Return on investment (ROI) is a good thing for any business, but unwarranted product returns and exchanges bleed precious cash from your coffers. Reverse-logistics costs caused by product returns account for 0.5 percent of the GDP (Source: IGI Global publication on Industrial Engineering) – or approximately $93 billion in the US alone. In the UK, transport costs for returns processing are estimated at 4x that of outbound deliveries, according to Global Trade Review. Unwarranted product returns fall into the category of “controllable” returns and exchanges, or “No Fault Found” (NFF) in manufacturing parlance.
When a customer calls the business to return something, contact-center agents or websites can help avert the return and keep the product “sold” by effectively resolving the issue or guiding the customer through product setup and use. Applicable to many industries, this solution is especially relevant to businesses in the wireless, retail, and manufacturing sectors.
Based on our experience working with leading companies in these sectors, we know that AI-infused knowledge (or simply “AI knowledge”) can help reduce not only returns but also unwarranted field visits. In fact, this approach can even go further by improving first-visit resolution and streamline returns processing when such visits and returns do become unavoidable. Additionally, it can turn a disappointed and dissatisfied customer into a brand advocate.
1. Implement AI knowledge for agent-assisted returns avoidance
While some contact centers have implemented knowledgebases over the years, they have achieved limited ROI from these systems, often due to a “one size fits all” approach. Agent churn is a common phenomenon in contact centers and, in the case of Millennial and Gen Z frontline staff, it has worsened. Contact centers will always have a mix of novice and expert agents, who have different learning styles, different levels of expertise, and different knowledge access preferences. For instance, expert agents may prefer to “search” for answers and quickly process hundreds of “hits,” while novice agents may fare better with more hand-holding – for example, a conversational help system, guided by AI reasoning. Likewise, keyword search or topic-tree browsing may work well for customer queries of low-to-medium complexity, while guided help with reasoning is more useful for queries of medium-to-high complexity. Returns-related queries tend to fall into the latter group. Consider these scenarios as you look for a new knowledge management system or upgrade an existing one.
Telco example: Most mobile operators have a policy of letting consumers exchange their “faulty” handsets for new ones free of charge, an expensive value proposition. A leading mobile operator client in Europe uses AI reasoning technology for contact-center agents, enabling them to effectively resolve subscriber queries and reduce unwarranted handset exchanges by 38 percent. The availability of primary access methods such as search and more intuitive and advanced methods such as AI-guided troubleshooting enable contact centers to replicate the performance of their best agents across virtually the entire agent pool — to scale the effectiveness of problem resolution in general, and returns avoidance, in particular.
Another mobile operator leverages this capability in an omnichannel fashion across 10,000 contact-center agents and 550 retail stores, boosting First-Contact Resolution by 37 percent. In fact, the use of AI knowledge improved the performance of even their best agents.
2. Leverage digital self-service
Extending AI knowledge to digital self-service can help drive down costs of returns avoidance by empowering customers with self-service. However, the key to success is implementing a common omnichannel knowledgebase to ensure consistent answers and AI-guided resolution across both self-service and agent-assisted service. Knowledge consistency also helps reduce the “dialing for dollars” phenomenon. This occurs when consumers sometimes use multiple interaction channels (or contact multiple agents in the same channel) to get the “deals” or “answers” they want, which could be “ship it back and we will simply replace at no cost, no questions asked.”
3. Harvest knowledge
Consumers, especially power users, often post tips and tricks on product usage and answers to FAQs on online forums and social networks. Contact centers can “federate” this knowledge by including them in search results on their website. Showing the knowledge source and trust level clearly alongside the search result is a good practice. The next level of leveraging this collective expertise would be for businesses to “harvest” — i.e., crowdsource, scrub, and publish – this knowledge on their website and make it available to agents. This not only increases the odds of returns avoidance but also helps drive down the cost of knowledge creation.
4. Optimize field service
Field service is the second layer of defense to reduce returns, and this approach includes two tactics:
- Field service avoidance: In the case of big-ticket items like large household appliances (e.g., washers, refrigerators, etc.), manufacturers and retailers can reduce unnecessary “truck rolls” or field visits through effective problem resolution by contact-center agents and digital self-service. Again, agents, empowered with AI guidance, can help resolve returns-related queries more effectively.
- First-visit resolution (FVR): In instances where a problem cannot be resolved in the contact center, a customer service system that unifies engagement, AI knowledge, workflow, and analytics can increase the odds of FVR by suggesting the right spare parts and tools to field service technicians, leveraging AI and context.
Branded manufacturing example: A premier home appliance manufacturer in the U.S. uses AI-guided problem resolution to reduce unwarranted truck rolls. In the process, the company has been saving an average of $50M every year for the last eight years, generating killer business for the company.
5. Streamline returns processing
When all else fails, and the business must handle returns, a unified cross-channel, cross-agent, cross-system approach works best, where interaction channels are not only unified with one another but also with backend systems (e.g., order management and supply chain). This enables the streamlining of returns and contact-center agents and self-service systems have a complete view of the process. Preemptive, unified, cross-channel alerts (e.g., email, voice, and SMS) on the status of returns, shipment of replacement products or refunds, can help reduce the number of status-related customer queries and associated costs.
In short, AI knowledge can work magic for any telco or manufacturer-it elevates returns on AI by eliminating (or reducing) returns.
