People dread contacting customer service. It means they have a problem or a need they can’t solve on their own. Experience has shown them it’s rarely quick and easy to resolve issues. Phone calls often come with long hold times, transfers between agents, and explaining the problem again and again.

Many people prefer messaging to phone calls for all their communications—but are met with disappointment when they try to message with their favorite consumer brands.
It’s not that most companies don’t offer messaging or chat in some form. It’s just that trying to resolve an issue by chat is often even worse than calling, so it has not reduced the volume of calls in the way companies hoped.

Why are these systems failing you?

You’ve got pipes.
You need something more.

The problem is that most of these systems were built on chat technology developed in the 1990s—and haven’t evolved much since then. They provide a basic communications channel for conversations between consumers and agents. And, they enable agents to engage with more than one customer at a time.

BUT this legacy technology doesn’t do anything to empower those agents to provide great service for anyone, let alone more than one at a time.

Meanwhile consumer expectations have evolved significantly over the past 30 years. They want companies to value their relationship and demonstrate that with readily available personalized and competent service in the channels they prefer to use. If a company misses, today’s consumers don’t hesitate to call them out in social media. And, with low barriers to switching they don’t stay with brands that don’t get the message.

The oldtimers may say they help agents –
but check the results.

Most chat solutions claim to use AI to help agents solve customer issues. But in reality, they only use simple AI to diagnose intent at the beginning of an interaction, and that’s where it ends. Little or no ‘intelligence’ is applied to the rest of the conversation, so agents do what they’ve always done: Follow a series of rules-based suggestions, rely on their own skills and knowledge (often after only minimal training), hunt for answers in their toolset, consult with other agents, and transfer the contact to someone else when they can’t get resolution.

It’s not surprising that a bit of AI layered on outdated technology is not delivering meaningful results.

Macario Namie

It’s time to REALLY help your agents.

Customer service is long overdue for real innovation. Legacy chat has its place in history. Now it’s time to harness groundbreaking advancements in AI—and realize the full potential of this technology to transform customer experience performance.

What’s possible when you reimagine customer experience with AI as foundational technology? Machine learning can be infused in every aspect of digital care interactions in an integrated system that will:

• Provide AI-driven predictive suggestions based on continuous learning, to guide agents on what to say and what to do to resolve issues quickly.
• Dynamically integrate automation into the workflow to handle routine tasks before, during, and after agent interaction, reducing agent workload.
• Enable rich personalization by providing agents with the full context of the consumer’s interactions before this engagement.
• Use a multivariate model to manage concurrency for agents—considering factors like agent experience, complexity of issues already engaged, customer history and sentiment, and more to set capacity in the moment.
• Give you deep—and actionable—insight into your customers’ motivations and concerns through both real-time and historical AI-driven analysis of every conversation.

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How does 3X productivity sound?

Helping agents helps companies achieve breakthrough results.

We’ve seen it time and again at ASAPP as our customers double and triple productivity—yes, really—and at the same time increase customer satisfaction scores and build brand loyalty.

That’s something no legacy chat technology can do.

Let us show you real results—in less than 60 days.