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How to Use Omnichannel Data to Your Advantage

Blog Post
We’ll look at 3 different types of retail data you can collect with an omnichannel order management system and dig into a few out-of-the-box use cases.
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Omnichannel shopping has thrown the door wide open to big data in retail. Unfortunately, many retail and eCommerce businesses still don’t know how to fully use this data to their advantage. As a result, they miss out on opportunities to streamline their business operations and elevate customer satisfaction.  

This doesn’t mean retailers need to give up on the idea of a fully data-driven business model, though. Let’s look at 3 different types of retail data you can collect with an omnichannel order management system and dig into a few out-of-the-box use cases for each one.  

Product Demand Insights 

Product demand is one of the most important insights that retail and eCommerce businesses can pull from their retail data. Besides highlighting the inventory your customers have and are purchasing, it also gives you an idea of what buying behaviors you can expect in the future. This information makes it possible for you to price your products competitively and ensure that you always have the right amount of inventory on hand.  

However, analyzing retail sales data offers a lot of value to other teams and departments, as well. Here are two additional ways you can use product demand insights to strengthen your brand.  

Target Your Marketing  

Product demand data allows you to see a few different angles of your customers’ shopping behaviors, including:  

  • What products they like to buy 
  • Where they make their purchases 
  • What channels they spend the most time on 

This is incredibly valuable to retail and eCommerce businesses because these three data points together offer a framework for personalized marketing. Not only do they help you create targeted offers that your customers might be interested in based on their previous buying behavior, but they also tell you which channels and mediums to share those marketing messages on.  

For example, if the data shows that your customers are buying summer clothing from your eCommerce store, you might send them promotional emails for a new line of sandals from the same brand or share a digital ad for sunglasses on their Instagram feed.   

By providing targeted offers like this that cater to your customer’s interests, you can improve the customer experience and simultaneously increase sales and revenue.  

Improve Fulfillment 

While product demand is critical to inventory planning at individual fulfillment centers and stores, it also has an impact on your fulfillment methods.  

For instance, if you’re dealing with heightened demand for delivery, but the only products available in a given area are located in brick-and-mortar stores, you might need to adjust your fulfillment methods to incorporate ship-from-store and employee delivery.  

On the flip side, if your customers want to purchase a product in-store — but you currently only offer it online — you might use in-store and curbside pickup options to meet the customer halfway.  

Regardless, product demand insights can help you deliver the experience your customers are looking for by showing you where adjustments need to be made. And, if done right, that can result in higher customer satisfaction levels and long-term customer loyalty.  

Inventory Management Data  

Inventory management data is another valuable data set that retail and eCommerce businesses can extract from their omnichannel order management system. It covers current inventory levels, stockouts, shrinkage, and a variety of other metrics.  

Being critical to product sourcing, inventory management data is nearly always used by retailers to minimize stockouts and overstock across the organization. But there’s another use for inventory data that you don’t want to overlook. 

Shorten Order Cycle Time 

Since customers expect quick order delivery (usually 1-2 days), eCommerce stores need to make sure their ducks are in a row every time. Products need to be available for purchase on the right channel, and they need to be in a convenient location.  

By showing you where your products are available and how much you have, inventory management data makes it possible to keep all those moving pieces perfectly aligned. At the same time, predictive inventory data can help you select a fulfillment location and method that will get the order to the customer the fastest.  

As a result, you can deliver orders on time — which can both increase customer satisfaction levels and earn you more word-of-mouth referrals.  

Customer Experience Data 

Unlike other types of retail data, customer experience insight isn’t found by analyzing retail sales data. It’s offered by the customers, themselves. You can collect it from customer support emails, phone calls, SMS messages, and live chat conversations. Customers can provide it directly via Net Promoter surveys. And your team can see it in the quarterly customer churn reports.  

Use cases for customer experience data stretch far and wide across every aspect of your organization. In fact, it can reach into aspects of your business you might not have considered.  

Offer Better Customer Care 

Rather than guessing, your customer service agents can use customer experience data to respond to your shoppers’ questions and concerns. They can meet them on their preferred channels (like social media or live chat) and provide personalized support throughout the customer journey.  

This ensures that your customers can get the information they need more easily. And since 92% of customers are likely to buy from you again if they have a good customer experience, that means more loyal customers for your brand.   

Simplify Payment Portals & Improve Fraud Protection 

Whether your customers have been exposed to fraud or are simply frustrated with your payment process (which exists to protect them from fraud), you can use customer experience insights to tell you what needs to change.  

You might simplify your payment forms to minimize the amount of friction your customers experience during checkout. Or you might swap out your fraud protection tools for a service that uses AI (machine learning) to proactively prevent fraudsters.  

In any case, by reviewing your customer sentiment, customer satisfaction, and loyalty data, you can create a payment process that is ideal for everyone involved. And that means more sales and happy customers.  

Tackling big data in retail is easily one of the most challenging tasks today. Not only is the amount of data overwhelming, but many retail and eCommerce businesses don’t know how to capitalize on it. Luckily, by thinking outside the box, you can make every retail data point valuable to your business — from marketing and fulfillment to customer care and beyond.