Seizing the Potential of Big Data with Retail Data Analytics

Modern analytics systems enable retailers to utilize their historical purchase and sock data to accurately estimate the demand for products and dynamically administer inventory levels.

FREMONT, CA: Implementing data analytics in retail businesses can enable them to understand customers’ needs and habits and using that information to optimize customer satisfaction and simplify operations. Retail data analytics allows organizations to retain customers and improve their lifetime value (LTV) to the business.

Retail data analytics analyses data to make informed decisions that enhance operations and sales. Big data and business intelligence (BI) allows retailers to optimize the analytic process and make smarter decisions. Omnichannel retailers accommodate online, and offline customer records to provide high-value insights into their customers’ interactions with their services.

Here are three ways to unleash the potential of big data with retail data analytics:

Manage Prices to Maximize Sales

Retailers can understand how changing prices affect the bottom line and determine the ideal pricing by tracking retail transactions and combining the data with real-life wholesale and operational costs. This type of analysis is appropriate for high-volume stores or chain retailers.

Tailored Customer Experience and Enhance Marketing

Retail data managers can use analytics to create customer profiles throughout the sales and marketing channels to improve personalized customer experience, which increases satisfaction, conversion rates, and basket sizes.

Businesses can extract their historical datasets to find patterns in customer interactions with the help of predictive analytics. Managers can use statistical models to establish what works instead of depending on instinct.

Improved Supply Chain Management and Logistics

Businesses can use retail data to enhance back-end supply chain management (SCM) and logistics. Most established retailers use simple limit-based models for handling inventory or basic heuristics to decide when demand for specific products fluctuates over time. Modern analytics systems enable retailers to utilize their historical purchase and sock data to accurately estimate the demand for products and dynamically administer inventory levels.

Analytics can also be used when scheduling in-store labor to predict customer demand by examining historical data and external datasets appropriate to these factors. Managers can use the result of the analysis to change staffing and store hours.

Mobile location analytics services offer insights into real-time consumer behaviors to show where they move across the city and the types of shops they patronize. Retailers can utilize this data to target locations with large volumes of consumers that are underserved in their retail markets.