3 Practical Reasons Retailers Should Adopt AI in 2019

In the world of retail, the goal is simple: sell product. 

However, reaching that goal is a completely different story. When you throw competition, customer expectations, stores, inventory, logistics, and fulfillment into the mix, things reach a whole new level of complexity. The challenge is really two-fold:

  1. ) First, demand is seemingly unpredictable, with a multitude of factors influencing a consumer’s desire to want to buy something. 
  2. ) Secondly, there’s the issue of optimization – how can retailers leverage their inventory more productively? 

Overcoming these challenges requires retailers to better predict demand and optimize their inventory decisions more effectively to ultimately maximize every opportunity for a sale. Artificial intelligence and advanced analytics technologies are growing to be the tools of choice for retailers when it comes to optimizing inventories as customer expectations force retailers to invest heavily in digital capabilities.

Many retailers, according to Gartner, are “simply not optimizing their most significant assets.” What asset is more important than a retailer’s inventory? Here are three reasons retailers should adopt AI for inventory optimization in 2019:

Read More

Topics: brick-and-mortar retail, merchandise planning, data, inventory, e-commerce, demand prediction, order fulfillment, advanced analytics, artificial intelligence, allocation, markdowns

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. 

Read More

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

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:

Read More

Topics: customer preference, advanced analytics, localization, allocation

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).

Read More

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

Read More

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

Ready for more?

Request a Demo