Harnessing AI
By Fabian Dupuis, Director AI & Analytics
Second part in our Leveraging Superior Customer Experience series.
To leverage exemplary customer service with AI, businesses need to see the human side of individual customers. The first part of the story, “AI: Leveraging Customer Experiences” can be found here.
Hyper-Personalization
What time of day does each of your customers prefer to be approached? What kinds of messages are likely to inspire purchases? These questions get to the heart of what hyper-personalization is all about: understanding individual behaviors and preferences to create profitable customer journeys.
Segmentation and traditional marketing tools cannot meet today’s retention and loyalty challenges. As online experiences improve, customer expectations increase. Superior customer experiences depend on treating every potential buyer as a unique individual. The stickiness factor isn’t generated by broadcasting information but through end-to-end relevancy. From brand recognition to purchases, personalized journeys are essential to the buying experience.
“It’s about unified customer journeys that drive brand loyalty and unleash the highest lifetime value,” says Gregory Verlinden, Associate Vice-President for Artificial Intelligence and Analytics in Benelux. “Think in terms of ongoing dialogue. You must deliver the right message to the right customer at the right time — and by the right channel.” In other words: businesses need a deep knowledge of profiles.
Experience Operating System
When devising an experience strategy, the best starting point is not “What do we have?” but “What should we deliver?” Success with hyper-personalization hinges on offering customers something of value in exchange for data about themselves and their activities.
“Deliver the right message to the right customer at the right time, by the right channel.”
Determining which experiences will matter to individuals lays the groundwork to leveraging customer relationships. But businesses still need the right resources to optimize their data and their decision-making.
Traditional marketing tactics cannot produce the data-driven intelligence necessary for speed and scale. Meanwhile, expensive marketing tools leave businesses constrained by the parameters of service providers. A fresh approach to customer conversations requires integrating sales, marketing and production processes for data-driven performance.
Businesses must combine their technology, customer data and artificial intelligence to deliver contextually relevant and truly personalized experiences.
To reap the benefits of hyper-personalization, companies will want to invest in the right platform — one that enables them to access and analyze the right data at the right time, all while maintaining data privacy and ethical AI governance.
High-Value Opportunities
With the advanced technology capabilities that are now available, it’s a good time for businesses to rethink how to upgrade their approaches to customer experience. More mature forecasting means companies can take their customer lifetime value initiatives to the next level.
One caveat to keep in mind: Gathering as much data as possible will not guarantee success — a lot of data will simply be irrelevant to the context. Businesses naturally don’t want to waste time and money on ineffective marketing. So they should use AI technology to identify and target the highest value customers.
Building loyalty and return business are just as vital in B2B contexts as in B2C, though with much more complex challenges to tackle.
How do you profile a company, and how can you profile individuals within that company? You know a prospect would take interest in a new product, but how do you communicate it?
The first crucial step in creating hyper-personalization journeys in B2B contexts is to identify the optimal customer personas. For example, knowing who to approach begins with pinpointing the different stakeholders.
The second most important step is a personalized approach to account-based marketing, which can reduce engagement time and lead to increased sales.
ROI and Hyper-Personalization
For our research with ESI Thought Lab, we surveyed 1,200 businesses across 12 industries and 15 countries. When it came to the ROI on hyper-personalization, the response was indisputably conclusive. The majority of respondents (74%) say that across functions, customer service and experience is the area most likely to result in a positive return from an AI investment.
Conversational Solutions
Engagement Engine
Conversational AI is changing how customers interact with businesses. From voice assistants and virtual agents to smart speakers and household appliances, consumers are increasingly using bots to make purchases, pay bills and manage daily household needs. For businesses seeking new ways to engage and retain customers, conversational AI can be a powerful tool.
Marketing, commerce and support leaders are applying conversational technologies broadly and widely to expand reach while increasing customer satisfaction. As a result, businesses are seeing remarkable gains in self-service, customer experience and operational insights. By creating contextual and personalized interactions, businesses can provide the timely, relevant information that supports customer buying decisions.
Conversational commerce is opening up a vast field of possibilities. In the finance sector, for instance, digital assistants are serving as Conversational Solutions virtual investment coaches, empowering human advisors to optimize client portfolios. On-demand delivery and re-order automation are helping e-commerce brands cement customer loyalty. Retailers are tapping conversational innovations to bridge gaps between in-store and online shopping.
Chatbots are simplifying and streamlining personalized customer journeys across a whole host of industries.
Value Dynamics
There’s an enormous variety of conversational applications, especially in customer service where fast query resolution and 24/7 support can make all the difference in a buying experience. Intelligent virtual assistants reduce overall support costs. And because they gather data about customer needs and intent, conversational technologies can deliver profitable insights when it comes to effective lead generation.
Despite the numerous benefits that conversational AI presents, many companies struggle to incorporate it into their digital ecosystems. The steep learning curve is a big reason many companies stall at earlier stages of development. Creating bots that people actually want to talk to requires a lot of trial and error. Yet it’s undeniably important to figure out what resonates: the more businesses learn about customers, the more they can harness conversational AI to tailor products and formulate high-value sales funnels.
On its own, conversational AI isn’t the solution for serving customers with relevant information and appealing offers. It’s in combination with clear goals, greater data maturity and hyper-personalization that businesses can make the most of intelligent virtual assistants.
To leverage conversational AI, companies should undergo a value-discovery process before experimenting with ideas.
Success Practices
With an expansive portfolio of proofs of concept, pilots and deployments, Cognizant finds that successful conversational AI solutions are born from sound methodology. It begins by asking the most practical questions.
Does your business want to cut costs, serve customers better and/or generate additional revenue? Whatever the answer may be, how could conversational AI steer the business towards that goal? To discover the value, company leaders must understand what they want to achieve and how they plan to reach project milestones.
The grandest visions do not necessarily lead to the grandest results. It’s always wiser to begin more humbly, says Balazs Vertes, Head of Conversational AI for Europe at Cognizant. “Think big, but start small,” he advises. “Choose a single domain, then learn from and expand on that. You don’t want to allocate resources to multiple domains, knowing that some projects may not work.”
It’s essential, then, to find the right uses. Establish a business case with a pathway to value. It’s equally important to reflect on your customer profiles. Conversational AI is an ideal way to interact with customers who want immediate service. If your customer journey isn’t open to on-demand interactions, conversational AI won’t be a good fit for that business case.
If conversational AI is a good fit, functionality becomes a key consideration. Conversational AI isn’t itself a channel. Rather, it’s an interface that bridges back-end processes with front-end experiences. Consider which channel is most appropriate to the customer journey, and base the conversational technology around that. Do a lot of user testing to ensure customers will value the product. Embrace feedback, and adapt the solution as needed before final development.
Think big, but start small.
Above all, keep the human in mind. Designing natural sounding conversational flows is an art. Experienced designers will bring the visual appeal, but linguistic skills are indispensable. A bot should speak like a local; you need people with native speaking skills on your team. Skilled copywriters who can build characters and develop scripts are invaluable in establishing authenticity and trust — don’t alienate potential buyers with pushy marketing messages.
A bot is only as good as the intelligence driving it. To consistently feed chatbots effective content, companies need a system that continuously captures, reuses and extends data in a structured, efficient way.
Such a knowledge framework creates the conditions by which chatbots can deftly match users’ intent to specific content.
In the next edition, we will share best practices for transforming AI delivery. You can download the ebook from here.
