Five Big Data Use Cases for Retail

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 and behavior retail analytics, to make the best plans and decisions, understand customers more deeply, uncover hidden trends that reveal new opportunities, and more.

The business impacts of these tools are real. According to a recent study conducted by IBM’s Institute for Business Value:

“Sixty-two percent of retailers report that the use of information (including big data in retail) and analytics is creating a competitive advantage for their organizations, compared with 63 percent of cross-industry respondents. We also discovered that retailers are taking a business-driven and pragmatic approach to big data. The most effective big data strategies identify business requirements first and then tailor the infrastructure, data sources, and analytics to support the business opportunity.”

To better understand the value of big data analytics in the retail industry, let’s take a look at the following five behavior retail analytics use cases, which are currently in production in various leading retail companies.

1. Customer Behavior Retail Analytics

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 Retail

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:

• Websites
• Point-of-sale systems
• Mobile apps
• Supply chain systems
• In-store sensors
• Cameras
• And more

With the help of data engineering platforms, omnichannel retailers can:

• Test and quantify the impact of different marketing and merchandising 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, omnichannel 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

4. Customer Journey Analytics

Today’s customers are more empowered and connected than ever before. Using channels like mobile, social media, and e-commerce, 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.

At the same time, customers expect more. For example, they expect companies to provide consistent information and seamless experiences across channels that reflect their history, preferences, and interests. More than ever, the quality of the customer experience drives sales and customer retention. Given these trends, marketers need to continuously adapt how they understand and connect with customers. This requires having data-driven insights that can help you understand each customer’s journey across channels.

With big data engineering technologies, you can bring together all of your structured and unstructured data into Hadoop and analyze all of it as a single data set, regardless of data type. The analytical results can reveal totally new patterns and insights you never knew existed — and aren’t even conceivable with traditional analytics. You’ll be able to get answers to complex retail questions such as:

• What’s really happening across every step in the customer journey?
• Who are your high-value customers and how they behave?
• How and when is it best to reach them?

5. Operational Analytics and Supply Chain Analysis

Faster product life cycles and ever-complex operations cause organizations to use big data in retail analytics to understand supply chains and product distribution to reduce costs. Many retailers know all too well the intense pressure to optimize asset utilization, budgets, performance, and service quality. It’s essential to gaining a competitive edge and driving better business performance.

The key to utilizing data engineering platforms to increase operational efficiency is to use them to unlock insights buried in log, sensor, and machine data. These insights include information about trends, patterns, and outliers that can improve decisions, drive better operations performance and save millions of dollars.

Servers, plant machinery, customer-owned appliances, cell towers, energy grid infrastructure, and even product logs — these are all examples of assets that generate valuable data. Collecting, preparing, and analyzing this fragmented (and often unstructured) data is no small task. The data volumes can double every few months, and the data itself is complex — often in hundreds of different semi-structured and unstructured formats.

Data engineering allows you to quickly combine structured data such as CRM, ERP, mainframe, geolocation, and public data and combine them with unstructured data. Then, utilizing the right analytical tools, you can use this data to detect outliers, run time series and root cause analyses, and parse, transform and visualize data.

Conclusion

Data engineering that drives action can rapidly bring together and explore massive sets of structured and unstructured data to uncover hidden patterns, new correlations, trends, customer insights, and other useful business information.  For retail companies to maintain a competitive edge in an accelerating marketplace, it is becoming increasingly important for them to find faster ways to transform their raw data into analytics-ready for truly actionable analytics.

Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.

With Datameer, retailers can easily transform their data using any of the approaches – no-code, low-code, or code.  Non-technical analytics team members can work with complex data without the need to write code using Datameer’s no-code and low-code data transformation interfaces.  You can foster collaboration amongst technical and non-technical team members to build data models and the data transformation flows to fulfill these models, each using their skills and knowledge.  And finally, you can generate rich documentation and add user-supplied attributes, comments, tags, and more to share searchable knowledge about your data across the entire analytics community and also use this information for compliance processes.

Learn more about our innovative SaaS data transformation solution by scheduling a personalized demo today!