The Challenges of Sequential Allocation

It’s almost that time of the year – Amazon Prime Day’s a-looming. The midsummer, Black Friday equivalent and annual reminder to retailers of one of the most wildly successful loyalty campaigns ever created. 

This four-year-old online shopping “holiday” is also a clear reminder to retailers of this:

It’s time to step up your game.

Beyond the Prime Day phenomenon, Amazon’s success stems from understanding what their customers want. 

In essence, understanding what your customer wants means the ability to offer the right product in the right place at the right time: the ultimate challenge retailers face today.

Perhaps not surprisingly, a huge part of this challenge can be traced back to a retailer’s allocations. You can have the best assortment plan in place, the perfect quantities purchased—and yet, you continue to see unsold product, day after day, that should have sold well at store A or out-of-stock situations at the supposedly-low-demand store D.

It’s not easy to know ahead of time that you should have stocked 30 more pairs of faux fur slide sandals in a size seven at Houston’s Galleria store. While hindsight’s 20/20, it’s also freakin’ costly. 

Here are some of biggest allocation challenges retailers need to quash ASAP:

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Topics: allocation, localization, advanced analytics, customer preference

The Challenges of Clustering

As the seasons change and the weather starts to warm up, I found myself on the hunt for a new summer outfit – ideally a lightweight blouse paired with a matching skirt. While the merchandise I’m looking to buy isn’t necessarily relevant, the situation I found myself in is.

It’s the kind of situation consumers experience waaay too frequently and, frankly, a large reason why providing a stellar customer experience while improving sell-through is such a challenge for many retailers today. 

The story goes like this: 

I came across THE perfect blouse/skirt on a retailer’s e-commerce page. Sizing is always a potential issue so instead of making an online order, I resort to a brick-and-mortar visit.

As I make my way to this retailer’s store, ready and willing to spend, I ultimately find:

  1. The blouse isn’t available at that particular location
  2. The skirt isn’t available in the size or color I wanted
  3. The sales associate was super unhelpful (irrelevant to the point I’m trying to make – but argh—still worth noting) 

Okay, fine. It happens. How was the retailer to know I was going to waltz into their Newbury location looking for product A and product B?

The thing is, businesses “doing retail right” do know – and plan their assortment accordingly. Successful retailers know when it comes to finalizing their assortment plan for the upcoming season, they must rely on truly localized demand to ensure they stock the right product at the right location (and in this particular instance, in the right color and size).

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Topics: allocation, assortment optimization, customer experience, merchandise planning, customer choice, merchandise buy, retail clustering, customer preference

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

Part Four: Q&A with Aéropostale’s SVP of Planning & Allocation (Bonus Questions!)

“Emerging technologies driving disruptive innovation, many yielding transformational benefits, must be prioritized for investigation, even though the ‘buzz’ and expectations for them are just on the rise.” – Gartner, Use Cases Harness Emerging Technologies to Deliver Delightfully Disruptive Customer Experiences 

How are you keeping up with disruption in the industry?

This question resonates with merchants globally—from luxury to fast fashion to off-price—as most are challenged to meet consumer expectations, anticipate future demand, and make the right investments and decisions throughout the merchandising cycle.

Given the circumstances retail is in today, the technology available to help businesses become digitally enabled provider[s] of unified retail commerce is, as Gartner so accurately highlighted, “yielding transformational benefits.”

As an example, retailers are expected to use AI to hone the accuracy and speed of human decision making well beyond current levels by 2020. 

And 2020 is RIGHT around the corner.

As such, we wanted to share some final insights from our recent Q&A with Aéropostale’s SVP of Planning & Allocation, Karen Walter, who shared plenty on how her team is incorporating emerging technologies (like advanced analytics!), as well as:

  • A new approach to the Merchandise, Planning, and Allocation process (Part One)
  • How new technologies are blended with existing business process (Part Two)
  • Why and how Aéropostale adopted analytics in today’s retail environment (Part Three

At the end of the Q&A, we gave retailers a chance to submit their own questions to Karen, which we outlined below! We hope her responses help provide some clarity on any looming questions you may have on Aéropostale's newly AI-enabled merchandising process or strategies you may be considering at the moment.

Also, feel free to access to full webcast Q&A video here for more context! 

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

Part Three: Q&A with Aéropostale’s SVP of Planning & Allocation on AI

"AI, which is forecast to grow to $36.8 billion by 2025, could bring a new way of transformation to retail." - Deborah Weinswig, CEO of Coresight Research   

Everyday you make really difficult inventory decisions. 

The impact of AI on retail is clearly a major topic of discussion for many merchandisers on the hunt for new tech to help make their job of managing inventory and forecasting demand better, faster and much more accurate than ever before. 

At the end of the day, your goals revolve around improving revenue — and yet, so much of it is tied up in unsold product, inefficient processes, or bad merchandising decisions. 

If a technology existed with the sole purpose of helping you meet these goals, why wouldn't you use it? Many retailers are in a dire position because of this failure to adapt and evolve with the tools available to meet the customer-centric demand driving the market today.

And, at the moment, machine learning and advanced analytics are the tools making it possible to do just that. Retailers like Aéropostale are taking steps to make better investments in the right products with their customer in mind by incorporating advanced analytics into their merchandising process.

Check out what Karen Walter, Aéropostale's SVP of Planning & Allocation, has to say in our latest webcast Q&A (transcript below!) about how and why her team adopted analytics in today's retail environment.

(Also, feel free to access Part 1 and Part 2 of the Q&A transcript for more insights from Aéro!)

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

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