After-sales service supplements the bond between manufacturers and users of their products. Attending to a customer after the sale is completed involves various processes, including installing the product and assisting the customer in using the product correctly.
By following up with buyers, manufacturers nurture brand loyalty while ensuring satisfaction. Consumer contentment remains a vital concern for organizations, as customer retention and conviction will lead to greater productivity and increased sales turnover for the organization.
Manufacturers are however increasingly faced with a multitude of challenges that they must overcome in order to provide after sales service to their buyers.
1) The call center as a point of contact for customers is becoming obsolete and expensive as good people with the necessary communication skills are becoming hard to find. Large enterprises also have limited call center bandwidth which increases the wait time for buyers. Often, call centers may not have all the relevant information to log calls, and count on the consumer to provide necessary details, which leads to an additional waste of time, causing more frustration.
Emerging trends in communication and buying behavior are now giving rise to "contact centers," which are leaner, digitally-driven offspring of the once ubiquitous call center. Contact centers leverage AI and CRM tools such as virtual assistants, chatbots, machine learning and Natural Language Processing (NLP), which picks out keywords from an inquiry, and also derives meaning based on the context of the sentence. With these mechanisms in place, manufacturers may run a much smaller team of human agents to handle a growing number of customers.
2) Having access to customer databases, manufacturers are also ideally positioned to reach out to their existing users and engage them with personalized offers, enabling the cross-selling and upselling of allied products. This interaction can also lead to creation of newer product ideas, delivering tweaks and upgrades to existing equipment, and helping push newer product categories to create market awareness.
3) New and emerging technologies are increasingly delivering broader interactions with customers as they integrate AI, bots, messaging and human agents into one intelligent platform. But, most estimates assess that these new age technologies account for less than 10 percent of post-sales customer contacts.
New technologies like AI introduce complexity that cannot always be managed by small and medium enterprises. Automation techniques put more pressure on the ecosystem, requiring a lot of backend infrastructure like data analytics, filters and personnel to assist and coordinate these technologies.
Surprisingly, even large enterprises face similar problems as they are not always equipped to comprehend and deal with the intricacies of AI, machine learning and deep learning. The data generated is hence raw and rudimentary, and cannot be used for any in-depth and productive analysis, as the organization has neither the expertise nor the tools to extract and manage the data.
Most early AI applications are only beginning to be tested in businesses, and clear ROI is not yet extensively proven for most AI applications. In reality, there exists a huge gap between the data science (or AI) team and the IT teams that actually use the AI tools. The IT team is not familiar with the way to integrate the new solution into its regular workflows, causing frustration to all parties involved.
4) Consumer privacy also becomes a key issue with the use of these technologies. Talking to inquisitive chatbots is not really what most consumers like to do, as the AI tool does not always understand the customer’s problem, which further frustrates the user. At times, the chatbot may even seem to shrug the problem off.
An eGain Chatbot Survey 2018 survey among 3,000 U.K. and U.S. customers revealed that 53 percent of the respondents found chatbots to be ineffective or only somewhat effective. 59 percent of those who took the survey complained about the need to repeat information to an actual customer support agent after getting through the chatbot, while 22 percent reporting that “they are not intelligent enough” when asked why they didn’t like chatbots.
Enterprises will soon be mandated by law to tell customers they that are talking to bots. Regulators in countries are also getting concerned about the privacy implications of deploying these new technologies. Earlier this month, California Governor Jerry Brown signed into law a bill that requires bots to disclose when interacting with consumers (over the phone, online or otherwise) that they are not humans. The move is directed at protecting consumers and voters from deception by bots posing as real people. The bill would make these provisions operative on Jul. 1, next year.
The use of On-product QR tag placed directly on the equipment is an eminently viable workaround to the technological and privacy challenges in the growing market for automating customer contact.
Such QR Tag allow the buyer to make a simple, one-touch service request. At the time of product installation, the service engineer can activate these QR Tag and fill in the necessary product and customer related details. This helps to save time and hassle when making a service request.
Once the contact is made through the QR Tag, real-time notifications are made available to customers on the status of their service requests. This forward-thinking approach allows anyone to reach out for service and assistance by scanning an On-product QR tag installed on the device.