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Is Digital Transformation Bringing Traditional Grocery to Its Expiration Date?

Posted by Alison Crawford on Dec 7, 2017 5:50:30 PM

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Grocery stores, long thought to be immune to the “Amazon Effect”, were shaken in August of 2017 when Amazon acquired Whole Foods. While companies like Instascart and Peapod have provided home delivery for years, this acquisition was like a shot across the bow for many grocers. About 40 million homes are within 5 miles of a Whole Foods, and Amazon plans to use that proximity to change how Americans buy groceries. How will they do that? By leveraging digital technology to go beyond traditional in-store shopping to offer shoppers options that meet personal preferences like click and collect and 2-hour home delivery.

The New Market

Amazon’s plan to be the “sell everything store” is demonstrated by a track record of methodically attacking new verticals. How the early vertical targets like fashion and electronics retailers responded to Amazon’s market entrance is a cautionary tale for what not to do.  Why? When Amazon entered their spaces, many retailers took a “wait and see approach” and were slow to react to the changes in their industry. Years later these department stores are struggling to remain competitive because they are playing catch up with customer expectations and market dynamics dictated by Amazon.

Grocers should be on notice that similar changes are coming. For starters, a recent Food Marketing Institute report says that 20% of global grocery sales will be online by 2025, with American consumers alone spending upwards of $100 billion USD.  Digital transformation is happening in the other 80% of consumer shopping dollars as well. This includes accept and fulfill transactions from physical stores, online and mobile channels, and hybrid models that use multiple concurrent shopping channels like “unified commerce”.  Progressive Grocer says “more than half of U.S. grocers now offer omnichannel services in response to the digital shopping trend” although “only 12.2% have a ‘fully integrated strategy’ for omnichannel retail.”

So, what can grocers learn from those that have gone before?

We’d argue that a great place to start providing a good shopping experience is to anticipate your customers’ purchases by understanding their preferences. Luckily shoppers have become comfortable giving stores information about their likes through loyalty programs, coupon use and mobile shopping lists. The outcome of sharing all this information is data. Lots and lots of data.  By digging into this data, stores learn who is shopping, what products they prefer, how often they buy, how they react to promotions, and how much they are willing to spend in a shopping trip. They are not only better able to personalize the customer experience (leading to increasing transactional value), but can also identify trends that give them a more accurate view of variable demand. Understanding variable demand through data mining and analysis helps predict the right inventory mix to guarantee an item is in stock, creating an effective and efficient supply chain which is the backbone of a high performing company.

Doing that data mining and analysis is not easy. Millions of transactions at stores offerings thousands of SKUs is a lot of data to process.  Throw in variability due to weather, promotions, holidays, optimal fulfillment location, and finding an actionable insight using traditional analytical methods is like “finding a needle in a haystack”.

Luckily machine learning solves this exact problem. This software learns as it analyzes millions of data points, not only “translating” activity into patterns that a grocer can respond to, but also recognizing repeating patterns over time and preemptively offering a plan of action. Ultimately more accurate replenishment decisions can be made by a machine learning engine using historical sales activity and external variables than by long tenured department manager.  In fact, according to a 2016 McKinsey & Company report entitled The Secret to Smarter Fresh-Food Replenishment? Machine Learning, grocers using machine learning achieved outcomes such as an 80% reduction in out-of-stock rates, declines of more than 10% in write-offs and days of inventory on hand, and gross-margin increases of up to 9%.

The Final Check Out

While outcomes like these pay for themselves in reduced waste and increased transaction values, most grocers still need to pay for technology without passing the cost along to their price-sensitive customers. But driving this digital transformation in a growing realization that the grocery vertical has arrived at a tipping point. Shopper behavior and expectations are evolving, competition is getting fiercer, product choice is growing, and the amount of available information is mushrooming. Smart grocers are learning from retailers in other verticals who invested early to align with emerging demands, rather than run the risk of a “wait and see” approach. By proactively preparing their IT environment and supply chain planning capabilities, grocers can put their future in the bag. The only question left will be “paper or plastic”? 


To learn more about how retailers are using machine learning, click below to read the Supply Chain Planning Brief "Machine Learning Steps Up Retail Performance":Machine Learning Sets Up Retail Performance

Topics: Supply Chain Planning, Retail and Multichannel

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