The retail industry continues to accelerate rapidly, and with it, the need for businesses to find the best retail use cases for big data. Sales alone are expected to grow by 3.5 percent in 2017, and e-commerce continues to make massive gains with an expected growth of 15 percent this year (Kiplinger, 2017). New sources of data, from log files and transaction information, to sensor data and social media metrics, present new opportunities for retail organizations to achieve unprecedented value and competitive advantage in an expanding industry space.
From a business standpoint, retailers will need to empower people across their organization to make decisions swiftly, accurately and with confidence. The only way to achieve this is to harness big data to make the best plans and decisions, understand customers more deeply, uncover hidden trends that reveal new opportunities and more.
To better understand the value of big data analytics in the retail industry, let’s take a look at the following five use cases, which are currently in production in various leading retail companies.
1. Customer Behavior 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. But consumers today interact with companies through multiple interaction points — mobile, social media, stores, e-commerce sites and more. This dramatically increases the complexity and variety of data types you have to aggregate and analyze.
When all of this data is aggregated and analyzed together, it can yield insights you never had before — for example, who are your high-value customers, what motivates them to buy more, how do they behave, and how and when is it best reach them? Armed with these insights, you can improve customer acquisition and drive customer loyalty.
Data engineering is the key to unlocking the insights from your customer behavior data — structured and unstructured — because you can combine, integrate and analyze all of your data at once to generate the insights needed to drive customer acquisition and loyalty.
2. Personalizing the In-Store Experience With Big Data
In the past, merchandising was considered an art form, with no true way to measure the specific impact of merchandising decisions. And as online sales grew, a new trend emerged where shoppers would perform their physical research on products in-store and then purchase online at a later time.
The advent of people-tracking technology offers new ways to analyze store behavior 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 incent consumers to complete purchases with the end goal being to increase sales across all channels.
Data engineering can turn in-store customer data sources into a major competitive advantage for retailers. Insights can drive cross-selling, increase promotional effectiveness, and much more. These insights can be gathered from:
- Point-of-sale systems
- Mobile apps
- Supply chain systems
- In-store sensors
- And more
With the help of data engineering platforms, omni-channel retailers can:
- Test and quantify the impact of different marketing and merchandizing tactics on customer behavior and sales
- Use a customer’s purchase and browsing history to identify needs and interests and then personalize in-store service for customers
- Monitor in-store customer behavior and drive timely offers to customers to incent in-store purchases or later, online purchases, thereby keeping the purchase within the fold of the retailer
3. Increasing conversion rates through predictive analytics and targeted promotions
To increase customer acquisition and lower costs, retail companies 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, customers interact more than they transact – and those interactions occur on social media and through multiple channels. Because of these trends, it’s in the best interest of retailers to turn the data customers generate via interactions into a wealth of deeper customer information and insight (for example, to understand their preferences).
Data engineering is capable of correlating customer purchase histories and profile information, as well as behavior on social media sites. Correlations can often reveal unexpected insights — for example, let’s say several of a retailer’s high-value customers “liked” watching the Food Channel on television and shopped frequently at Whole Foods. The retailer can then use these insights to target their advertisements by placing ads and special promotions on cooking-related TV shows, Facebook pages and in organic grocery stores. The result? The retailer is likely to encounter much higher conversion rates and a notable reduction in customer acquisition costs.
Using data engineering platforms, omni-channel retailers can:
- Test and quantify the impact of different promotional tactics on customer behavior and conversion
- Use a customer’s purchase and browsing history to identify needs and interests and then personalize promotions for customers
- Monitor customer purchasing behavior and social media activity to drive timely offers to customers to incent online purchases with a specific retailer