Why Contextual Intelligence is Key to Great Customer Journeys

Contextually intelligent systems save customers from having to repeat themselves and ultimately contributes to superior experiences. This article is the fourth in a six-part series on how to create a great customer journey with your contact center.

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Why Contextual Intelligence is Key to Great Customer Journeys

For many people, the term “artificial intelligence” brings to mind a fully automated society run by robots. But the fact is, artificial intelligence (AI) is far less dramatic than it’s often depicted in science fiction. In fact, AI is already in widespread use in our everyday communications—from search engines to spam filters to online shopping recommendations. In the contact center, AI is becoming an increasingly essential element to great customer journeys.

In Forrester’s 2018 Customer Service Trends report, 46% of global contact center decision makers projected their contact centers would grow by 5%-10% in the next year. Kate Leggett, Vice President and Principal Analyst at Forrester said, “This approach is not economically sustainable. Enterprises must re-imagine their operations, with automation and AI at the center of their strategy.” Powered by contextual intelligence, AI contact center tools learn from their interactions and evolve over time to provide better service.

What is Contextual Intelligence?

When we refer to context, we're specifically referring to the customer’s personal information, interaction history, and all other aspects of the customer journey being available to the agent during a service or sales interaction. When your agents know who your customers are, what they’re looking for, and what they’ve already spoken to another agent about, they can give better service, faster.

The need for context from the entire customer journey has caused innovative businesses to look to advanced AI applications for real-time data analysis to develop an interpreted understanding of customer intent. Contextual intelligence is being used to not only inform live agents so they can have more productive conversations while handling customer issues, it’s also empowering a company’s self-help tools, like interactive voice response systems (IVRs) and bots, to better respond to customers’ needs in a more human way.

Contextually intelligent systems help businesses provide contact center agents with personal data, interaction history, and other aspects of the customer journey. The more an agent is contextually aware, the easier it is to provide better, faster service. Contextually intelligent systems save customers from having to repeat themselves and ultimately contributes to superior experiences.

Contextual intelligence is an essential ingredient for modern contact center technology. When choosing the right contact center platform for your business, look for infrastructure that supports the integration of innovative AI technologies, so you can stay ahead of emerging communication trends instead of struggling to keep up.

Practical AI in the Contact Center

By 2020, Gartner predicts that customers will manage 85% of the relationship with an enterprise without interacting with a human. This is becoming possible because of advances in machine learning. Machine learning refers to the ability of AI systems to learn and improve automatically. Machine learning models take in data through observations and interactions and adapt accordingly without being explicitly programmed. Essentially, machine learning models program themselves.

Here are a few ways modern contact centers are already utilizing AI:

Natural language processing (NLP)

APIs like Twilio Understand make it easy to capture and leverage customer information to deliver a great experience. Powered by machine learning, this API can analyze text and determine intent during a live call using natural language understanding. It then turns freeform text into structured data. NLP models are often used to build more effective and accurate IVRs to get customers to the right agent as quickly as possible. With life-like text to speech and speech recognition, NLP engines give business the tools they need to build the exact customer experience they want using humans or highly intelligent bots.

Sentiment analysis

Sentiment analysis is the process of determining whether language reflects positive, negative, or neutral sentiment. Sentiment algorithms provide insights into customer opinions about a topic. Automated sentiment analysis is a powerful tool for gauging customer opinion at scale. Rather than relying on humans to read and evaluate large volumes of text, an algorithm can process and score sentiment rapidly and efficiently. IBM Watson’s Natural Language Understanding service is one example. This sophisticated algorithm reads text from inbound customer messages, looks for words and phrases indicating sentiment, and scores each message as positive, negative, or mixed. It’s smart enough to understand negation and modifiers and speaks multiple languages (with more in development).

Conversational assistants or bots

Proactive companies are leveraging natural language processing and sentiment analysis to create “conversational assistants” which automatically help customers get the real-time information they’re seeking. Businesses can build conversational assistants and fully-integrated bots, trained within the context of the business, to help customers without human oversight. By using these AI technologies to glean the context and actual intent of a customer’s text, voice, or message, the “assistant” can take immediate action—with a more natural, human-like response. These actions might be generating a ticket, appropriately replying via text, proposing a callback from a live agent, or automatically routing the user to a chat window or live agent for quick resolution. Twilio Autopilot is a conversational AI interface designed to bridge the gap between human agents and self-service bots.

AI in the Customer Journey

With the right contact center platform, businesses can use AI to build more efficient customer journeys, enabling customers to solve their issues faster or independently. Capabilities such as intelligent routing and keyword spotting can help agents create better experiences. Tools like phrase detection, call scoring, and intelligent redaction of recordings make contact center operations teams more efficient. When contextual intelligence helps customers resolve common issues without needing to speak to an agent, agents are free to focus on higher value activities.

Modern contact centers use contextual intelligence in three different areas of the customer journey:

Before an interaction

Before a call even takes place, businesses can utilize conversational assistants to deflect calls and handle routine issues, and intelligent routing to connect customers with the best agents to serve their needs.

During an interaction

During an interaction, businesses can provide contextual suggestions to their agents and automatically flag interactions for supervisor intervention. In cases where human agents aren’t necessary, virtual assistants can provide instant help.

After an interaction

Once agents have completed a customer interaction, businesses can score agent performance to improve future customer interactions. Businesses can also use transcriptions/messages for keyword mining, pattern spotting, and training purposes.

From Contact Center to Context Center: Simply Business

Recently acquired by Travelers Insurance, Simply Business is the UK's leading business insurance broker — protecting more than 300,000 small businesses nationwide. With over 40% of Simply Business purchases taking place over the phone, they needed a reliable contact center to manage and effectively route callers. To ensure that their customers have the best experience possible, Simply Business knew they had to eliminate time spent on hold or the need for customers to repeat themselves issues multiple times.

“The contact center really needed to be core to the customer experience,” said Lukas Oberhuber, CTO of Simply Business. “That’s what’s driving our investment in the Twilio platform.”

Simply Business also wanted to make their contact center employees more efficient by automating as much as possible. To accomplish this, Simply Business designed and built an intelligent contact center from scratch leveraging Twilio APIs that use real-time data to track every step of the customer journey and keeps agents up-to-date on customer needs.

Working with Twilio enabled Simply Business to utilize their in-house data science model, designed to target the right customer with the right products and right conversations. Since Simply Business began integrating their machine learning capabilities with Twilio, they’ve seen a significant impact on customer outcomes.

“We believe that these [AI] approaches will transform the way that businesses operate, and specifically to our market, how insurance is sold,” said Oberhuber.

With approximately 200 agents, Simply Business uses Twilio TaskRouter to route customers to the right agent at the right time. Plus, by tracking the customer’s entire digital lifecycle, Simply Business can run A/B tests and iterate and adapt based on real-time interaction. A recent test on an IVR channel, which took only 15 minutes to set up, drove a 3% increase in conversion.

With their new contact center in place, call handling at Simply Business rose 15%, inbound sales conversion increased by 9%, and offline net promoter scores increased by 5 points. They've also been able to build a lead scoring model that has an 80% predictive result.

"Customer understanding and data analytics would not be possible if we didn't have intelligent information available,” said Oberhuber. "No other platform (aside from Twilio) provides that kind of data."

The Intelligent Contact Center

Machine learning is on the verge of transforming many of our applications, including the contact center. Advanced AI solutions are an important key to today’s great customer journeys. To enable what’s coming next in machine learning for your business, you need the right contact center architecture. Whether your in-house data team creates your own machine learning models or you use another vendor’s AI technology, you’ll want to have an innovative contact center platform that can extract the data, train those models, and inject back in insights for the right changes to your workflows.

Ultimately, contextual intelligence provides your customers with a more personalized and efficient experience—the best of which happen without them even noticing. It improves agent conversations by helping to anticipate customer needs and automate actions where possible. AI tools can also power self-service for more efficient issue resolution, and help those interactions feel more human. Look for a contact center platform that can integrate with innovative AI technologies such as natural language processing, life-like text-to-speech, and speech recognition. This will give you the tools you need to build the exact customer journey you want, using the perfect combination of humans and AI.

Want to dive deeper? We wrote an entire e-book on the subject. Download the Four Essential Ingredients for a Modern Contact Center.

Check out Part 1 for an overview of the four key ingredients for building a great customer journey, Part 2 to learn all about customizability, Part 3 for a deep dive into omnichannel communications, Part 5 to understand the importance of trust and scale, and don’t miss the difference between SaaS, CPaaS, and CCaaS in Part 6.