2-minute read
Today’s consumers have high expectations of the customer experience, and they are equally willing to reward businesses who meet those expectations as they are to walk away from those who fail to deliver. To build loyalty, organizations must create delight at every stage of the customer journey. Many businesses are discovering that the key to delivering those relationship-building human experiences lies, ironically enough, in technology—specifically, in artificial intelligence.
For companies and customers, experience is everything.
Businesses are under pressure to deliver the experience customers want, when they want it, according to their individual needs and preferences. Consider the following results from recent surveys:
- 67 percent of consumers say their standard for a good customer experience is higher than ever.
- If an experience falls short of their expectations, 58 percent of customers show little hesitation in severing the relationship with the company.
- 84 percent of consumers will go out of their way to spend more money with businesses that offer great experiences.
Experience is everything, and a rapidly growing percentage of those experiences is digital— especially given the increased demand for contact-less services arising from the coronavirus pandemic. Technology enables optimized digital experiences, helping us design ever-more personal interactions, provide seamless service, and close the feedback loop. Whether the channel is phone, email, website, or text message, companies must deliver a consistent, high-quality experience to inspire the loyalty that keeps customers coming back over and over again.
To get insight into customers and drive business outcomes, companies can analyze a myriad of data sources. Most organizations have no shortage of data on their current and potential customers. However, translating that data into meaningful information can be challenging for two reasons: the data is disparate, and human behavior is complex.
Customer data is found in a huge number of sources—from product orders to support tickets to emails to social media messages to transcribed phone calls—in databases scattered across the organization. Analyzing terabytes of data and providing real-time actionable insights lies beyond the capacity of even the most capable human employee. Enter artificial intelligence.
Why now is the time to invest in AI as a competitive advantage
Unlike human employees, AI can access hundreds of sources simultaneously, extract the data needed for a desired task, and transform that data into insights. Using natural language processing (NLP), AI can understand human inputs—text and voice—and “listen” to outside sources (such as social media networks) as well as human conversations to identify opportunities and make recommendations. And it can leverage data to generate predictive analytics, which enables organizations to predict customer behavior and to take a more proactive stance (for example, in preventing customer churn).
Only recently has AI evolved to the point where it can “tame the customer data beast” to drive delighting experiences throughout the customer journey. Early iterations of AI for customer service were limited by an inability to handle complex situations and a frequent need for maintenance to accommodate new scenarios. Thanks to advancements in machine learning, today’s AI-based customer service applications can learn from a vast array of data resources, including unstructured data.
Due to the complexity and cost of developing AI models that can be used in multiple contexts, AI has historically been out of reach for mid-size companies. But now, with the incorporation of AI into tools like Salesforce, the benefits of artificial intelligence are no longer limited to large enterprises.
The AI-powered customer journey
Artificial intelligence can empower organizations to raise the bar for customer interactions throughout the customer journey:
AI and the customer experience: more use cases
AI offers businesses advanced capabilities that can increase customer satisfaction, agent productivity, and strategic use of customer data. Here are just a few of the use cases that are proving successful in organizations of all sizes.
Personalization
AI can tap into all areas of a customer’s history—not just what they’ve bought, but also other products they’ve viewed, reviews they’ve posted, videos they’ve watched, even their public social media postings—for data that can reveal their needs and predict their behaviors. Machine learning can track customers’ activity to yield deeper insights and to enable real-time adjustments when behaviors change. These insights can be accessed by both human employees and AI-guided applications to create experiences that bespeak a true understanding of who the customer is and what they need.
For example, when a Netflix subscriber logs in, she’s immediately greeted with a list of recommendations based on AI-driven analysis of her past behaviors—not only the shows she’s watched, but also details such as the day of the week, the time of day, the device used, and possibly even the location for each viewing. Netflix has honed personalization to such a fine art that 80 percent of the shows users watch are discovered through the platform’s recommendation system.
Virtual agents
The category of AI that was born as programmed chatbots has evolved into sophisticated virtual assistants and, in the customer service world, virtual agents (VAs). With the rise of Siri, Alexa, and other virtual assistants, interacting with conversational AI technology has become a part of everyday life.
Thanks to recent advances, today’s VAs can mimic human interactions more effectively and handle more complex issues. Organizations can offer 24/7 service-on-demand for a broad range of issues, and call center reps are free to focus their efforts on more complex, interesting cases. AI can also monitor virtual agent interactions to gauge user sentiments and route customers to human agents if anger or frustration is detected.
AI insights for agents
AI benefits organizations behind the scenes by offering powerful tools to support agents’ real-time conversations with customers. For example, if an agent is on the phone with a customer asking about payment plan options, AI can pull up knowledgebase articles about payment plans immediately, right on the agent’s screen. The agent has the information she needs to help the customer make an informed decision, and the customer doesn’t have to wait for the agent to click around looking for the right resource.
Agents can also benefit from AI in situations where information must be pulled from a variety of different sources. For example, if a customer calls with questions about a warranty, the agent will likely need product information, warranty information, the date of purchase, and other details to provide accurate answers. AI can fetch these details from their respective resources simultaneously, allowing the agent to focus on the customer instead of on hunting down each piece of information.
Discover trends with predictive analytics
AI algorithms can aggregate and analyze data to generate predictive models of future customer behavior. Organizations can leverage these models to, for example,
- Prevent churn by identifying “red flags” that may indicate a customer is considering leaving
- Leverage upselling and cross-selling opportunities at times when customers are most likely to buy
- Explore new markets based on shifts in customer demographics, geographic locations, and other indicators
At every stage of the customer journey, businesses have an opportunity to meet and even exceed expectations. The key to leveraging these opportunities lies in data, and AI-driven applications have the power to mine terabytes worth of information in real time to elevate the customer experience. Ironic as it may seem, technology holds the key to building a more personal experience and inspiring the loyalty that keeps customers coming back over and over again.
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Alexis Greenwood is a manager in the Logic20/20 Digital Transformation practice, focused on offerings development and innovations. In her experience as a business systems analyst, she enabled change through development of low-code platforms, including Salesforce and ServiceNow, custom applications, virtual assistants, and a variety of tools including ERPs, ITSM tools and CRMs.
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