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Operations Analytics for Retail

Which suppliers are the best choice for our new product line? Can we optimize store layouts and display units to boost sales? How can we improve our customer experience without increasing staff levels? Operations analytics helps retailers answer these questions and many more. Using machine learning, artificial intelligence, data discovery, and other analytics methods, retailers can better use the data they collect from their operations and partners. In this article, we’ll share how retailers are using data analytics to uncover new opportunities and solve some of the industry’s most pressing challenges. 

What Is Operations Analytics?

Operations analytics is the practice of using data to identify opportunities for improvement across the entire supply chain, from sourcing to fulfillment. Although many leading retailers have prioritized using big data to improve operations, few use it to its full potential. Operations analytics can provide retailers with fresh insights on improving business processes and increasing profitability.

How Operations Analytics Benefits Retail

For both online and physical retailers, operations analytics provides numerous opportunities to streamline processes and improve the customer experience.

Increase loyalty through the in-store experience

Retailers that operate brick-and-mortar locations face unique challenges and opportunities as customers navigate a physical space and interact face-to-face with store employees. As competition from digital retailers grows, operational analytics can help these businesses thrive by boosting customers’ in-store experience to increase loyalty.

Personalize the shopping experience

Using data gathered from a customer’s past purchases, point of sale systems, mobile apps, and in-store sensors, retailers can serve up highly targeted promotions at the register or via email or SMS. A tailored shopping experience provides value to the customer and maximizes the potential value of each sale. 

Better align staffing levels to handle periods of peak demand

Using live video feeds from store locations, data analytics tools can track how long customers wait in line to make a purchase, which helps store managers balance staffing levels with periods of peak demand. 

Evaluate employee performance

Video feed can also be analyzed to identify missed opportunities to engage customers on the sales floor. These results can help inform targeted customer service training.

Reduce out-of-stock items

Retailers can use operations analytics to monitor how frequently products go out of stock to better align product availability with consumer demand. Data from inventory management systems and live video feed can also be used to track out-of-stock items in real time, helping retailers quickly address logistical issues that are barriers to timely replenishment. 

Better understand online customers

Today’s digital shoppers expect a seamless purchase experience backed by exceptional customer support. With online price comparison tools and product data just a click away, consumers are increasingly willing to exchange brand loyalty for a simpler, more pleasant online shopping experience. Operations analytics helps ecommerce retailers better understand their customers to maximize the value each one represents.

Recommend related products

Related product recommendations introduce customers to new products they may not be aware of but are likely to be interested in based on their prior purchase history or general purchasing data. For example, customers who purchase cold medication in winter will likely buy canned soup and tissues at the same time. Dynamically generated recommendations increase average order value and broaden the scope of products customers will return to your site to purchase again. 

Create targeted promotions

Retailers have access to a treasure trove of data on individual customers. Prior purchase histories can be supplemented with social media activity as well as purchase patterns at related retailers. Using this data, retailers can create enriched customer profiles for use in ad retargeting initiatives. Data can also be used to evaluate the effectiveness of different promotional tactics, helping retailers test different strategies and identify the ones that generate the greatest return on investment. 

Anticipate purchasing trends

Sensing the changing winds of consumer preferences and adapting quickly allows retailers to realize the full value of emerging trends. Conducting topic extraction and sentiment analysis using data sources such as social media feeds and YouTube video content can help retailers spot the next big thing before it enters the consumer mainstream. 

Operations Analytics Use Cases

Data from in-house and third-party sources can help retailers optimize their day-to-day operations in a variety of ways. Here are several use cases where operations analytics can be used to solve real-world business problems. 

Tighter integration of the online and in-store shopping experience

Many retailers conduct business on both ecommerce platforms and in-store locations. Combining purchase histories from in-store, ecommerce, social media websites, and retail partner channels creates a richer, more actionable customer profile. 

Proactive customer support

Proactive customer support can remove friction, prevent returns, and strengthen consumer loyalty. For example, a mobile device retailer can use data to identify customers who made a purchase but have not completed device setup within a specified period of time. This data could trigger a call from a customer service representative offering assistance with completing the setup process. 

Improved employee performance

Satisfied employees typically create satisfied customers. Operations analytics can reveal how incentives such as performance-based pay increases or additional breaks during long shifts impact employee performance. Data gathered from point-of-sale systems can be used to track how many customers employees ring up per hour, allowing business planners to compare efficiency rates over time. 

Powering Operations Analytics with the Snowflake Data Cloud 

An effective operations analytics strategy brings together data from across a retailer’s operations and beyond. The Snowflake Data Cloud provides a single, centralized platform that unites data from a variety of sources. Our elastic performance engine powers complex data pipelines, large-scale analytics, feature engineering, interactive applications, and more. Instantly and cost-efficiently scale to handle virtually any number of concurrent users and workloads without impacting performance, reducing the time to insight, even for the most complex operations analytics use cases. Enrich insights gathered from your in-house data with third-party data, connect with thousands of Snowflake customers, and extend your workflows with data services and third-party functions through Snowflake Marketplace.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.