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Why Our Partnerships Help Retailers Succeed

Henry Ford once said, that "a customer can have a Model T in any color they want as long as it is black." This worked when there was only one game in town, but when General Motors started offering consumers options, the rules of the game changed. A similar shift is now happening in retail as traditional brick-and-mortar retailers offer a variety of fulfillment options for consumers demanding faster, cheaper, and more convenient delivery options. 

Yet, providing these new fulfillment options successfully, whether it's ship-from-store (SFS) or buy-online-pickup-in-store (BOPUS), requires a new way of thinking about existing processes, organizational frameworks, and technology. To help prospective clients tackle these new retail processes and changes, Celect has partnered with top-tier retail consulting companies across the globe who know change management, retail, and I/T. With these new alliances, our clients have trusted advisors who can help them with the process changes needed to reap the benefits of artificial intelligence and machine learning (AI/ML) technology for successfully tackling inventory and fulfillment challenges plaguing retailers today. 

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Topics: artificial intelligence, inventory optimization, brick-and-mortar retail, physical stores, machine learning

5 Practical Steps to AI Success in Retail

Artificial intelligence and machine learning (AI/ML) technologies are transforming businesses. In fact, the global annual spending on AI by retailers is estimated to reach $7.3 billion by 2022. However, along with this growth comes hype, uncertainty and questions around what AI/ML can really do for retailers today. 

One thing, if anything, is certain: AI/ML in retail is here to stay. 

For those of us who have been in retail a long time know this isn’t the first time data science was used to address retail business challenges. In the early 2000s, data science was applied to common problems within retail merchandising and the supply chain with mixed success. Markdowns were optimized, followed by statistical forecast-driven replenishment, then price and transportation optimization.

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Topics: artificial intelligence, inventory optimization, brick-and-mortar retail, physical stores, machine learning

Key Takeaways from Unified Retailing 2019

The Unified Retailing 2019 Conference hosted by Columbus Consulting yesterday was energizing, as it’s always a fun to meet up with our customers, old colleagues and friends. The speakers were excellent, with frank and interesting conversations around how to win in today’s new retail environment. However, what really struck me throughout the event was the fact that everyoneand I mean everyone—appeared to be dealing with the same type of challenges, such as:

  1. How to support an environment where store sales are dropping, e-commerce is flattening, and mobile continues to grow
  2. How to win in an environment where there are so many store closures, but just as many store openings from digitally-native brands
  3. How to support the shop anywhereexperience consumers demand
  4. How to keep product, experiences and services relevant 

As every speaker discussed different strategies to address each issue, it became apparent over the course of the day these problems haven’t really changed over the last few years. In fact, they’ve only magnified. Despite these struggles, it only solidified my beliefand that of many others in the room—of the following:

If you want to be relevant, you must consider the customer and their preferences first in everything you do.  

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Topics: artificial intelligence, inventory optimization, brick-and-mortar retail, physical stores, machine learning, awards

Lucky Brand's Approach to Successful Merchandise Allocation

The idea behind inventory allocation seems pretty straight-forward: Ensure stores are kept in stock with respect to their ability to sell merchandise. Simple enough, right? Yet, as an allocator, one end-of-season scenario you’re likely familiar with is the aching realization of a missed opportunity — where better inventory allocations in one store could have led to more transactions, fewer markdowns, and improved margins.

Of course, you and I both know the subtle art to successful allocation is far from simple. In fact, a recent U.S. retailer survey estimated inventory misjudgments, including misallocating inventory, account for more than half (53%) of unplanned markdown costs. This issue has prompted retailers, like Lucky Brand, to search for ways to infuse intelligence into their allocation systems and processes.

Mike Relich, COO of Lucky Brand, recently shared his experience leveraging Celect artificial intelligence and machine learning (AI/ML) software to successfully improve retail allocation results. 

Here are the five biggest takeaways:

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Topics: allocation, artificial intelligence, inventory optimization, brick-and-mortar retail, physical stores, machine learning, merchandise assortment

Why I'm Excited for the Future of AI & Retail

I’ve spent most of my career in the retail industry. Throughout that time, I witnessed a great deal of change – from the evolution of the stores’ role, to the seismic shift in how retailers sell within the context of convenience, service and compelling experiences. Most noticeably, the fundamental core of how retailers think has changed: Managing at averages will no longer suffice. 

Despite these ongoing transformations, the one constant—and what, for me, is the most enticing aspect of the industry—is the intimate connection retail has with the consumer. With a background in astrophysics, my passion for science and innovation fuels my desire to help retailers use technology and data to make smarter decisions and preserve this crucial connection.  

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Topics: artificial intelligence, inventory optimization, brick-and-mortar retail, advanced analytics

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