A big source of business for us at Talla is failed chatbot projects. Building chatbots for customer support is hard because most of them are scripted. You have to go in and do some regular expression matching and then label language intents, and then map those to a workflow. It's a lot of work to create an interaction, and then the bot doesn't really learn much once it is deployed. As the world changes and new support issues arise, you have to go do a bunch of work to add those new issues.
"Inbox Zero" is all the rage for personal productivity, but the equivalent for your support team would be "Backlog Zero" -- and it's achievable with artificial intelligence.
There are a lot of companies that want to use AI, but just don’t know where to start. 85% of executives believe that AI will give their business an inherent advantage (MIT Sloan Management Review and the Boston Consulting Group), however, of the half of CIOs planning to use AI, only 4% have actually implemented the technology (Gartner).
"You never get a second chance to make a first impression" isn't just a truism; it's a warning about deploying customer support chatbots before they are ready. If you prematurely hand off customer interaction to an artificially intelligent chatbot before that chatbot has been properly trained, you can do far more harm than good.
For those trying to introduce AI projects internally, generating buy-in requires more than facts and figures or trusting that the AI mystique will sell itself. As with any software sale, there will be an objection handling effort. It’s likely no one in your company has had to navigate this with an AI implementation yet, so you’ll find little internal advice. To help, here’s a brief look at three objections you’ll face. Included are insights on how to overcome each.
Customer support leaders are generally receptive to new technology that is faster, cheaper and more reliable. What they aren’t onboard with is overhyped tech that promises everything. It’s why AI gets met with eye rolls inside some offices. No one believes there’s a panacea for every support ail. Yet everyday unscrupulous marketers boast of AI as a cure-all. Often, they don’t even have true AI, just basic scripts or data science techniques. They promise big and invoke “AI” in their marketing to stay relevant. These products disappoint and it’s no wonder why the real opportunity of AI get lost as leaders grow wary of dubious claims.
For as long as there are businesses with customers, there will be a need for customer service teams -- but what your customer support organization will look like in the future will be heavily influenced by artificial intelligence and software automation.
Case deflection will be far more proactive in the future, as automation software will monitor customer usage of your products and services and detect problems as the happen -- perhaps even before the customer notices a downgrade in service or performance. This automation system will reach out to the customer itself -- via chat, email, autmated voice calling, or contacting their smart-speaker -- to advise on how to solve the problem without needing to search for advice in your FAQs or contact your support team.
Support case deflection is the art of empowering your customers to troubleshoot their own support issues without having to actually engage with your customer support teams. While case deflection is inarguably a cost-saving practice, it isn't an optimal experience for your customers. The real goal should be case prevention -- solving your customers' problems before they even need to contact you.
If you are looking to deploy a chatbot for support and have started to investigate the market for chatbot providers, you've probably been left with your head spinning trying to understand how they are different. Chatbot companies have different ways for building bots (how much is it out of the box ready, vs designed by you?) and different places they can deploy (website, facebook, slack, etc). This post is going to look quickly at the AI, or, in some cases lack of AI, that these bots have. In general you can divide them into one of 3 camps.