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.
Let’s start with a moment we’ve all experienced. You’re having an issue with a company’s product. Whether it’s your cell phone, your bank account, or Starbucks mobile account. It’s safe to say you’re not happy, and you’re definitely not over-the-moon-excited to call or chat customer support. You probably don’t have much time, and you’re picturing yourself being shuffled from automated recording to automated recording, eventually screaming into your phone, “Speak to a rep-re-sen-ta-tive!”. Long story short, you want your problem solved as quickly and painlessly as possible.
It's the difference between a map and directions, between a recipe and a meal, between sheet music and a song. That's the difference between search results and a real answer to a question, and that difference matters a great deal to customer support teams.
We've written previously about why allowing AI to capture the "tribal knowledge" of your customer service teams is critical to deriving value from your artificial intelligence solutions, but the value of this practice goes beyond just optimizing customer service. Allowing AI to capture and quantify your "unwritten rules" has wide-ranging benefits for your entire business.
According to Forbes’ Predictions For Customer Service In 2019, the two most important things for customers today are convenience and speed. With that being said, the prediction that 2019 will be a record year in AI investment for customer support comes as no shock.
Many artificial intelligence solutions promise to make your customer support teams more efficient and effective, but how do you separate the vaporware from truly valuable AI-for-CS products and services? You use The PAC Framework for Customer Support Teams eBook
The PAC Framework is an AI evaluation tool developed by Talla to help you determine what tasks artificial intelligence software can actually perform, as opposed to what marketing spin and pundit hype suggest is possible. The PAC Framework is based around three types of AI skills: prediction, automation, and classification. These three things are what modern AI software is actually good at.