How Can Customer Data Help Brands and Retailers | Radial

How Can Customer Data Help Brands and Retailers

Insights

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Catering to customers across dozens of different channels takes data. Lots of it. But having data on hand (and stored in data warehouses) is only half the battle. To capitalize on the insights available to you, you need to know what to look for and how to use it to your advantage.  

Here are 4 insights retailers and eCommerce brands can extract from their customer data and leverage to improve their customer experience.  

Customer Sentiment  

As a metric used to gauge your customers’ feelings toward your brand, customer sentiment analysis is a useful way to get both big picture and micro insights into the customer experience.  

Through surveys, direct feedback, and customer conversations with your support agents, you can see how your customers feel during individual interactions and pinpoint areas where improvement is needed. You can also highlight successes (like empathetic responses, strategic fulfillment choices, and effective solutions) and share them throughout your organization to create a more consistently positive customer experience.  

Customer Interactions 

Your customers interact with your brand on your website, your blog, your ads, social media channels, search engines, and your brick-and-mortar locations. And they often bounce between these channels a handful of times before making a purchase.   

Pulling interaction data from your customer data allows you to see exactly what the customer journey looks like (i.e. when and where customers interact with your brand), and pinpoint the channels that are most effective in generating sales. This, in turn, helps you make smarter decisions about where you should invest your time, resources, and energy to capture and keep loyal shoppers.  

Shopping Behaviors  

Not all products are sold at the same rate. Some explode in popularity after an Instagram influencer promotes it in their content and others move straight from shelf to clearance rack before being thrown away or donated.  

Sifting through your data to determine which products are flying off the shelves and selling out in your online store can help you effectively restock your inventory and spread products across your locations to meet the demand. At the same time, when analyzed through a predictive lens, this data can help you plan future inventory and fulfillment routes so you always have exactly what you need, where you need it. 

Fulfillment Preferences 

Not all customers like to shop in the same way. While many like to have their order shipped directly to their home, others prefer to pick up their package curbside or in the store.  

Identifying customer fulfillment preferences (both individual and regional) can help you determine the best way to share inventory across your warehouses and storefronts and map out the most cost-effective and efficient fulfillment routes for customer orders.   

While mountains of customer data can be overwhelming to comb through, there are a lot of valuable insights hidden beneath the surface. By mining your data for insights on customer sentiment, fulfillment preferences, interactions, and shopping behaviors, you can effectively improve your customer experience in the way your customers want.   

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