Your new gadget didn’t arrive this morning from that big online retailer as promised, and you need to contact them. Where do you go first: a phone call? your laptop? Maybe less likely, text them? Ping them on Facebook, Twitter, Instagram?
Customers shouldn’t need to know how your customer support channels work to get help. They don’t need to know that Twitter feeds into your help desk and that your chat channel offers self-service solutions using a friendly chatbot. Customers, in other words, aren’t interested in your omnichannel solutions. What customers need is to be able to get help easily, when they want it.
Brainstorming sessions are challenging enough when everyone is sitting in the same room. Throw remote work into the mix and it can sometimes feel impossible to get a decent result out of it. Here at Infinite Red, we’ve spent a lot of time figuring out how to make brainstorming work well for us. We’ve developed a process for brainstorming sessions that we love – and, more importantly, our clients love.
Personalized customer service, and a personalized customer experience, means that a business 1) knows its customers and their needs, and 2) is willing and able to provide them as efficiently as possible. This is sometimes called personalization at scale: where software solutions help provide the context necessary for assisting customers in need or pointing them efficiently to what they want.
Landing new customers is a hell of a lot of work for B2B (business-to-business) software companies. Even the easy deals require extensive brand awareness efforts, targeted marketing, testimonials, sales conversations, proposals, and so much more. If there’s so much effort put in to acquiring a customer, then why do so many businesses stop caring once the deal is signed?
Machine learning in customer service is used to provide a higher level of convenience for customers and efficiency for support agents. Support-focused tools enabled with machine learning are growing in popularity thanks to their increasing ease-of-use and successful applications across a variety of industries. Gartner predicts that by 2021, 15 percent of customer service interactions will be handled completely by artificial intelligence.
An active user community within a help center is a great way for users to connect with one another, share ideas, and get answers to questions. Communities like these are helpful for companies scaling their support operations, because they allow users to get help from the people who most deeply understand their needs—their peers. Here’s how the Zendesk Community works and why it’s valuable in improving and enhancing the customer experience.
Consider all the different ways humans have come to rely on machines to expand their abilities. From calculators to cell phones, industrialization to robotics, humans have been able to achieve more than they ever have before, due to adopting machines. Machine learning is the next frontier in using machines to work more efficiently. And it’s particularly helpful in optimizing customer support. What is machine learning?
Social media is growing in popularity as a support channel. Customers love it because it allows them to get help right where they’re already spending time, and all companies should love it because it’s an excellent way to gain insights about your customers and cultivate brand loyalty. Paying attention to your customers on social media, also known as “social listening”, allows you to do a few key things that can help build lasting relationships with your customers.
Most customer service systems keep customer data siloed within each channel, i.e., in chat, phone, or email. A customer’s information and history with your company could be locked up in their chat history, but now that same customer is trying to solve their problem over the phone.
