From customer exasperation to intelligent support: Part 2

Click here for part 1

Is a shadow ‘department of diagnostics’ hiding inside your engineering and maintenance teams?

Sounds like the right place to exist in a hospital, not in an industrial customer support team, right? I bet you agree. But what do you call it when two highly skilled people of your design team, and a bunch of maintenance engineers and a few client servicing personnel are working on understanding one thing – what went wrong in the machinery house of your dragline excavator? Sounds familiar but doesn’t sound right at all.

Remember that error code and logs we were talking about? Let’s forget that for a moment, and let’s imagine that the project manager at the site managed to click a picture of something that looks burnt in the machinery house. He sent it to your dealer, and that makes its way finally to to the maintenance teams, and that’s when they finally figure it was simply a radiator malfunction.

Guess what? An exactly similar issue came up a couple months back, but with a different team. Both ended up with a ‘eureka!’ but the company probably lost money taking up so long to figure out the problem, while increasing the ticket backlog. What if five such tickets showed up in a row? Then we are talking delayed service, angry voices, loss of money, and customers too. A 360-dergee impact, isn’t it?

Increasing capacity at the service stage doesn’t really sound like an option, when the quality of service decreases exponentially with volume of issues. But consider a scenario where you didn’t need an engineer to look at the picture to identify the radiator malfunction. Then, your CRM application could directly notify the nearest maintenance engineer about the issue, while he would be on his way to the site in minutes. Sounds revolutionary? Let’s see how this is possible.

Making your customer support intelligent

For many, this is a reality today. Enterprises identify the following benefits as the most impactful, as a result of using AI in the context of B2B customer service:

• 24x7 live support: AI allows small customer support teams to work with high volume of tickets more efficiently. It makes space for automatic resolution of low complexity issues, relevance ranking and risk assessment of tickets. This helps support teams prioritise issues associated with bigger accounts, higher risk, and greater complexity in an efficient manner.
• Improved searchability – talk instant resolution: By making use of your enterprise data from all channels, AI powered solutions can empower your support teams with the right information instantly. Coupled with intent extraction, support teams dealing with a complex product-space can actually deliver on a promise of instant resolution in real time. Want to take it to the next level? Deploy such a solution on a curated knowledge base to enliven a self-service feature for your digital-savvy customers.
• Cost savings: Companies are operating with tight budgets and dealing in a low-liquidity environment globally. Therefore, improving your customer experience calls for a strategy that can lower your costs too. Chatbots alone are expected to save $8bn for businesses by 2022. Imagine freeing your support teams of 70% of the ticket volume, and 100% of the frustration when a particular file just cannot be found. 

AI is revolutionizing the way customer support has been approached by companies. In B2B, where complexity associated with products is much higher, taming the mess with AI sounds like a perfect solution. Most B2B companies, including mid-sized (‘mittelstand’) are unclear about what AI can do for them, and mislead about the costs of deployment, continuity in the transition process, etc. It's not that complicated at all, and magnitudes cheaper than the support teams we talked about earlier