contrasted with the simultaneous closings of retail stores on high street, would lead one to believe that physical stores are our past and that digital is the future.

 

While digital is definitely in retail’s future, it’s only part of the future.

 

And high street shopping is far from dead.

 

As much as ecommerce has changed when and how we shop since Amazon was founded 24 years ago, these changes have been evolutionary, not revolutionary. Though digital-first functionality, like Amazon’s product recommendation, which accounts for 35% of what customers’ purchase on Amazon, is certainly revolutionary, there is no reason that a store with its own application or website cannot utilise machine learning technology to provide similar recommendations to shoppers in-store.

 

What retailers need to do is integrate retailing technology which has been the cornerstone of the ecommerce company’s innovations, like Amazon’s product recommendations. An excellent place to start would be with machine learning technology, which enables a retail chain to provide their customers with product recommendations with a higher sale/conversion rate when those customers walk into the store or open their email. By analysing particular shoppers’ buying history and patterns, and matching this information with similar customers, machine learning technologies can make automated product recommendations which can be sent to the tablet of an in-store sales person within seconds of that customer entering the store.

 

These relevant product recommendations enable the salesperson to provide real value to the customer and generate revenue for the store.

 

When these sophisticated machine learning systems are tied to actual store inventory, they can make additional recommendations that will optimize retailer inventory. For example, offering a shopper who already purchased three pairs of Frozen-themed winter pyjamas in size five a 50% discount on the last (and different) Frozen-themed pyjamas in size 5 in the store would make sense. This would save the store storage and shipping costs versus the chance that the retailer would be able to sell those pyjamas in the future.

 

At the heart of an effective product recommendation system lies data. By effectively collecting and analysing data, retailers can use that data for some other successful data-driven marketing initiatives.

 

The first that comes to mind is personalisation. Like with the Frozen-themed pyjamas example above, retailers that have invested in personalisation can improve the in-store experience for their customers by making personalised recommendations. Whether it’s a salesperson suggested an item relevant to an earlier purchase or a cashier offering shoppers a discount based on previous shopping patterns, personalisation provides retailers with an opportunity to increase revenue while making the customer feel appreciated in a way that’s rarely possible for an online-only retailer. Thanks to an effective machine learning-based system, personalisation can be automated, and be used in other channels, too, like email marketing, retargeting, chat/bot conversation and more.

 

It’s time for high street retailers to stop making us versus them comparisons with their digital-only competitors and instead look to integrate the best ideas and technologies from e-commerce companies.

 

At the end of the day, digital sales only amount to 9% of US and 17% of UK retail sales. This means that the overwhelming majority of products are still purchased in physical stores.

By integrating more of the technologies first pioneered in e-commerce, high street retailers with a digital presence can win through a true cross-channel, online/offline experience for their customers.

 

Author: Roei Livneh, chief executive officer at Curve.tech

Image credit: Fotolia

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