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With conversational AI maturing, a lot of organizations are warming to the idea of implementing bots that can understand natural language and subsequently engage and solve customers’ most frequent questions — just like human agents can. Contact center teams see a significant ROI as bots handle repetitive queries across various channels when the contact center faces high volumes of issues — and has limited agent resources. However, how well these bots perform in a real environment has always been a concern.
You’ve likely experienced it. You need help right now with a flight or a recent purchase or your bank account. You can wait for an agent, or you can use a bot. The advantage of the bot is it’s always on and always ready to answer your questions. Data shows that customers are open to engaging with a bot. Yet, despite the eagerness to engage, there’s still hesitancy.
Many customers have already had a bad bot experience. Bots are notorious for asking the same question over and over: I’m sorry, I didn’t understand that. Can you repeat it? Or there’s the less-than-useful: I’m sorry. I don’t understand.
Data shows that some customers see bots as a cynical attempt for brands to avoid providing service. They feel they’re a way to deflect contact permanently. So, while the brand might realize cost savings with using a bot to deflect calls, the brand might actually erode customer satisfaction and create detractors.
To deliver empathy at scale, companies can’t create barriers to service. Effective bots make customers want to engage. They work with customers — not against them.
And to create effective bots, bot builders must master these five key concepts.
The heart of a bot is intent management. When a customer communicates with a bot through speech or text, the bot applies natural language models to get to the meaning of the statement. Natural language can be complicated. We can pack a lot of information into a single ask; what we say doesn’t always convey what we mean.
Bot builders spend hours trying to come up with possible things customers will say to convey an intent, such as “order status” or “bill payment.” This can take a long time and it’s fraught with errors. Often, builders start with a small set of utterances (possible statements) and add more over time. Of course, that means the initial bot users are confronted with the dreaded I’m sorry I can’t understand you response from the bot.
The solution is to take a data-driven approach to intents. Using data from past interactions to understand how customers phrase their asks — and it gives you a view into what those asks are. This is becoming increasingly critical as the world moves online and the volume of customer service requests accelerates.
Taking a data-driven approach to intents and using actual data to create and update the intent models on which bots rely will, in turn, create more accurate bots. They’ll understand and answer the question correctly the first time — without asking for endless clarification.
Customers don’t like bots that pretend to be human. They know they’re interacting with a machine; they don’t like feeling tricked. That said, talking to a robot can be uncomfortable and difficult. Customers sometimes repeat the same information – like their age, or account number — because the robot doesn’t know who they are — and it doesn’t remember what they said.
Making the interaction personal means using contextual data to drive the conversation. Look for opportunities to personalize the experience. Here are some examples:
To build bots that allow for personalization, they must be able to leverage the data the customer has provided. Bots should be built into the journey, rather than exist outside of the journey as a standalone widget.
Bots can only do so much; when they reach the end of their capabilities, the worst experience for a customer is a dead end. This feels like the opposite of empathy.
One way to avoid leading customers to a dead end is to ensure they can transition from a bot to a live agent. The clunky way is to have the bot say: I can’t help you. Call XXX-XXX-XXXX. While that eliminates the dead end, it puts the burden on the customer. The customer must do the work themselves and often leave the interaction without a resolution. As an aside, for those scenarios, the bot often is disconnected. If the customer does reach and agent, the agent will be unaware of the previous interaction. Similarly, the bot will be unaware of any agent interactions that have already occurred.
An empathetic transfer is smooth, seamless and feels like a continuation of the interaction rather than a disruption. The bot should automatically place a customer into the agent queue when the interaction calls for it. With predictive routing, artificial intelligence (AI) is then used to route the customer to the best agent for the desired outcome. Interactions are more successful because the customer is automatically connected to the agent with the right skills, rather than being manually passed from agent to agent.
For bots, context is everything. According to the Oxford-English dictionary, context is “the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed.” Note that the words “understood” and “assessed” are part of the definition. Ineffective bots fail because they don’t understand; and they can’t assess what the customer is asking. Context gives the language models that drive bots the ability to understand the meaning behind the customer request.
Context for bots is multi-dimensional. It’s the ability to understand sentences that are either spoken or written. That ability is technology — and the bot builder trains the technology. The technology itself provides a blank slate. The data used to train, and the ability to correct and re-train, all provide context.
Context is more than the training data. Context is the interaction history and the ability for the bot to “see” where the customer is on their journey. And predictive engagement technology uses data to predict a next-best action, including the need to engage a bot or transfer to an agent.
When a bot transfers an interaction to an agent, it takes the context of that interaction and passes it to the agent. This means customers don’t have to repeat things. By providing these smooth, data-driven transitions, bots aren’t a strategy to replace humans. Instead, bots unburden agents from routine tasks so they can solve more complex issues. It reduces stress for agents and minimizes their frustrations in answering the same questions repeatedly.
A standalone, disconnected bot that’s blind to the customer journey isn’t as effective as one that recognizes where the customer has been and has a sense of where the customer is going. A bot that sends you to the same place you just visited would frustrate even the biggest bot enthusiast. A bot that did so continually would be used as an example of customer service gone wrong.
Many DIY solutions can build a bot. These DIY bots can be trained to do a lot, but without the ability to work within the context of the total journey, they remain siloed — unaware of the customer journey. These bots often seem to pop up randomly, providing answers to questions customers don’t have. They sometimes feel like the brand had a bot and wanted to use it, instead of building a bot to offer better service.
Before choosing a bot-building tool, developers must ensure they can access interaction data in real time, historical data on the customer, and all the context needed to ensure this bot is tightly integrated and aware of the journey. What might seem like an easy solution can quickly become a very complex implementation when the developer wants to integrate data. And this becomes even trickier at scale.
When thinking about time to production, consider how much effort you need to spend getting interaction data into a format your bot can understand. Be sure the data can be read; performance doesn’t degrade, and your data isn’t compromised.
The Bottom Line
Contact center teams struggle to build bots that are tuned to the needs of the contact center. They rely on DIY and open-source bot platforms to create generic bots. These bots aren’t easy to build — and they’re scripted. So they don’t understand the context of the customer journey.
Genesys Dialog Engine BOT Flows makes it easier for organizations to build conversational bots that deliver empathy at scale for companies that don’t have in-house data science or bot development teams. It gives contact centers an interface for building bots in an intuitive way while working with other conversation flows — inbound call, chat or SMS.
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