Strategies to promote self-service for post-sales support

Prosenjit Sen- portrait - Nov-2022

Prosenjit Sen

In my first article in this series, What B2B must learn from B2C, I discussed how, despite the great promise of B2B commerce, this space faces significant challenges for both pre-sales and post-sales due to the limitations of existing technologies such as “search.” In the second article, I discussed how you can Skyrocket product discovery on your B2B site using the latest AI/ML technologies. In this article, I’ll take a look at how you can provide world-class post-sales support to keep customers happy—at a low cost.

Chatbot technology has evolved where you can have an “unlimited conversation” with the user.

blog-ProsenjitSen-Series3-TiersToday, for post-sales, you probably have tier 1 and tier 2 omnichannel support, where your support staff chats with customers live or talks to them on the phone to understand their issue, and then looks to your reference documents for answers. to help the customer. .

Often, if the problem is complex, they create a ticket and escalate it to level 3 support. For such complex problems, a lot of time is wasted with L1 or L2 support trying to investigate the problem first, before escalating it.

Technologies to consider

In Myself last article. in search of products, I discussed some of the latest technological advances in deep learning, natural language processing (NLP), and computer vision (CV). With such technologies, it is now possible to support a chatbot or voice-bot with instant responses directly from their reference documents, taking self-service to the next level and making it a viable tool for post-sales support.

blog-ProsenjitSen-Series3-sales representative-summaryChatbots have traditionally used 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 where you can have an “unlimited conversation” with the user, using responses directly from a company’s reference documents. Such Chatbot 2.0 can be used to automatically handle a significant portion of support issues.

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

Another self-service might be to automatically interpret a customer’s email or ticket about a support issue to identify the problem discussed, get responses from reference documents or back-end systems, and then automatically reply to the customer if confidence in the responses is high. tall. Once again, deep learning and NLP now make it possible.

Use cases

after sales purchase

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

The technology for this strategy involves the ability to derive answers from structured, unstructured, and real-time reference data; I talked about this in my previous article on product discovery use cases 1 and 2.

The customer has questions about installation and configuration

Instead of L1 or L2 support staff spending time on these queries, it is recommended that you use a self-service chatbot or voice-bot. Please see my previous article, use case 3.

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

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

This is an inefficient and time-consuming process, as many customer tickets often go unattended. Using the technologies As I mentioned, you can automatically interpret a customer ticket to identify the problem topics discussed, then get the correct responses from the referenced documents so that when the support agent opens a ticket (in Salesforce or ServiceNow), they see the responses ( specific sections) of different documents have already been raised for them. This boosts your productivity, resulting in reduced MTTR and fewer unresolved tickets. As a result, you will have happy customers and ensure their loyalty. (MTTR stands for “mean time to resolve” customer service incidents.)

Help with order processing

After the sale, your customers will occasionally need to know the status of their order, its estimated arrival, or to change or cancel the order. They may also need to see if any other products associated with your 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 conveniently; This is very important to retain customers.

Junk mail

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


Use automation throughout the support lifecycle: pre sales and aftermarket (discussed here). Keep your customers happy and informed with accurate information when they are looking to make a purchase and even after the purchase. If you use the right AI technology, you can often do it without spending a lot of money, and ROI should be achieved within six months.

In the next article, I’ll discuss how to provide the fastest route to purchase on your site and where support is headed in the coming years, given continued developments in AI/ML technologies.

About the Author:

Prosenjit Sen is a serial entrepreneur and is currently the CEO of, an “autonomous support” platform that uses deep learning, natural language processing (NLP), and computer vision (CV) to bring automation to sales support and field support. He was previously employee number 5 as part of 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 Startups Accelerator. And he is co-author of the book “RFID for Energy & Utility Industries”.


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