Q&A with the ALDO Group on the ROI of Advanced Analytics (Part 2)

In our latest webcast, Marc Chretien, Sr. Director of eCommerce Operations at the ALDO Group, shared his perspective on how the company approaches order fulfillment and advanced analytics in today's evolving retail landscape. 

Last week, we touched on the first part of the live Q&A, which consisted of insights around: 

  • the potential and complexity of optimizing store inventories for online order fulfillment
  • the unique approach the ALDO Group takes when fulfilling online orders from stores

In this post, we'll share the second half of the discussion to learn:

  • The process the ALDO Group took towards adopting advanced analytics
  • How ALDO Group dramatically increased sales, margins, and conversions with Celect Fulfillment Optimization 

Read on to learn what Marc has to say below: 

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Topics: order fulfillment, fulfillment optimization, cross-channel demand, demand prediction, customer preference, advanced analytics, ecommerce, brick-and-mortar retail

Q&A with the ALDO Group on the Challenges of Online Fulfillment (Part 1)

To thrive in a hyper-competitive market, retailers are turning to advanced analytics and optimization to drive their ship-from-store effectiveness

The ALDO Group, a global fashion retailer, is taking a different approach to omnichannel fulfillment—and realizing some serious benefits leveraging advanced analytics with Celect. 

Todd Harris, Director of Marketing at Celect, sat down a couple weeks ago with Marc Chretien, Sr. Director of eCommerce Operations at the ALDO Group, for a live Q&A to hear his thoughts on:

  • the potential and complexity of optimizing store inventories for online order fulfillment
  • the unique approach the ALDO Group takes when fulfilling online orders from stores

Check out the the first part of the Q&A transcript below, where Marc shares his experience and thoughts on some of the biggest challenges and opportunities the ALDO Group faced as it relates to fulfilling online orders from stores. 

You can (of course) access the full video interview here

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Topics: order fulfillment, fulfillment optimization, cross-channel demand, demand prediction, customer preference, advanced analytics, ecommerce, brick-and-mortar retail

Optimize Omnichannel Fulfillment for the Holiday Season

We’re just a quick block and jingle bell ring away from the holiday season — a high stakes, strategically coordinated shopping season retailers strive to perfect each and every year. 

At this point, inventory assortment plans and buys have been squared away.

However, once the frantic shopping frenzy ensues, the scale of orders coming through (from any and all channels) becomes increasingly difficult to manage. When an e-commerce purchase is made, retailers are bending over backwards to meet customer expectations even at the expense of a more profitable sale.

“[O]nline sales are growing at a respectable rate for many omnichannel retailers in large part because they continue to bear nearly all the associated costs of attracting and accommodating online shoppers, while their store-based sales often languish.” - WWD

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Topics: order fulfillment, fulfillment optimization, cross-channel demand, demand prediction, customer preference, advanced analytics, holiday shopping, ecommerce, holidays

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

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