Retail customers expect an engaging personal experience when shopping online or in a store. Retail businesses can do a better job of providing that experience by using data analytics to learn their customers’ needs and habits, and using that information to increase customer satisfaction and streamline operations. Retail data analytics helps organizations retain customers, and can enhance their lifetime value (LTV) to the business.

What is retail data analytics?

Retail data analytics is the process of analyzing data to inform smarter decisions that improve operations and increase sales. Both end-user data and back-end processes such as supply chain and inventory management are targets for data analytics.

Big data and business intelligence (BI) enable retailers to improve their analytic processes and make smarter decisions. Omnichannel retailers have begun to reconcile online and offline customer records, providing high-value insights into their customers’ complex interactions with their services.

Use cases for retail data analytics

Retailers can use data analytics to enhance almost every aspect of their businesses.

Personalize customer experience and enhance marketing

Personalizing the customer experience can increase satisfaction, conversion rates, and basket sizes. Retail data managers can use analytics to build customer profiles across all sales and marketing channels to better personalize customer experience.

Imagine that a grocery store can determine the habits of customers who buy vegetarian products. The store could use this data to create customized email and social media campaigns for new, trendy plant-based protein products. A store with an e-commerce presence could use this data to customize the structure of their online menu and upsell with recommendations for similar types of products. The goal would be not just to increase basket size on a one-off transaction, but to provide a great customer experience to drive long-term value.

Retailers can track customer behavior in more detailed ways than simply collecting purchase data. Customer discussions with sales representatives in person or likes or comments on a social media post are valuable data points that businesses can use to tailor experiences and target customers with smarter product recommendations and personalized advertisements.

With predictive analytics, businesses can mine their historical datasets to find patterns in customer interactions. Business managers need not rely solely on intuition and can instead use statistical models to determine what works.

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Optimize supply chain management and logistics

Enterprises can also use retail data to optimize back-end supply chain management (SCM) and logistics. Some established retailers use simplistic threshold-based models for managing inventory, or basic heuristics to determine when demand for certain products fluctuates over time. Modern analytics systems allow retailers to use all of their historical purchase and stock data to more accurately predict demand for products and dynamically manage their inventory levels.

For example, grocery stores often have to increase inventory before holidays to account for an increase in demand. Absent analytics, and with potentially tens of thousands of stock keeping units (SKU) in-store, management can adjust only broad categories or select products. This results in regular over- or understocking relative to true demand. By analyzing historical and market trend data, organizations can refine forecasting models down to the individual SKU and determine optimal purchasing levels.

Businesses can also use analytics when scheduling in-store labor. Anticipating labor needs can be difficult due to the many factors at play, including the time of day and week, seasonality, holidays, and weather patterns. Enterprises can forecast customer demand more accurately by analyzing historical data and external datasets relevant to these factors. Managers can use the results of this analysis to adjust staffing and store hours.

An enterprise can even use retail data analytics to plan expansion. Mobile location analytics services provide insights into real-time consumer behavior, showing where they move throughout the city and what types of shops they patronize throughout the day. A retailer can use this data to target locations with a high density of consumers that are underserved in its retail market.

Manage prices to maximize sales

Tracking retail transactions, and combining this data with real-time wholesale and operational costs, can help retailers understand how changing prices may affect the bottom line, and help determine optimal pricing. However, determining optimal pricing requires large sample sizes. A small retail location may not process sufficient volume in a short period of time to statistically determine whether a price adjustment had a material impact on sales or profit. Other factors, such as weather, can also affect demand. These types of analyses are therefore most appropriate for high-volume stores or chain retailers.

Pitfalls of retail data analytics

Reconciling various sales and marketing channels may be the most difficult aspect of retail data analytics. Retailers ideally want to analyze all customer interactions, both online and in-store. This is made difficult by customer privacy concerns, and retailers should avoid intrusive attempts to obtain customer data. However, there are ways to politely nudge users to share their information, such as providing regular discounts for customers that are part of rewards programs or by asking them to fill out surveys.

All businesses should collect data securely and transparently. Any advantages from analyzing potentially broader sets of customer data are far outweighed by the repercussions of negatively affecting customer experience or accidentally leaking sensitive information. Enterprises should prioritize data security as a critical part of retail data analytics.

Take advantage of retail data analytics

Retail data analytics has the potential to improve customer experience, increase retention and sales, and optimize back-end processes for managing inventory and labor.

Integrating your enterprise’s data is key to effectively performing retail data analytics. Stitch provides an easy-to-use pipeline for replicating data from more than 100 databases and SaaS platforms to the data warehouse of your choice, centralized and ready for analytics. Begin to take advantage of retail data analytics with Stitch today.

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