Thanks to emerging technologies like Big Data, retail chains can access the insights required to enhance their offerings, boost sales, forecast demand (by analyzing demand data and purchasing patterns), and optimize the overall customer experience—with a primary focus on customer retention and acquisition.
In other words, Big Data is already driving new revenue streams by improving customer engagement through targeted incentives and tailored offers. But that is not all: with Big Data solutions accelerated by cloud adoption, companies can also optimize business operations, decision-making, inventory, and supply chain management, as well as execute smarter pricing strategies.
Far from being a futuristic concept, these technologies deliver short-term impact. A study indicated that over the next 5 years, innovative and disruptive technologies will continue to drive digital transformation in retail at a rapid pace. In this landscape, thriving retail chains will be those that champion data-driven, experiential, and personalized strategies. By 2021—the report notes—60% of top retailers will use a consent-based approach to maximize the value of contextualized customer journey personalization and automated interactions, quadrupling brand loyalty. By 2022, 20% of retailers will undergo a complete cultural shift where leadership champions disruptive innovation, driving investments that trigger the next phase of growth, while the remaining 80% experience a slowdown. Furthermore, by 2022, 35% of retailers will announce partnerships or ecosystem cross-overs to offer new services that merge online and in-store customer experiences.
Big Data Analytics
The hallmark of our era is personalization. Delivering a robust, seamless, and integrated multichannel shopping experience with total continuity between physical and digital touchpoints has become a baseline requirement. Big Data solutions—specifically advanced analytics platforms designed for massive data volumes—enable retailers to generate personalized recommendations based on purchase histories, forecast future spending trends, and make strategic decisions backed by comprehensive data and market research.
By capturing web browsing behavior, visited products, and geographic location, businesses can understand customer habits and even infer their specific stage in the customer lifecycle.
For example, Amazon uses large-scale data to recommend products to customers based on their search histories and past purchases. In fact, its recommendation engine has driven nearly 30% of its overall sales.
In short, analyzing these data streams unlocks unique opportunities. For instance, weather data can be leveraged to forecast demand, as weather patterns help personalize product recommendations for consumers.
Additionally, these solutions enable companies to:
- Manage supply and product distribution based on accurate, real-time data.
- Improve service quality by leveraging insights from recorded calls, customer feedback (such as "social listening" via Natural Language Processing/NLP models), web claims, and support tickets.
- Build a complete, 360-degree customer view, tracking preferences and habits in granular detail.
- Optimize dynamic pricing by monitoring search trends and web traffic to adjust pricing policies accordingly.
- Track the customer journey to enhance experiences by identifying where users look for information, where they abandon purchases, and how to reach them more effectively.
- Analyze in-store customer movement (via video analytics and motion sensors) to place products strategically.
- Perform market basket analysis to determine which products customers are most likely to buy together.
Big Data Solutions
Ultimately, Big Data empowers retailers to inform, test, and design their commercial strategies. It grants the ability to predict which products will become popular and identify which customers will most likely be interested in them, as machine learning algorithms leverage historical data to generate highly targeted recommendations.
A prominent case is Walmart, which has been building one of the world's largest private cloud systems. The company created the Data Café, a state-of-the-art Big Data analytics hub that ingests its own transactional data alongside information from over 200 external sources (economic data, weather reports, social media trends, competitor pricing, local event information, and more) to monitor market shifts. Thanks to these technologies, the company can quickly identify and resolve pain points across both its physical stores and online business.
Big Data is not the future of retail. It is the present—and it is marking a clear before and after in the industry.