Big Data in Retail: Too good to resist
10 Jul 2019 Big Data in Retail: Too good to resist
Personalization is critical to customer interaction and it has become more prominent in recent years. The expectation of seamless experiences between online channels and brick and mortar stores has become a pre-requisite for any customer interaction. With the retail industry continuing to accelerate rapidly, there is a compelling need for businesses to find the best retail use cases for big data.
The value of big data analytics is reflected through these use cases which has the potential to transform the way retail operates.
Customer behaviour analytics for Retail
Deeper, data-driven customer insights are critical to tackling challenges like improving customer conversion rates, personalizing campaigns to increase revenue, predicting and avoiding customer churn, and lowering customer acquisition costs. However, customer today interact with brands through multiple channels – mobile, social media, stores, eCommerce sites and more. This significantly increases the complexity and variety of data types you have to aggregate and analyse.
When all these data are integrated and analysed, it brings out insights that are never seen before for e.g. information about high-value customers, what motivates them to buy more, how do they behave and the best way to reach them. Armed with these insights, you can improve customer acquisition and drive customer loyalty.
Data engineering is integral to unlocking the insights from your customer behaviour data – structured or unstructured – as you can combine, integrate and analyse all of your data at once to generate the insights needed to drive customer acquisitions and loyalty.
Using big data to personalize in-store experience
In the past, merchandizing couldn’t be measured tangibly, as it was impossible to gauge the specific impact of merchandising decisions. With the proliferation of eCommerce, a new trend emerged where shoppers would perform their physical research on products in-store and then purchase later online.
The advent of customer tracking technology offers new ways to analyse store behaviour and measure the impact of merchandising efforts. A data engineering platform can help retailers make sense of their data to optimize merchandising tactics, personalize the in-store experience with loyalty apps and drive timely offers to persuade customers to complete purchases with the end goal being to increase sales across all channels.
Data analytics can turn in-store customer data sources into a major competitive advantage for retailers. Insight can drive cross-selling, increase promotional effectiveness, and much more.
Boosting conversion rates through predictive analytics and targeted promotions
In order to enhance customer acquisitions and lower costs, retail brands need to target customer promotions effectively. This requires having a 360-degree view of customers and prospects that’s as accurate as possible.
Historically, customer information has been limited to demographic data collected during sales transactions. But today, customer interaction precedes transaction – and those interactions occur on social media and through multiple channels. These trends led the retailers to turn the data customers generate via interactions into a wealth of deeper customer information and insights.
Data analytics is capable of correlating customer purchase histories and profile information, as well as behaviour on social media sites. Correlations can often reveal unexpected insights – for example, the channels or content that the customers prefer to consume in terms of entertainment or information can be derived as insights for targeted advertisements on these channels. This may result in higher conversion rates and a notable reduction in customer acquisition costs.
Analytics for customer journey
Customers today, are more empowered and connected like ever before. Using channels like mobile, social media and eCommerce, customers can access just about any kind of information in seconds. This informs what they should buy, from where and at what price. Based on the information available to them, customers make buying decisions and purchases whenever and wherever it’s convenient for them.
With big data analytics, you can bring together all of your structured and unstructured data and analyse all information as a single data set, regardless of the data type. The analytical results can reveal totally new patterns and insights you never knew existed – and aren’t even conceivable with traditional analytics.
Operational analytics and supply chain analysis
Faster product life cycles and ever-complex operations cause retailers to use big data analytics to understand supply chains and product distribution to reduce the costs. Brands know all too well the intense pressure to optimize asset utilization, budgets, performance and service quality. It’s critical in enhancing competitive edge and driving better business performance.
Data analytics that unlocks insights buried in log, sensor and machine data enables operational efficiencies. These insights include information about trends, patterns, outliers, etc that can improve decisions, drive better operations performance and save millions of dollars.
For retailers, big data can create opportunities to provide better customer experience. In order to have a competitive edge, it is becoming increasingly important for brands to seek proactive methods of harnessing new and extensive data sources in innovative ways. With the help of data analytics, retailers are able to achieve deeper understanding of their customer data, which will, in turn, lead to valuable business insights.
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