Applying artificial intelligence for next-generation conversational customer experience

Customer experience is a combination of the perceptions, emotions, and reactions that a consumer has while using different channels to interact with a company’s environment, products, or employees.

Major brands aspire to deliver an engaging and compelling experience across all the touch points and “moments of now” they have with their individual customers. To deliver on their ambition, they build the ability to consistently support the entire customer experience lifecycle: from initial contact, through the process of acquisition, engagement, and into a long-term relationship – anywhere, anytime, any device, seamless, contextualized, personalized, and proactive.

Many of them have already started the transformation towards having a single, consistent customer platform that supports the end-to-end customer experience by interconnecting enterprise-wide sets of customer data and insights, customer engagement engines (such as marketing, CRM, commerce), and enabling processes through a digital architecture. This next-generation digital platform not only connects the whole client ecosystem of business and technology partners, but also the ecosystem of internet platforms, social networks and devices favored by the consumer.

And now, in this new experience era, most brands are now orienting their customer platform transformation journey towards the most human way of communicating with consumers: conversations.

Conversational customer experience promises to fundamentally alter relationships between consumers, brands, and their partners. It is the next big battlefield for the hearts and minds of consumers. For enterprises and their partners, it presents the opportunity – and the challenge – of developing deep, sticky brand affinity with an ever-growing cohort of consumers.

With conversations, user experience is being simplified and humanized with natural language-based interactions, through messaging apps (e.g., Facebook Messenger) or voice-based assistants (e.g., Amazon Echo or Google Home).

Conversations will progressively become the vehicle of choice for connecting individual consumers with brands, ranging from basic question and answer or dialogue, to playful conversations, and even personal assistants. They are deeply connected inside the underlying customer platform with the data, engagement engines and enabling processes required to deliver the frictionless, personalized, and proactive experience that consumers expect.

So-called “artificial intelligence” technologies and solutions are increasingly becoming critical enablers for this transformation journey towards conversational customer platforms.

These conversational customer platforms – powered by “machine learning/deep learning” algorithms continuously learning from captured customer data, customer information, interactions, and feedback – will support many new use cases in domains such as natural language-based interfaces, predictive personalization, improved customer understanding, and employee augmentation.

Below are a few concrete use cases that illustrate the value that AI technologies and solutions can bring when they are applied to customer platforms:

• Implementing user interfaces for conversations (for e.g., chat/voice-based interfaces) supporting sales/service interactions
• Improving immersive customer experience in stores or branches: facial recognition, natural language and intent recognition, sentiment detection, augmented reality
• Automating and personalizing customer contacts (email, mail, chat …) qualification, prioritization, and distribution to service agents
• Improving service agents’ efficiency when handling customer requests/claims: real-time answer suggestions, request/claim content analysis based on context, field service image recognition
• Improving sales force efficiency with lead scoring, account and opportunities insights, improved forecasts
• Improving marketing efficiency with engagement scoring/predictions, audiences’ micro segmentation
• Providing predictions for personalized and contextual propositions (offers, products, services, rewards) to customers, for both inbound and outbound interactions, in real time
• Optimizing products, services, and experience from customer feedback and behavior
• Anticipating individual customer churn
• Detecting and adapting to customer sentiment and tone during interactions (voice, text, image)
• Listening social networks for monitoring and understanding brand sentiment.

Customer platforms vendors are now embedding the required AI foundation technologies such as machine learning, deep learning, and pre-trained AI models into the solutions they deliver to the market. A key market trend is to enable brands to securely and quickly build their conversational customer platform by connecting and orchestrating vendors “AI Legos” such as out of the box AI-powered use cases and easy to integrate AI services, as well as using easy to access AI tools for conversational user experience design or domain-specific models training. There are many leaders in this space – Pega, Salesforce, Microsoft, IBM, and others.

Orchestrating the transition to those large-scale conversational customer experience platforms can be a game changer. Shaping, designing, and implementing customer platforms with applied artificial intelligence requires mobilizing diverse streams of expertise, assets, and methods: innovation strategy and business models for artificial intelligence impact, customer data hubs design and connection, conversational user experience design, customer platforms technology and architecture for applied artificial intelligence, as well as new ways of working and collaborating. This must embrace both business and IT capabilities to ensure the platform implementation aligns with business intent and priorities and delivers the expected value in a measurable way, at scale.

Mobilizing and orchestrating business and data consultants, conversational experience designers, digital architects, and new-ways-of-working leaders into client-facing “tip-of-the-spear” teams make the difference when designing and implementing next-generation conversational customer platforms at scale.

This is the Capgemini new way of engaging with our clients in their journey towards large scale conversational customer experience platforms; enabling them to deliver the frictionless, personalized, and proactive experience that consumers are demanding while being predictable on the ability to implement with quality, speed, cost effectiveness, scalability, and elasticity – all combined.

If you are interested in discussing this further contact me – I would be happy to hear from you.