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June 1, 2018

How Brick and Mortar Retailers Can Avoid Getting Amazoned With Big Data

Ami Gal

(Jeramey Lende/Shutterstock)

It’s a merger that touches the lives of every one of us in some way.

Amazon’s deal to buy Whole Foods captured the imagination of the financial world as well as a global audience. The online retailing juggernaut that everyone has come to rely on snapped up one of the biggest brick-and-mortar grocery chains in the U.S.

Beyond this high profile tie-up, the trend of mega-mergers has soared with impressive momentum. Just before the end of 2017, drugstore chain CVS agreed to buy medical insurer Aetna for $69 billion. In February, Albertsons Cos., the second largest supermarket retailer in the U.S., and Rite Aid, the third largest pharmacy chain, announced plans for a merger that will create a company with $83 billion in sales. The list goes on.

What’s driving the recent trend of mega mergers? Industry analysts have offered several explanations.

But perhaps one of the biggest catalysts powering many of these massive buyouts is actually the disruptive power of technology and how it’s rippling out to industries outside the traditional tech universe. Tech companies are buying traditional brick-and-mortar outfits and vice versa.

Don’t Get Amazoned

These days, traditional companies are searching for strategies, tools, and equipment that will help them more effectively transform themselves into tech-savvy outfits. They don’t want to get “Amazoned” — rendered irrelevant by the tsunami of e-commerce and online retail players. With the help of an effective big data strategy, that doesn’t have to be the fate of traditional enterprises. This is especially true for retailers and restaurant chains.

Businesses are generating more data than ever. As they migrate more of their operations onto the internet, they need to keep up with an exploding amount of information. Effectively harnessing these reams of data for business analysis and insights could prove the difference between success and failure for a business.

Brick and mortars can take steps to avoid losing business to Amazon (Monkey Business Images/Shutterstock)

When it comes to retailers, an effective big data strategy is necessary to maintain the same customer experience and preserve their loyalty. In the case of a merger, business applications and databases need to be harmonized and integrated as fast as possible. One can imagine the enormous job of fusing the data operations of Amazon and Whole Foods.

Two years before being bought by Amazon, Whole Food’s Chief Information officer said that “Retail is a really data-intensive industry” and that the proper big data solution would give the supermarket “granular” details for big decisions.

Indeed, when the Amazon-Whole Foods deal was announced, many observers speculated that the driving force behind the purchase of the upmarket grocer was actually Amazon’s thirst for Whole Foods’ data. Information from grocery stores about shopper habits filled in a gap of Amazon’s retail know how. The shopping giant will be able to combine its big data infrastructure with Whole Foods’ vast store of information about buying patterns.

Amazon already had a long track record of big data success when it purchased Whole Foods, but for most companies, the data isn’t enough by itself. Companies have to figure out exactly how they will use the information to improve their businesses.

The proper big data setup can surface insights about customer behavior that enables businesses to better understand the most lucrative parts of their business, and what is causing the loss of business. They can also deploy this information to better tailor sales promotions to potential customers.

Indeed, a recent survey by IBM found that 62 percent of retailers believed that the use of information and big data gave them a competitive advantage for their organizations. Conversely, companies that don’t effectively harness their big data are at risk of being rendered irrelevant.

(Andrey Burstein/Shutterstock)

With the help of “Internet of Things” devices, brick and mortar retailers can fight back against virtual competitors. IoT hardware that collects data from in-store activity can give them a bird’s eye view of the customer experience and preferences in a way that online retailers can’t. Smart mirrors and check-out scanners can be used to analyze how customers interact with merchandise. Tracking beams can be deployed to optimize product and promotion placement.

Ordering Up a Meal with a Side of Big Data

Big data strategies are being embraced by food retailers as well. Restaurant chains recognize that big data can give them a leg up in a hyper competitive market. Analyzed effectively, the mountains of information can help restaurants to better manage inventories, pricing and labor schedules.

Information-driven methods likely helped the owner of Burger King in last year’s $1.8 acquisition of Popeyes Louisiana Kitchen, a 2,600-branch food chain. Burger King’s rival, MacDonald’s, has transformed itself into a data driven business. For example, it uses data collected at its drive through windows, such as information about the vehicles and interactions with passengers, to improve customer experience.

A Big Data Checklist

The first step in transforming an enterprise into an information driven business starts in the C-Suite. The executive team needs to define the major business dilemmas that need solving, and decide what type of insights it needs to reap from its data. What type of data does the organization need to collect to produce those insights? How will the organization collect it? What will be the desired impact?

A retail enterprise needs the right analytic tools that will deploy its data to make critical business predictions. Known as “predictive analytics,’’ this capability is necessary to give retailers a comprehensive view of who their customers are. Predictive analytics can help suggest how their buyers shop, when they buy certain products, and at what price.

With the right analytic tools, retailers need to crunch product tracking data to more effectively manage inventories. With a real-time view of product movement, big data can enable enterprises to improve operational efficiency by eliminating product bottlenecks, for example. When inventory stocks are more effectively managed, retailers can boost sales and better maximize earnings despite thin profit margins.

Just as essential to an effective big data strategy is the right choice of database infrastructure. The mountains of information collected by businesses won’t be very useful if they can’t be accessed swiftly and analyzed efficiently. If retailers can’t get real-time insights into their operation, the value of a big data strategy declines. And as retail companies merge with one another, it will be critical to ensure that their massive data stores can make operations more efficient, rather than bogging down operations.

So, as big data technology accelerates the pace of business, drives mega-mergers, and raises expectations for new levels of performance, companies need the proper strategies and gear. Ill-equipped organizations are likely to find themselves stumbling on the path to technological transformation: visions of big data efficiency could instead turn into a big data nightmare. However, for those companies who take advantage of emerging technologies to effectively reap the full potential of their large data stores, the future holds great promise.

About the author: Ami Gal is the CEO and Co-founder of SQream, a provider of a GPU-based database for big data analytics. Gal brings more than 20 years of technology industry expertise and executive management experience to his role with the company. Prior to SQream, Gal was Vice President of Business Development at Magic Software, where he generated new growth engines around high performance and complex data integration environments.  Previously, Gal co-founded Manov, which was later acquired by Magic Software and served to play an integral role in the company’s secondary offering. In addition, over the last decade, Ami has invested in and served on the boards of several startups, as well as mentoring founders in startup programs including IBM Smartcamp, Seedcamp, and KamaTech.

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