Five Strategies for the Intelligent Automation of Your Post-Sales Service
Artificial intelligence (AI) is no longer science fiction; it has now arrived in all areas of business, including post-sales service. 78 % of all brands say they currently use technologies based on artificial intelligence and virtualization in their customer service. This isn’t particularly surprising, as there are now numerous innovative concepts and ideas for intelligently automating processes in after-sales and customer service.
For many companies, however, the challenge lies in deciding which of these numerous solutions on the market genuinely work for their specific business case. By “work”, we mean the solution is easily scalable in post-sales and leads to tangible value add that is reflected in more satisfied customers and better results.
In this blog post, we’ll provide six examples of concrete use cases with practical AI-based solutions that are already being successfully deployed in post-sales service today.
1. The AI Chatbot That Thinks
Do you know the business areas for which artificial intelligence is most important? Marketing, sales and service! 40 % of all departments say they already rely on artificial intelligence to achieve their goals. One of the reasons for the technology’s success is chatbots, which are already utilized in customer service in 80 % of all companies, according to a recent study.
As a general rule, these chatbots answer and classify initial queries before they’re forwarded to a service agent for further processing. The benefits of chatbots are crystal clear: they respond to simple customer queries faster and more accurately than human employees, while also giving customer service employees time to handle more complex cases.
However, intelligent chatbots can do even more. With AI technology such as augmented messaging, customers with problems are automatically forwarded to human agents using intelligent criteria during the interaction, who can then process the case further or return it to the bot. To do this, intelligent bots use various training data to recognize whether they can handle a customer query with their own in-house resources or whether the problem is such that the customer needs to be forwarded to an employee. For example, the latter can occur if the customer wants to resolve a more complex issue or reacts emotionally to the bot. This way, using chatbots in customer service can be scaled in an extensive and precise manner while simultaneously boosting customer satisfaction.
2. The AI Supply Chain That Organizes Itself
Organizing the supply chain is one of the biggest challenges associated with post-sales service. In particular, global companies have to weigh up the costs and benefits very carefully in order to supply their customers worldwide with spare parts. On the one hand, stocking spare parts must cover costs; on the other hand, customers expect rapid replacements if problems arise.
The process of ordering accessories is generally designed as a reactive system intended to remedy faults. It’s activated when a fault has occurred and been initiated as a service case by a customer. Artificial intelligence makes this system smart by incorporating the wealth of data already collected by numerous sensors on machines and devices into the supply chain.
This data allows your service team to track device operating times and downtime in real time and process it to detect anomalies and faults. This data evaluation uses machine learning models that can make proactive predictions about error messages and downtime that occur using predictive analytics. Based on this, the supply chain can be built much more effectively while the ordering of accessories can be made “touchless”.
This way, an intelligent supply chain not only enhances the customer experience in post-sales service and boosts the efficiency of warehousing; it also works more accurately over time through scaling and the subsequent influx of new data.
3. The AI Customer Service That Speaks Every Language
Global hardware manufacturers not only have to establish a complex supply chain; they also have to adapt their customer service to the cultural challenges of a worldwide customer base. One of the most important issues here relates to the problem of language barriers. Customers appreciate being able to communicate with a supplier in their native language. Yet not all global brands can offer customer service in every language.
Artificial intelligence is already providing viable solutions to this problem for post-sales service. To this end, it makes use of natural language processing (NLP) – a process in which human language can be automatically processed and applied with the help of machine learning. This way, you can train chatbots to answer simple customer service queries in the local language and only forward customers to another source if more clarification is needed.
4. The AI Program That Replies to Emails Itself
To “firstname.lastname@example.org”: many customers still send queries to a manufacturer by email. They generally use the email address they first come across in their search. In the best case, this is an email address that is linked to the desired department, but in the worst case, these queries land somewhere else in the company.
But even if a customer query lands in the right email inbox, it still has to be read and, in many cases, manually forwarded to the right person in customer service or sales. If this isn’t the case, it results in delays and a poor customer experience. In the worst case scenario, a complete order from post-sales may even be lost in the company’s inbox jungle.
AI-based email management methods are a practical solution to this problem. They combine machine learning with NLP methods and are capable of automatically forwarding incoming emails to the relevant service center and even answering them in an automated way as far as possible. To enable this, the AI software learns to understand customer queries based on the incoming emails and, with the help of an NLG engine (natural language generation), formulates the answers in context so that they are personalized for customers and sound authentic.
5. The AI Powered Claims Management with 24/7 Availability
Claims management is one of the central tasks in post-sales service. The process ranges from recording the damage report, to organizing the repair, to returning the goods to the customer. Manufacturers are faced with the challenge of setting up a process that is well structured, functionally and economically, while offering the best possible customer experience.
Artificial intelligence offers various starting points for taking claims management to a whole new level:
- For example, intelligent algorithms can be used to set up troubleshooting workflows in such a way that the customer is guided interactively through a process via voice or chatbot. The bots extract the necessary data from the spoken or written dialogue and use it to create an automated claims report that can be processed further internally without disruption. The AI bots are trained to obtain all the necessary details in dialogue with the customer and learn to evaluate information correctly for further action.
- In addition, the customer can immediately receive a Return Material Authorization (RMA) number and initiate claims processing immediately.
- After returning the material, the customer can check the delivery status of the shipment via the AI bot.
- For more complex repairs or other service matters, the customer can interactively arrange an appointment with the voice or chatbot – for example, for a pickup in a service center or retail shop. In the process, telephone AI bots enable a high level of convenience such as call acceptance including callback.
At B2X, we integrate AI into our ONE Technology Platform to efficiently and scalably map service and logistics processes for our customers. Discover more about B2X ONE and how we can help your company to optimize its post-sales service.