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Overview

“Sell to your consumers wherever they want to shop” was the philosophy behind much of the current expansion in retail. Store and ecommerce channels were designed to make buying easy. Receiving and processing returned merchandise, however, was a channel-focused procedure that historically has yielded little insight to the whole business. The logistics of handling the returned merchandise took priority over data integration.

This has created a situation in which processes are taking priority over consumer relationships, CX, and future growth. While logistics are important, retailers need a broader view of the impact returns have on the whole business in order to grow profits in today’s omnichannel retail environment.

Holes in your data drive up costs and lower satisfaction

Now that ecommerce has grown to be such a large portion of a retailer’s revenue and costs, many have discovered that their current business models are not delivering sufficient profit.

If you look at your channels you will probably discover that each channel has determined its own data requirements for a sale or return. These channel-specific decisions create inconsistencies in the consumer experience, the data collected, and in the types of data retained in your organization. It is an integration nightmare; even simply “recognizing” a loyal consumer is difficult because of differences in data collection across channels.

Now that ecommerce has grown to be such a large portion of a retailer’s revenue and costs, many have discovered that their current business models are not delivering sufficient profit.

For example, imagine a consumer using a credit card to make a simple, one item purchase in each of your physical and digital channels. Easy. Now image making a return. Not so easy. Even just knowing whether the consumer qualifies for a return can be a challenge. Your brick-and-mortar stores may require a loyalty card, credit card, or other ID such as a driver’s license to authorize a return. Meanwhile, digital channels may need an account number or some other identifier that is part of the online purchase process in addition to the credit card number. How do all your systems “recognize” the consumer so you can create a holistic relationship?

Now amplify these difficulties because your channels may have different products, SKUs, pricing, taxation, or return policies. A buy-online-return-in-store situation, in which the store doesn’t have visibility into any ecommerce purchases, either frustrates the consumer because the return can’t be authorized, or it introduces risk because the retailer must operate on trust until the systems can be reconciled. Unfortunately, the process for making an online return can also be disheartening.

Using data and AI to cut return rates and recognize valuable consumers

A recent study by Incisiv showed that 73% of returns were due to factors the retailer can control. An analysis of your ecommerce and store return reasons can show you where to improve. So can looking for items that have low returns in one channel and very high returns in another; this may indicate that the item should not be sold in all channels. For example, a line of fine crystal goblets may not be factory-packed to survive the extra drops and bumps from ecommerce. Making the goblets available for in-store pickup only is an option that could lead to happier consumers and healthier profits.

By bridging the gap between channels and sharing the data, you can create a holistic view of your consumer and their interactions in all your channels.

Data analysis can help you address individuals’ behaviors, too. By bridging the gap between channels and sharing the data, you can create a holistic view of your consumer and their interactions in all your channels. Artificial intelligence can be used to review all transactions from all channels and segment your consumers based on their behaviors with your brand and enable you to respond appropriately. You may want to offer targeted incentives to consumers who are in the habit of shopping with you frequently. Negative habits like overbuying and returning the extras can be discovered and addressed through personalized messaging or dynamic policies. Intentional losses—like ORC activity—can be isolated and prevented when artificial intelligence is used to understand in real-time where the activity is taking place and by whom.

Through intelligent, personalized interactions, you can reduce your return volume and make consumers with returns more profitable.

Omnichannel shopping and returns place heavier demands on real-time insights

Consumer experiences such as checking inventory availability, placing BOPIS orders, visiting the brick-and-mortar store for a BORIS transaction, tracking a shipped return, and more are improved with up-to-the-second information. Real-time omnichannel data helps your associates and consumer service representatives provide quick, quality services; it also helps them deter attempts at return fraud.

Data integration: The best way forward

The consumer’s path to purchase has changed. You need to change with it by using data from all your channels to support the end-to-end experience.

The shopper’s journey now often includes returning one or more items. The huge influx of returns has retailers like you grappling with high costs and disappointed consumers. The solution is to prevent the need for most returns through analytics and action. That is the long-view, best case scenario. While managing logistics can solve some problems with your returns, it does not help you address root causes.

Instead, when a return is made, use the information you have about the consumer to make the transaction a smooth and positive experience no matter what channel was used for the purchase or return. To achieve consistency, you need to pair machine learning or other AI-powered solutions with real-time omnichannel data. Then you can maximize the margins from your inventory, curb outlier behavior, and enhance the consumer experience in ways that lead to millions in benefits year after year.

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Author

Carrie Cassidy, Director, Marketing, Appriss Retail

A technology advocate for more than 25 years, Carrie makes information about advanced data analytics solutions accessible to retail professionals through a variety of media. She has written numerous white papers, case studies, and articles for a variety of industries ranging from motion control to human resources.

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