As retail becomes a more competitive, digital industry, companies must be proactive, understanding the behaviors and attitudes of their consumers, allowing them to organically create demand for the right products at the right time.
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In recent years, the landscape of retail industries around the globe has shifted dramatically, as businesses are forced to adapt to modern practices to maintain relevance in an increasingly competitive market. Understanding the modern consumer, their digital interactions, and their behaviors has become more important than ever. As online sales and the importance of digital platforms continue to grow at shocking rates, retailers are faced with the challenge of changing their traditional strategies and employing new, digital approaches to strategy and problem-solving. While this is no easy task, new technological innovations have transformed business practices, as businesses now have access to a previously inaccessible ocean of knowledge. Big data, predictive analytics, and data modeling has and will continue to revolutionize markets, especially within the retail industry.
The world of big data combined with new predictive analytic platforms has unlocked great potential for retailers and businesses of all variety. Trends in big data have set in rapidly, as not long ago, this information was scarce and lacked the ability to be analyzed. In fact, 90% of all large data collections that exist today were created between 2015 and 2016 (IBM). Since then, data collections have continued to grow at unprecedented rates, transforming big data into the backbone of many modern retailers. According to a 2018 Forbes poll, 79% of company executives believe that a failure to embrace big data and its potential will cause companies to lose their competitive edge and potentially lead to their demise (Forbes). Those who have managed to adapt and embrace big data and predictive analytics have certainly seen the benefits.
Many of today’s businesses face a similar dilemma: managing their unstructured data, putting sense to it, and ultimately implementing responses according to it. Retailers have been particularly impacted by these shifts, given the customer-centric nature of the modern retail industry. While many retailers execute new strategies and approaches based on customer responses and reactions, predictive analytics can give companies insights to act based on a pre-formed understanding of customer preference and behavior.
Retailers that leverage their big data, investing in analytics platforms are likely to see returns on their investments. A 2019 Entrepreneur study revealed that businesses that used their big data saw
profit increases of around 8–10 percent, as well as overall cost reduction of around 10 percent. Kroger Co., the largest supermarket chain by revenue in the United States, has seen the benefits of such practices. Kroger’s joint venture with UK-based analytics company Dunnhumby, owned by Tesco.
The platform employed by Dunnhumby and Kroger embraces artificial intelligence, as their data models evolve from day-to-day, constantly improving through machine learning algorithms. Such a dynamic model is essential within retail, as it can account for complex, typically unpredictable human behaviors that are especially prevalent when analyzing shopping preferences. “Claiming 95% of sales are rung up on the loyalty card, Kroger sees an impact from its award-winning loyalty program through nearly 60% redemption rates and over $12 billion in incremental revenue by using big data and analytics since 2005.” Moving forward, more and more retailers will employ similar tactics, seeking to leverage their mass data in the most efficient way possible.
Automated analyses of historical data, external data (social media, web traffic, etc.) and integrated platforms, in many ways, have fundamentally changed strategy and operations for many businesses. However, these predictive processes still require much attention and improvements in moving forward. Analytic processes have revealed flaws, weak points, and other mismanagements of company data. In many ways, big data and predictive analytics platforms are works in progress, getting more effective and more efficient each day by the work of talented tech insight teams.
Models must constantly be updated, as assumptions are challenged and traditional approaches are broken. As more and more data is readily accessible and utilizable for companies, analytics translators are required to synthesize findings, interpret them, and put them into grand strategy. While the full potential of precise analytics and predictive processing remains untapped, automation within big data is the future of sales and marketing. To remain relevant into the future, retailers must adopt and embrace modern data practices to better understand their consumers, their behaviors, and their constantly-shifting preferences.
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