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Retail Business Review | Thursday, May 12, 2022
Retail data analytics aids businesses in retaining customers and increasing their lifetime value (LTV) to the company.
Fremont, CA: Retail customers expect an engaging personal adventure while shopping online or in a store. Retail business can enhance their ability to provide that experience by using data analytics to learn their customers' needs and habits and then utilizing that knowledge to increase customer satisfaction and streamline operations. Retail data analytics aids businesses retain customers and increasing their lifetime value (LTV) to the company.
Applications for Retail Data Analytics
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Personalizing the customer experience can improve marketing customer satisfaction, conversion rates, and basket sizes can all be increased. Retail data managers can employ analytics to create customer profiles across all sales and marketing channels to improve the customer experience.
Consider the chance that a grocery store can learn about the purchasing habits of vegetarian customers. The store could utilize this information to create individualized email and social media campaigns for new, trendy plant-based protein products.
In addition, this data could be used by a store with an e-commerce presence to customize the structure of their online menu and upsell with recommendations for similar products. Further, the goal would be to provide a great customer experience to ride long-term value rather than simply growing basket size on a one-time transaction.
Retailers can track customer behavior more closely than just collecting purchase data. For example, customer in-person interactions with sales representatives and likes or comments on a social media post are valuable data points that businesses can utilize to tailor experiences and target customers with more innovative product recommendations and personalized advertisements.
Optimizing Supply Chain Management and Logistics
Businesses can also employ retail data to improve back-end supply chain management (SCM) and logistics. For example, some established retailers manage inventory using simple threshold-based models or basic heuristics to determine when demand for specific products fluctuates over time. However, modern analytics systems enable retailers to utilize their historical purchase and stock data to predict product demand and maintain inventory levels accurately.
Grocery stores, for instance, frequently have to enhance inventory before the holidays to account for an increase in demand. However, 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 regularly about actual demand. Organizations can refine forecasting models to the individual SKU and determine optimal purchasing levels by analyzing historical and market trend data.
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