The Flux Capacitor of Retail

What if you could travel into the future?

In the 1980s sci-fi classic trilogy, Back to the Future, Marty McFly brings back from the future the Sport's Almanac, a compilation of sports statistics and scores, with the intention of gambling his fortune on games with a 100% accuracy. 

What if you could fast-forward to next season? What would you bring?

If I had to guess, as a merchant, you’d probably gather all of the transactions across each store. With that knowledge, you could make the best use of inventory by knowing exactly where each product was going to sell.  

If only it were as easy as time travel. While nothing will be as easy as time travel, it can get pretty close with advanced analytics.

Here's why advanced analytics is the flux capacitor of retail. 

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Topics: brick-and-mortar retail, advanced analytics, machine learning, demand prediction, merchandise buy, merchandise planning, allocation, fulfillment

An Interview with Aldo’s Sr. Director of E-Commerce on Predictive Analytics

If you’re familiar with Celect, then you know how excited we are about our latest press release, which announces our recent partnership with multinational footwear retailer, ALDO Group.

For those of you who haven’t heard—here’s the lowdown on the announcement:

By leveraging predictive analytics, Celect is providing Aldo with unique insights on customer demand to help optimize the fulfillment of online orders across its global network of stores.

Which brings me to my next point:  

Total Retail just released an exclusive interview with ALDO Group's Director of E-commerce Operations, Marc Chretien, to talk about his experience with Celect and Aldo’s omnichannel fulfillment efforts: 

“Customers have become very flexible in their purchasing patterns and have embraced omnichannel, leading them to expect these options from retailers. […] A strategy that ALDO Group has adopted is placing the most possible inventory in-store (vs. distribution centers) where both the walk-in and the digital customer can access it. The Celect technology is a great compliment to that strategy and allows ALDO Group to more accurately predict customers’ demands in real time and, subsequently, determine which stores have inventory opportunities.“Marc Chretien, Sr. Director of E-commerce Operations at ALDO Group

The Q&A provides some additional insights into how Aldo is adapting to the changing consumer and leaning on predictive analytics to make better, faster and more profitable order fulfillment decisions.

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Topics: order fulfillment, fulfillment optimization, fulfillment, ship-from-store, predictive analytics, advanced analytics

The ROI of Optimization: A Case for the Board

In a retailer’s perfect world, knowledge of what your consumer wants to buy (along with when and where they want to buy) would be instantaneous and inventory lead times would be nonexistent.

A retail utopia.

Unfortunately, this isn’t how it works. Instead, merchants are tasked with predicting enormous (sometimes erroneous) investments to: 

  • Determine the optimal assortment/mix of product to sell
  • Determine the optimal store location to allocate each product
  • Determine the optimal fulfillment path for online orders

As a merchandiser, you’re on a perpetual quest to provide the right product your consumer base demands.

So many variables come into play as you try to accomplish this.

Furthermore, it can be very difficult to make the best merchandising decision when the data required to make future decisions isn’t accessible or, best case, spread out across various systems.

As a result, retail professionals globally are looking to invest in advanced analytics optimization technologies. Whether your organization is searching for ways to buy less inventory or improve fulfillment efficiency for online orders—the case for AI-driven inventory optimization is a no-brainer for many once they gain a clear understanding of its ROI.

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Topics: inventory optimization, advanced analytics, data, predictive analytics, merchandise planning, allocation, fulfillment

Solving for Today's Retail Order Fulfillment Dilemma 

The future of retail relies heavily on fulfillment.

Over the past decade, delivery expectations have surged. Two-day and one-day shipping is emerging as the new norm. Retailers continue to move towards shorter delivery times, yet most cannot deliver products profitably within three to five days.

At NRF earlier this year, our CTO, Vivek Farias, made a bold prediction: within the next ten years, two-hour delivery is going to be table stakes.  

Retailers are at a point where the failure to provide options for quick delivery comes with the risk of losing a sale. This shouldn’t come as a surprise for those in the industry, yet looking at the actual stats is always mind-boggling: 

  • About 72% of consumers factor in two-day delivery to help decide whether or not to make an online purchase.
  • About 45% of consumers factor in one to two-hour delivery to help decide whether or not to make an online purchase.

Given this data, maybe Vivek’s prediction isn’t so bold. 

It’s completely reasonable, and retailers need to prepare for deliver on these expectations in order to survive.

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Topics: inventory optimization, omnichannel, ship-from-store, fulfillment, supply chain

3 Retail Technology & Data Trends to Watch for in 2018

There’s no easy way to trulyknow how the future will pan out. We anticipate certain outcomes from what we know—like the fact the Patriots won over five Super Bowl championships or that your women's cold-shoulder sweaters sold 500 units last season.

With this historical data at hand, it would seem logical to place our bets on past performance.

And, as you know, merchants are constantly making bets.

Yet, while placing our bets for another New England Super Bowl win isn’t entirely a gamble (what can I say, we’re Boston-based ¯\_(ツ)_/¯), anticipating consumer demand for the fall or spring season six to eight months in advance is.

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Topics: retail technology, artificial intelligence, fulfillment, consumer trends, e-commerce, online shopping, data

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