Lessons from Amazon's 4-Star Store, GH Lab, & Fashion Pop-Up Boutique

It’s easy to gloss over the timeworn Amazon headlines, but media coverage of the e-commerce titan’s recent physical retail strategies incited us to dig a little deeper to share some key learnings for you today:

1). First, Amazon’s partnership with Good Housekeeping magazine to create a new pop-up shop and digital boutique, GH Lab, at the Mall of America beckons a unique approach to the physical shopping experience using technology and a thoughtfully crafted, limited assortment.

2). Secondly, the opening of Amazon 4-Star — a new physical store where everything for sale is rated 4 stars and above, is a top seller, or is new and trending on Amazon.com— signals a new level of data-driven curation and transparency that only seemed possible online. 

3). Lastly, the introduction of Amazon’s first fashion-focused pop-up shop in London further affirms the 'brick-to-clicks' phenomenon driving many digitally-native brands to embrace offline despite the 1,773 store closures so far in 2018. 

Amazon has been dabbling in physical retail since its acquisition of Whole Foods in 2017, but its bold experimentation with these new store models clearly reveal the company’s ambitions across multiple physical retail verticals – from fashion to furniture to wellness and more.

These moves signal a future in retail where the power of data drives curated assortments, transparency, and more pleasant, stress-free shopping experiences.

Retailers, take note!

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Topics: brick-and-mortar retail, merchandise planning, merchandise assortment, product assortment, amazon, data, inventory, e-commerce

Why the Biggest Problem with AI is Human

Every brand in retail is talking about AI. We’ve read the reports, the articles, and the survey results about all the different pain points AI can solve for retailers. From all the chatter around groundbreaking innovations and the changing retail industry, brands understand this need for technology in order to adapt. 

And there isn’t a shortage of technology solutions out there either—retailers have a ton of options. 

Understanding this need, the need for tech, then why is it so difficult to actually adopt? Why are retailers still struggling to keep margins afloat and adapt to the digital consumer? 

What’s the biggest problem with AI in retail?

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Topics: inventory optimization, data, advanced analytics, digital transformation, artificial intelligence, machine learning

Why It's So Difficult to Introduce New Products to Your Assortment

“One of the biggest challenges that companies face is predicting demand for new products over time. Overestimate it, and risk warehouses full of excess inventory. Underestimate it, and your customers could leave empty handed—or you might be left with a hefty bill for expedited delivery.”Kellogg School of Management, Northwestern University 

Innovate or die trying. 

The phrase is almost as overused as “retail apocalypse,” yet a concise depiction of what retailers face constantly when we think about the video-gone-viral level of speed behind the changing retail landscape and unpredictable nature of demand. 

As if determining the optimal assortment wasn’t already difficult enough, introducing new products to your strategy when some fashion trends barely last is a risk merchants must take. 

When it comes to new products, retailers are getting burned (more often than not) when a quick pivot or adjustment to changing demand is unmet. Today, these losses hurt big time, especially considering retailers are clenching onto their purse strings more tightly than ever.

Why is it so difficult to accurately predict consumer demand for a new product? And, more importantly, how can we overcome these difficulties? 

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Topics: inventory optimization, data, consumer trends, consumer habits, demand prediction, advanced analytics

What Retailers Can Learn From H&M’s $4B Worth of Unsold Inventory

“In the world of fashion retailing, where shopping is fast moving online and stores try to keep inventories closely matched to sales, even a small stack of unsold clothes can be a bad sign.”  The New York Times 

The news circulating about H&M’s colossal pile of unsold merchandise simply reminds us of the risks fast-fashion retailers face to deliver trends at quicker-than-lightning speed consumers demand.

Although at the unfortunate expense of H&M’s affected profits, here are three invaluable lessons retailers can take away from the big, pile of unsold inventory getting so much press lately:

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Topics: inventory optimization, data, convenience, fast fashion, advanced analytics, speed, accuracy

The Need for Retail Speed and Accuracy in an Ultrafast Fashion World

“Consumers are rabbits in today’s supply chain, and brands, retailers and manufacturers are the too-slow turtles that can’t keep up—despite knowing they need to.” – Spencer Fung, CEO of Li & Fung 

Speed is the name of the game. 

The see-now-want-now mentality isn’t going away anytime soon. While convenience and access continue to drive the consumer desire for immediacy, the gap between customers and retailers expands even more steadily.                       

The number one problem on every top retailer’s mind is this: What can we do to shrink this gap?

“What’s been made very clear in recent years is that the entire supply chain is being disrupted because of consumers’ see-now, want now mentality, because of e-commerce and the convenience it offers, and because of new business models coming from startups that aren’t bogged down by remnants of the old days and ways.” – Sourcing Journal

At the end of the day, leads times cost. The old days of showcasing designs months in advance before delivering to stores are long gone. The speed to market issue is where the problem lies – and slight improvements year over year are not enough at the rate consumers are going today. 

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Topics: inventory optimization, data, convenience, fast fashion, advanced analytics

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