Become Amazon in Your Space with AI/ML: Strategies to Boost Self-Service for After-Sales Support

In my first article in this series, What B2B must learn from B2C, I explained how, despite the huge promise of B2B commerce, this space faces significant challenges in both pre-sales and post-sales due to the limitations of existing technologies such as ” research”. In the second article, I explained how you can Skyrocket product discovery on your B2B site using the latest AI/ML technologies. In this article, I will explain how you can provide top-notch after-sales support to satisfy your customers, at a lower cost.

Today for after-sales, you probably have tier 1 and tier 2 omnichannel support where your support staff chat with customers live or talk to them on the phone to understand their problem and then research the answers in your reference documents to help the client. .

Often, if the issue is complex, they create a ticket and escalate it to Level 3 Support. For such complex issues, a lot of time is wasted with L1 or L2 Support trying to investigate the issue first, before to climb it.

Technologies to consider

In my last article. on product research, I discussed some of the latest technological advances in deep learning, natural language processing (NLP), and computer vision (CV). With technologies like this, it is now possible to back-end a chatbot or voice-bot with instant responses directly from your reference materials, taking self-service to the next level and making it a viable tool for after-sales support.

Chatbots traditionally use a predefined decision tree to guide a conversation. They also had the limitation of relying on data such as FAQs or logs. This technology has now evolved and allows you to have an “unlimited conversation” with the user, using responses directly from a company’s reference documents. These Chatbot 2.0 can be used to handle a significant portion of support issues automatically.

Voice robot technology is also improving; however, even today the conversion of live voice (telephony) to text is often erroneous. But recent improvements in this space can give you around 80-90% accuracy if the AI ​​models for such conversion are trained with your voice data. This level of precision is a very encouraging sign. Soon it will be possible to interpret a live conversation between the customer and the support staff, identify what the customer is asking for and automatically extract the answers from your reference documents or back-end systems, such as than ERP control systems.

Another self-service might be automatically interpreting a customer’s email or ticket on a support issue to identify the problem being discussed, extracting answers from reference documents or back-end systems, then automatically respond to the client if the confidence in the responses is high. Again, deep learning and NLP make this possible.

Use case

After-sales purchase

Once a customer has purchased your product (air conditioners, heavy machinery, networking equipment, etc.), they should be able to easily purchase aftermarket parts and tools through self-service. If you have the parts and tools data on your website, you should allow customers to chat with a chatbot to specify their exact specs/requirements and get information about features, compliance, etc. — And if you also provide a fast path to purchase — this strategy will help drive sales and keep customers loyal and happy.

The technology of this strategy involves the ability to derive answers from structured, unstructured, and real-time reference data; I covered this in my previous post on Product Discovery, Use Cases 1 and 2.

Customer has questions about installation and configuration

Instead of L1 or L2 support staff spending time on these queries, it is recommended to use a chatbot or voice bot-based self-service. Please refer to my previous article, Use Case 3.

Customer submits a support case for a complex issue (in your Salesforce or ServiceNow system)

For complex products, there will often be complex issues that a customer needs help with quickly. They would typically create a support ticket in Salesforce or ServiceNow that would go to your support queue for the right support engineer to work on. The support engineer would usually open the ticket, read it, then submit a query using “search tools”, receive several documents, then open each document and read it. They do this over and over until they find the right solution. Then they may need to fix or reproduce the problem.

This is an inefficient and time-consuming process, due to which tickets for many customers are often left unattended. Using the technologies I’ve talked about, you can automatically interpret a customer’s ticket to identify the topics covered, then get the right answers from the reference documents. So when the support agent opens a ticket (in Salesforce or ServiceNow), they see the responses (specific sections) from different documents already checked out for them. This skyrockets their productivity, leading to lower MTTR and fewer unresolved tickets. Thus, you will have satisfied customers and ensure their loyalty. (MTTR stands for “average time to resolution” for customer service incidents.)

Order processing assistance

After the sale, your customers will occasionally need to know the status of their order, its estimated arrival, or modify or cancel the order. They may also need to see if certain other products associated with their purchase order are in stock or can be bundled with the order placed. All of this information is probably present in your SAP or other ERP system. You can provide customers with a self-service tool (chatbot or even voice-bot) so they can manage their needs quickly; it is very important to retain customers.

Advertising by e-mail

It’s also a good idea to develop a database of customers and prospects, then send them information about new product releases, personalized to the products they’ve purchased, and promotions. You can also have a “Technology” section in email communications where you can give them an overview of the latest technologies and developments in their areas of interest.

Summary

Use automation throughout the support lifecycle – pre-sales and post-sales (discussed here). Keep your customers happy and informed with accurate information when looking to make a purchase and even after the purchase. If you use the right AI technology, you can often do this without spending a lot of money, and the return on investment should be achievable within six months.

In the next article, I’ll explain how to provide the fastest path to purchase on your site and where support is heading in the coming years, given the ongoing developments in AI/ML technologies.

About the Author:

Prosenjit Sen is a serial entrepreneur and currently CEO of Quark.ai, an “autonomous support” platform that uses deep learning, natural language processing (NLP), and computer vision (CV) to automate sales support and field support. He was previously employee #5 on the founding team of Informatica, a pioneer in online data integration technology. Prosenjit is a mentor for the Alchemist Accelerator and the Bay Area IIT startup accelerator. And he is the co-author of the book “RFID for Energy & Utility Industries”.

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