By Will Thiel
“By 2020, 85% of customer interactions will be managed without a human” – Gartner
Today’s customers live in an omnichannel world. But most companies still force these evolved customers onto engagement paths that are steeped in legacy and instantly feel outdated.
Artificial intelligence can be successfully employed to provide an intelligent, convenient and informed customer experience at any point along the customer journey. This will result in re-imagined customer experiences and end-to-end customer journeys that are integrated and more personal, so that they feel more natural to customers.
In this post, I will lay out why artificial intelligence is a game changer in CX, take a look under the hood at how AI is applied to CX, and explore use cases for how leading edge companies are already reaping benefits from AI applications in customer experience.
The Need For AI In Customer Experience
Customer experience is a competitive driver of growth when successful and the greatest source of risk when failing. Data insights are one of the primary tools for CX enhancement. CX datasets are messy, however, and the customer behaviors are chaotic. The rules are undefined and the success criteria are ambiguous. CX is the nightmare dataset for an AI developer.
At the same time, this complexity is precisely the reason why AI can unleash so much value across the customer experience. Salespeople, call center agents and employees in other customer-facing roles cannot be expected to understand a customer’s entire history and derive their own insights from it in real time.
Automated systems cannot be hand-programmed with rules to handle every conceivable customer history. Delivering a coherent experience across all enterprise touchpoints requires finding patterns across an overwhelming number of data points. This is prime stomping ground for AI.
3 Building Blocks for Successful Application of AI in Customer Experience
The successful application of AI in customer experience requires 3 fundamental capabilities:
- Data Unification
- Real-time Insights Delivery
- Business Context
Data unification to create a single customer view is a must for any type of behavioral analytics. AI thrives on information—the more the better.
The new generation of data unification tools make this daunting task cheap, fast, and relatively pain-free. Customer journey analytics platforms provide this service for a fraction of the cost of the dedicated data services providers of yore—even delivering a level of data integration free of charge.
The tedium of pulling together dozens of data sources is now just background noise. Expect timelines of days not weeks, with simpler data sources integrated within 1-3 days.
It’s a far cry from the expansive data engineering initiatives that likely still haunt your dreams.
Real-time Insights Delivery
For AI to impact the customer experience, insights need to be conveyed in the moment through the customer’s chosen touchpoint. Integrating with these touchpoints is the key to in-the-moment engagement.
Most leading SaaS platforms have APIs and consider 3rd-party integrations to be a critical component of their value proposition. The world would be a beautiful place if all touchpoint data was available through APIs.
The truth is that, in addition to elegant SaaS data streams, most enterprises must rely on myriad on-site, home-grown and legacy touchpoint data sources—product interfaces, payment platforms, point-of-sale systems, customer care, etc.. This reality creates a challenge for delivering real-time insights and are still very much a custom affair.
Customer journey analytics platforms are now filling this gap with a host of APIs options and development kits to deliver comprehensive, real-time touchpoint integration with minimal investment.
For a simple, isolated interaction, AI is able to deliver results by simply knowing that an email is an email and a campaign is a campaign. Our web analytics and CRM platforms take advantage of this inherent luxury.
But in holistic, cross-channel journey analytics, the idea that touchpoints of a similar category will be the same across enterprises is an antiquated notion.
Customer journeys are as unique to individual businesses as fingerprints. Every company has their own set of touchpoints and a distinct method for employing those engagements in their customer experience.
For AI to deliver value, it must be given some context. By context, I mean more than simply designating a certain interaction as an “inbound call” and another as “order fulfillment.” AI must know the significance of these events in shaping a customer behavior. That requires an awareness of both the journey that these touchpoints helped to shape and the KPIs which were subsequently impacted by that customer behavior—whether related to revenue, profitability, customer lifetime value, customer satisfaction or other factors driving high-level business performance.
Armed with that information, AI systems can do more than find the “next best action” or the optimal audience. With proper business context, an AI can find touchpoints and tactics which actually shape the customer behaviors behind the business’s primary measures of performance.
Three Ways AI Is Being Applied to Improve Customer Experience
Now that we understand what it takes to successfully apply artificial intelligence in customer experience, let’s delve into some of those applications to see how AI is unleashing disruption across various aspects of customer experience by unifying data, providing insights in real-time, and incorporating critical business context.
1. Customer Service Gets A Gigantic Makeover
AI’s biggest impact undoubtedly will be to transform customer service by making it automated, fast and hassle-free. As I previously mentioned, salespeople, call center agents and employees in other customer service roles cannot be expected to ingest and understand a customer’s entire history prior to each conversation. But, artificial intelligence is now making it possible.
Here’s how AI applications are giving customer service a makeover:
Chatbots are AI-based conversation agents that are being used in many different customer-engagement scenarios. They are designed to simulate human interactions and provide immediate, personalized responses 24*7. This eliminates frustrating delays and errors in customer service, particularly for handling customer complaints.
Virtual assistants utilize AI to obey commands or answer questions. Online retailer Spring was one of the first to start using Facebook’s Messenger Bot store to offer a personal shopping assistant. It helps shoppers find what they are looking for by engaging them in simple conversations.
2. Predictive Personalization – Going From One-Click to Zero-Clicks
Artificial intelligence is helping businesses create experiences that naturally integrate with consumers’ everyday lives.
Consumers will no longer change their pattern of communication when interacting with brands in order to satisfy their needs. Intelligent prediction and customization will make customers feel as if every product or brand experience was tailored just for them.
Companies will be able to assess individual shopper inventories and consumer behaviors to predict and deliver goods to homes before they even realize they are running low. Self-driving cars will use their knowledge of preferred routes and in-vehicle entertainment drawn from past behavior to optimize daily commutes and long roadtrips. Even asking for help will become easier as AI infused with emotions will make customer experience interactions smoother and streamlined across channels.
3. AI-enabled Customer Analytics Discovers High-Impact Customer Insights
Optimal customer experience is achieved when a business remembers a customer and treats them with attention, respect and consideration throughout their unique customer journey.
Mining insights across billions of unique customer journeys using traditional analytics methods and tools is a laborious and slow process, which tends to confine it’s usage to a small set of pre-defined problems.
The power of AI-enabled customer journey analytics is that it can sift through a much, much larger and more complex data space and thereby uncover many more business opportunities—even opportunities you didn’t realize you should look for. As a result, you can spend your time prioritizing these insights instead of hammering away at the underlying data.
AI-enabled customer journey analytics finds every single relationship in the data that exists(without expressly being told to look for it). It can predict the likelihood of future behaviors with high accuracy, while simultaneously finding the drivers and inhibitors of customer performance.
Artificial intelligence-enabled customer journey analytics can find answers to important CX queries like:
- What customer behaviors are early indicators of impending outcomes such as churn?
- What CX actions have your team taken that have been successful or unsuccessful?
- Which customers/prospects should you engage with to maximize the impact of your outreach?
For example, a leading retail bank uses predictive analytics to visualize high-impact journeys that lead to decreases in savings account deposits, reduction in app activity and stoppages in auto payments.
Leading The Charge – Companies That Are Getting AI in CX Right
Leading companies are constantly experimenting to determine the best way to employ AI to improve customer experience. These companies have unified disparate customer data sources, analyzed end-to-end customer journeys and are using machine learning algorithms to predict future customer behavior. They are reaping the rewards through quantifiable improvements in customer experience, increased customer lifetime value and reduced churn.
Large banks have thousands of customer touchpoints that capture millions of customer interactions every day, making customer experience a minefield for them to navigate. Nordea, a Stockholm-based bank, has looked deep into the customer journey to find the best points to deploy AI for maximum impact.
They recently introduced virtual employees, that perform repetitive tasks faster and more efficiently than their human counterparts. These virtual employees (Liv in Stockholm, Erma in Denmark, Roberta in Norway, Sirius in Finland, among others) are empowered to take correct and fast decisions using rule-based robotics initiatives. This frees employees to engage in those activities that provide an enhanced experience to customers.
Nordea also partnered with an AI-based text analytics solution provider to interpret hundreds of inbound customer communications per second and intelligently forward them to the right business unit. This eliminates the agent-based sorting that frustrates bank customers around the world.
Sephora is not the only beauty brand to use chatbots, but it has surged ahead in artificial intelligence usage with its Visual Artist product.
Through the Visual Artist website or app, visitors can try on cosmetic products such as lipsticks, eyeshadows and highlighters to match their skin tones. Using AI, the tool can map and identify facial features and apply the product to the user’s face. Visual Artist is also available as a Messenger bot, so shoppers can send a picture to the chatbot and it comes back with an image of how they will look once the recommended beauty product has been applied.
Sephora has thoughtfully considered the entire customer journey—the Visual Artist tool ties in to Sephora’s entire inventory of products seamlessly, and driven by the AI engine, personalized recommendations and offers are made in real-time. When the visitor is ready to buy the product, the checkout is of course made as smooth and painless as possible.
Ski-equipment retailer Black Diamond realized the value of using AI for personalized engagement long before its peers. The company knew that the old-school customer experience, which required hours of browsing to find the best product, was not going to cut it anymore.
Skiers often know exactly what equipment they need to stay safe and compete, such as avalanche airbags. Black Diamond predicts these needs and pushes the right items to website visitors, rather than waiting till they checkout to make suggestions.
They use sophisticated analytics to glean insights from a customer’s purchasing history, and combine it with weather conditions and other relevant data to make product recommendations in real-time. This effort has reaped rewards for Black Diamond, increasing sales significantly and dropping cart abandonment rates.
Application of AI in B2B Companies
It is not hard to imagine how B2B businesses too can benefit from the same applications of artificial intelligence in customer experience. AI-enabled tools can automate customer-centric tasks to increase productivity and enhance customer experience at B2B workplaces by orders of magnitude.
It can create data where previously there was none and analyze this data to predict future customer behavior. For example, Salesforce’s artificial intelligence technology (called Einstein) uses machine learning algorithms to analyze customer conversations as they happen. It then alerts managers in real-time whenever an opportunity to enhance customer experience, solve a problem or cross-sell/upsell arises.
Putting It All Together
AI presents an opportunity to turn many-siloed, multi-channel enterprises into singular “personas” who remember, understand, and respond to their customers’ achievements and setbacks in a meaningful way. The ability to speak with One Voice is the difference between share-worthy and forgettable.
The challenge, however, lies in determining how to start developing the right processes and expertise for collecting data—as well as building AI algorithms and models—swiftly enough to reap the benefits. Most companies find it difficult, if not impossible, to accomplish those tasks on their own, given the dearth of data scientists, the fact that disparate systems are not AI ready, and the need to rapidly build new systems, apps, and capabilities. Moreover, companies are only now waking up to the idea of applying AI to improve CX—so most don’t even know how or where to begin.
This is where a sophisticated AI-enabled customer journey analytics platform can help deliver high-impact customer experiences rapidly and effectively.
It is time to stop treating customer engagement as a cost center and acknowledge it as an investment in customer relationships. With some imagination and application, artificial intelligence can and will enhance every aspect of customer engagement.