Mention the name “L.L. Bean” and images of the outdoors to mind. “High quality” might be another response. Long-time customers would also mention their customer service and return policy that remains generous despite recent changes.
It’s that attention to quality and service L.L. Bean’s loyal customers would probably praise most. It’s been a long-time strength of the brand. So it’s equally surprising and satisfying to read this account from The New England Historical Society. It details how the Bean Boot–the very genesis of the company–nearly destroyed the company out of the gates: ninety of the first one hundred pairs were returned when the leather upper separated from the rubber sole. Not only did the company stand behind its money back guarantee, it went on to perfect the boot that anchored its rise to fame.
Over one hundred years after L.L. Bean’s boot debacle, companies still experience the same product quality issues and may struggle to respond. The terms have changed somewhat: customers are mobile, connected, and have ever-increasing expectations for service. The good news is that new methods and technologies like digital workflow, automation, and machine learning aid companies to better respond to these challenges. Consider how L.L. Bean might address the same situation in the twenty-first century.
Digital workflow is the connective tissue that not only helps orchestrate a fast response but also provides visibility throughout the resolution process. In the L.L. Bean example, there is a ninety percent failure rate in their product. Real-time analytics in customer service would quickly identify this trend. Using workflow, customer service would then quickly notify other teams, share case details, and collaborate across the company to formulate a response; for example:
- With Finance and Billing, to make them aware of the potentially higher number of credits and exchanges that will occur
- With Sales, to temporarily cease selling boots both to individuals and any distributors or other retailers
- With Shipping, to stop processing any orders to customers and to expect returns
- With Research and Development, to provide the details of what caused the failure and customer contact details if additional information is needed
- With Manufacturing, to effectively stop producing the defective boots until R&D has developed a solution
These are just a few departments involved in addressing such a scenario. The good news is that workflow not only ensures collaboration, it drives a faster response than more manual means like spreadsheets and email (or assistants running from place to place in 1911). It also provides visibility into the timeline for getting the business back on track.
Proactive customer service
L.L. Bean must address the boot’s failure and honor their product warranty. They also have the opportunity to do more for their customers: to alert those unaware of the problem.
L.L. Bean has contact information for all its customers–individual, distributors, and others–who have purchased the boot. This is a matter of isolating those customers, notifying them of the issue, and that a fix is coming. When that solution is available, they would again inform customers.
This results in two benefits. The first is it provides a better experience for the customer: they won’t encounter the boot failure stomping through the wilderness or the urban jungle on a cold, wet day. It also means they won’t need to spend time contacting customer service if they have experienced it.
The second benefit is it reduces the impact on customer service. A large volume of telephone calls, chats, and emails is avoided. This allows customer service to focus on other customers and issues.
Companies have continued to invest in self-service channels because customers often look to them first for assistance. This scenario provides ample opportunities to tap into the strengths of self service.
A knowledge base article would effectively answer customers’ common questions about the boot problem. Under what conditions does it fail? Are only certain boots affected? What is the current status of the resolution? What can be expected in the return and exchange process? As new developments occur, the article can be updated.
Not all customers are comfortable or successful searching a knowledge base. This is a great opportunity to use a chatbot. It would respond to these same boot-related questions in a more conversational form.
When new boots are available, an automated process (driven in the background by workflow) would assist customers with the return or exchange process. Responding to a few questions, it would tackle everything from creating a return label to processing a return order to providing tracking details on the replacement boots.
Guided solutions for agents
The other route customers might choose is to contact L.L. Bean through a live customer service channel, such as telephone or chat. Modern customer service platforms today set the agent up for success to assist these customers.
Machine learning would identify that this customer has purchased the problematic boot. This would help set the stage for the interaction, preparing the agent for the likely reason the customer has contacted L.L. Bean.
Machine learning would also recommend the next best actions to propose to the customer. Depending on the timing, it might be to refer them to that knowledge base article that explains what is going on with the boot’s redesign and the timeline. If a fixed model is now available, machine learning might suggest the agent follow the steps in a step-by-step playbook to facilitate the return of the faulty boots and process a replacement order.
The path to greatness
In spite of its rough start, L.L. Bean not only recovered from that first product failure but went on to become a legendary brand in outdoor products and customer service. Those successes were no doubt built on additional lessons from errors in design and manufacturing. L.L. Bean has persevered, with customer service that stands behind the company’s products.
Unlike the fledgling outdoor outfitter in 1911, businesses today are better equipped to quickly respond to similar product challenges. The use of modern customer service processes and technologies can help address issues quickly, deliver the best possible customer experience, and build loyalty that might one day rival even the likes of L.L. Bean.