The retail industry continues to accelerate, 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% in 2017, and e-Commerce continues to make massive gains with an expected growth of 15% this year, according to Kiplinger. 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.
Retailers will need to empower people across their organization to make decisions swiftly, accurately and confidently. The only way to achieve this is to harness big data to make the best plans and decisions, understand customers more deeply, and uncover hidden trends that reveal new opportunities.
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. 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. Retailers must 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.
By analyzing data sources like POS systems and in-store sensors, 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; and
- 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).
Correlating customer purchase histories and profile information, as well as behavior on social media sites 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.
4. Customer Journey Analytics
Today’s customers are more empowered and connected than ever before. 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. 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, retailers can bring together 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 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 retailers to use big data 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.
For retail companies to maintain a competitive edge in an accelerating marketplace, it is becoming increasingly important for them to seek proactive methods of harnessing new and extensive data sources in innovative ways. With the help of data, retailers stand to be able to achieve deeper understanding of their customer data, which will in turn lead to valuable business insights.
John Morrell is responsible for product marketing at Datameer, where he leads the go-to-market efforts for the Datameer product family and understands how customers use Datameer to solve their business problems. John has a 25-year history in enterprise software, bringing to market numerous enterprise software products and working extensively to help solve difficult business problems in data management, BI, and analytics for companies such as Aleri, Coral8, Active Software, webMethods, Oracle, Informix, and Fair Isaac. John holds an MBA from Bentley College and a BS in computer engineering from Syracuse University.