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Data analytics and the changing world of retail in 2017

If there’s a word that described the retail space in 2016, it’s change. Change in technology, tools and best practices. And, (no surprise), 2017 promises more of same.

Photo source: istock.com

December 15, 2016

By Ilan Hertz

If there's a word that describes the retail space in 2016, it's change. Change in technology, tools and best practices. And, (no surprise), 2017 promises more of same.

Here are five trends destined to make retailing more effective and profitable in 2017.

Multi-channel data integration

After using data analytics for several years, retailers are getting a clear idea of the benefits that high-volume, high-speed data analytics can provide. Unlimited computing capacity in the cloud and advanced analytics enable retailers to overcome a familiar challenge: collecting and analyzing huge volumes of different types of data (databases, social media and instant messages, reports).

More recent developments show by using data analytics software, retailers can unify online and offline data by:

Extracting data from different places such as legacy systems and database platforms on-premises or in the cloud.
Using new sources of data from commerce, supply chain and customer channels.
Integrating conventional retail information and data from new channels with company ERP, order management and warehousing software.
Delivering useful operations suggestions quickly enough to capture business opportunities as they occur. Modern data analytics software can cut the time from weeks to minutes.

Modern retail analytics software packages customer and supply chain data and trends in a single view of what's going on. Putting all relevant data into a form that's easy to understand and use helps business users set up operational and promotional strategies and continue to improve efficiency and performance.

Predictive data analytics

Every retailer wants to have the right products available to customers at the right place and time. Making this happen, however, is not an easy matter.

Data analytics provides retailers with a better understanding of their current business. Predictive analytics provides retailers with a look into the future.

Until recently, retailers had to rely on insights gained from their own experience and retailing skill, analyst forecasts and customer feedback. But it all added up to high-quality educated guessing.

Predictive analytics uses mountains of data, which retailers already have, and a wide array of technologies and approaches (statistical modeling, data mining and other techniques) to analyze and project the likely outcome of future events and consumer behavior.

The biggest business value of predictive analytics is its ability to help retailers stay ahead of the expectations of discerning, tech-savvy consumers. This includes:

Delivering a better shopping experience. That is, enabling customers to shop whenever and wherever they want in an attractive, no-worries environment, in the store or online.
Getting a clearer view of customers. This includes a 360-degree view of customers and click-stream analysis.
Merchandizing and planning. Add real-time promotions, demand forecasting, pricing and markdown optimization and out-of-stock analysis and management.

One of the biggest changes in retail analytics lies in where all this data comes from.

Internet of Things in retail

Pioneering major retailers are scrambling to collect and analyze data from the Internet of Things. Customers provide useful IoT data by using and connecting to smartphones, tablets and wearables. Brick-and-mortar stores use IoT data generated by digital signage and other in-store sensors and devices.

Together, these sources generate massive data stores that describe customer behavior. Retailers use this data to make decisions and create sales strategies for their brick and mortar stores and distribution centers.

Innovative uses of IoT data and technology enable retailers to:

Customize a shopper's in-store experience. Increasingly, customers expect personalized service. Data collected from in-store IoT devices and the shopping history of connected consumers enable retailers to create a shopping profile of each customer. IoT data analysis discovers shopping patterns that help retailers deliver a more customized shopping experience.
Make in-store operations more efficient. Data harvested from in-store, IoT-enabled smart cameras, beacons, and sensors provide store managers and employees with a deeper understanding of what does and doesn’t work well on the floor. For example, analysis of real-time location datafrom smartphone apps can be transformed into customer traffic patterns and buying behaviors. With this information, employees can be alerted to bottlenecks immediately and reduce customer wait times at the cashiers.
Improve inventory and supply chain management. Smart transportation management applications and demand-aware warehouse fulfillment are two ways to transform IoT data to into an understanding of what’s underperforming, overstocked or running out of stock at your store.
Take advantage of new revenue opportunities: Leading-edge retailers are using the IoT to find new methods of acquiring customers and increasing revenues. For example, beacons and Wi-Fi can create an in-store environment, in which customers engage in contests, meet-and-greet events and social media product reviews.

Self-service analytics software

Not long ago, data analytics software users had to wait for reports designed and delivered by data analyst middlemen. When customers lobbied vendors for change, they got results. Business users got self-service applications that included easy-to-use dashboards and enabled direct queries. The software empowered business users to ask relevant questions and get answers—quickly—without data science degrees.

Specialized retail analytics software enables store managers and retail decision makers to:

Use easy-to-understand analytics methods on data relevant to their store.
Easily access, explore, and analyze data with just a few clicks
Quickly and easily engage with supply chain data.
Make decisions by analyzing products and merchandising methods.
Identify spending patterns and gain insight into customer behavior by choosing from a library of interactive visualizations.

Mobile to the rescue

We’ve all heard the complaint that customers enter brick-and-mortar stores with more product information than the staff. Equipping staff members with mobile devices linked to key internal applications and databases enables associates to personalize customer services and perform "save the sale" rescues with pricing, promotion and product information.

Ilan Hertz is head of digital marketing at Sisense. He has close to a decade of experience in applying data-driven methodologies in senior marketing positions in the technology industry.