Customer Analytics In Retail: How Data Drives Dollars For Vendors

Technologies like computer vision and augmented reality have unlocked a new world of consumer data for retailers

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Business is an information game: the more you know, the better your decisions, and the higher your revenue. For retailers to win this game, they need to discover new ways of obtaining and analyzing relevant data. And there are few data types they should prioritize over customer analytics.  

If you want to get current on how brick-and-mortar businesses are operationalizing this data, then read on. From collection to implementation, this article provides a comprehensive overview of customer analytics in retail.

Want to learn more about the cutting-edge of consumer analytics in retail? Download our ebook, 4 Ways Augmented Reality Can Change Brick & Mortar Retail.

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Defining customer analytics in retail

Understanding the ROI of customer analytics

Collecting customer analytics

Developing a data-driven layout

Driving inventory optimization

Enabling smart merchandising

Building recommendations engines

Learning more about customer analytics for retail

Defining customer analytics in retail

Customer analytics are data-driven insights into consumers that retailers use to improve business operations and drive profitability. Customer analytics can inform store layout, test campaigns, optimize pricing, deliver personalized recommendations, and much more. To fully take advantage of customer analytics, retailers must create a comprehensive data integration plan and ensure key stakeholders can operationalize the information. This guide explores how new technologies like augmented reality (AR) are changing the industry — a must-read for any retailer ready to embrace the next generation of analytics. 

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Understanding the ROI of customer analytics

Growing access to customer data and the rise of technologies like machine learning have led to remarkable revenue gains for early adopters. Recent research analyzing the impact of data analytics on business performance indicates that retailers who adopt a data-based approach see significant organizational growth. Surveying almost 300 respondents from supermarkets, department stores, and ecommerce companies, the study revealed data initiatives had led to at least 5% growth for 68% of respondents, at least 10% growth for 38% of respondents, and at least 15% growth for 27% of respondents.

In other words, the integration of sophisticated customer analytics significantly improved retail performance. These results reinforce the classic wisdom business guru Peter Drucker, “If you can't measure it, you can't manage it.”

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Collecting customer analytics

While the growth of the internet and universal adoption of smartphones led to an explosion of consumer data, recent tech policy shifts have ushered in a new era. Third-party data — data purchased from outside providers — will become increasingly scarce and cannot be thought of as a reliable business resource. To compensate, retailers need to go beyond loyalty programs and build additional native consumer intelligence collection channels.

One of the most promising data sources is linked to a technology being adopted by more and more brick and mortar retail locations: augmented reality (AR). AR is a visualization technology that enhances or reproduces real-world spaces. From navigation to product recommendations, retailers are using AR to provide more engaging shopping experiences for consumers. In addition, these applications can be a rich source of customer data: generating granular, user-level, and real-time analytics on shoppers as they browse.

Vera is an industry-leading AR platform that allows retailers to deliver immersive consumer experiences while they collect a wealth of customer analytics. If you thinking about developing your own next-generation consumer data engine, then reach out. Our team would love to set you up with a demo.

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Developing a data-driven layout

While many industry best practices connect to historical consumer preferences, the signal they provide is generic. To truly optimize the in-store experience, retailers need to understand their shoppers’ idiocracies. Especially if derived from live interactions, behavioral analytics can help retailers make precision design decisions that drive revenue. This intelligence can include detailed traffic heatmaps, points of interest, and shopper navigation patterns — enabling businesses to identify the ideal flow, layout, and landmarks, given their unique audience.

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Driving inventory optimization

Next-generation customer analytics is powering a revolution in retail inventory management. Real-time customer flow statistics provide retailers with precise, accurate, and up-to-minute data that retailers can use to optimize their stocking strategy. Complementing information flows like seasonal patterns and historical demand curves, this dataset allows inventory managers to stay agile — adjusting to the day-by-day fluctuations in consumer behavior.

Forward-thinking retailers like Kroger have been investing in this approach for years. The grocery giant has developed an in-house analytics department to ensure they can anticipate demand trends and adjust their business strategy as needed. As technology accelerates the evolution of consumer preferences and expectations, retailers will need to incorporate real-time customer analytics into their inventory management process.

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Enabling smart merchandising

Building out physical collateral for promotional campaigns is an expensive and high-risk project, and the stakes increase according to a retailer’s square footage and property sprawl. Branded collateral is simply much easier for ecommerce platforms to design, distribute, and replace than it is for brick-and-mortar shops. To hedge against this risk, retailers need to conduct thorough concept testing with their target audience — a project current AR technology is well-suited to undertake.

Using an AR shopping application, retailers can deploy product placements, displays, signage, and other promotional elements and receive immediate feedback via customer analytics. After running multiple iterations, retailers can then invest in a merchandising strategy that has minimal downside exposure. 

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Building recommendations engines

Few techniques fuel purchasing volume like personalized recommendations. But, until recently, this feature has been challenging for most brick-and-mortar retailers to operationalize. However, retailers that utilize AR applications to enhance in-store experiences can readily deploy this revenue-driving feature. Through a granular analysis of buying history, retailers can deliver personalized product recommendations to their consumers via AR as they browse.

While many store-front retailers have yet to implement this into their customer experience, a recent study by Accenture indicates it could be a massive boost to their bottom-line: 65% of consumers want to buy from retailers that know their shopping history.

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Learning more about customer analytics in retail

We’re at a turning point in the history of retail. New technologies like AR catalyze an in-store shopping revolution, and the businesses that catch this wave will benefit on all fronts. They’ll not only be able to enhance the entire consumer journey through engaging and useful visuals, but they’ll also have access to customer analytics that will transform their bottom line.

If you’re considering using AR to upgrade your consumer intelligence toolkit, feel free to contact us. Retailers use our AR platform, Vera, to generate comprehensive, real-time customer analytics that boost shopper satisfaction and drive revenue.

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