
How to Unlock Big Data with Retail Data Analytics
Personalizing thecustomer experience can enhancemarketing customersatisfaction, conversion rates, and basket sizes. Retaildata managers canutilize analytics to create customerprofiles across all sales and marketing channels in order to improve
By
CIO Applications Europe | Tuesday, November 23, 2021

Fremont, CA: When shopping online or in a store, retail customers expect an engaging personal experience. Retail business can improve their ability to provide that experience by using data analytics to learn their customers' needs and habits and then using that knowledge to increase customer satisfaction and streamline operations. Retail data analytics helps businesses retain customers and increase their lifetime value (LTV) to the company.
Applications for Retail Data Analytics
Personalizing the customer experience can enhance marketing customer satisfaction, conversion rates, and basket sizes can all be increased by. Retail data managers can utilize analytics to create customer profiles across all sales and marketing channels in order to improve the customer experience.
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Consider the possibility that a grocery store can learn about the purchasing habits of vegetarian customers. The store could use this information to create personalized email and social media campaigns for new, trendy plant-based protein products. This data could be used by a store with an e-commerce presence to customize the structure of their online menu as well as upsell with recommendations for similar types of products. The goal would be to provide a great customer experience to drive long-term value rather than simply increasing basket size on a one-time transaction.
Retailers can track customer behavior in greater depth than just collecting purchase data. Customer in-person interactions with sales representatives and 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.
Optimizing Supply Chain Management and Logistics
Businesses can also use retail data to improve back-end supply chain management (SCM) and logistics. Some established retailers manage inventory using simple threshold-based models or basic heuristics to determine when demand for specific products fluctuates over time. Modern analytics systems enable retailers to utilize all of their historical purchase and stock data to more accurately predict product demand and maintain inventory levels dynamically.
Grocery stores, for instance, frequently have to increase inventory before the holidays to account for an increase in demand. Management can only adjust broad categories or select products in the absence of analytics and with potentially tens of thousands of stock-keeping units (SKU) in-store. As a result, overstocking or understocking occurs on a regular basis in relation to true demand. Organizations can refine forecasting models down to the individual SKU and determine optimal purchasing levels by analyzing historical and market trend data.
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