You already know that AI chatbots didn’t turn out to be the next big thing. This doesn’t change the fact that more than 2.5 billion people are going to use messaging apps to communicate with businesses in 2020. For businesses to implement their conversational strategy across multiple channels, automation is necessary.
However, a learning curve still exists regarding how to open and scale with messaging apps. Thankfully, there are best practices and guidelines to help make the process easier.
Pitfalls to avoid when scaling your conversational strategy
Most companies overkill by diving headfirst into Artificial Intelligence. They end up spending thousands of dollars when there’s a cheaper alternative that will work just as well, if not better.
A drag and drop chatbot platform may be easy to use, but it has limitations. It doesn’t work well across multiple channels. And in most cases, it won’t sync with your CRM, or with any backend systems, which means you lose out on powerful insights. This is counterproductive, as creating a seamless customer experience is a key reason that companies adopt chatbots in the first place.
These approaches don’t work well for enterprise businesses. Instead, companies should focus on having a clear goal for how they’re going to use messaging and automation to help their customers. With well-defined use cases, it’s easier to develop specific solutions, and scale human resources and policies accordingly.
Get started with automation and scale later
Once you’ve defined your use case, start with Minimum Viable Automation. This is just enough automation to make things easier, not more complicated. Use decision trees and keywords to get started faster. You will use your MVA to gather information on how your customers want to use your channel. Learn from the limitations of your customer experience, and scale based on where you’re seeing the most opportunity.
Remember the distinction between automation and AI. Simple automation, like decision trees and basic keywords, is possible without introducing AI.
Our clients often find that rule-based bots are flexible enough to handle their use cases. AI works well for companies that have a lot of data, technological resources, and broad use cases. It takes time to get AI right. Once you have gained insights from your MVA, you can invest in AI for the next phase – if you decide that it’s right for you.
Replacing humans is not the goal of good conversational strategies. It’s important to remember that with any type of automation, it’s important to plan for a human handover. Combining bots and humans is key to managing customer expectations and providing great customer experiences at scale.