By Will Thiel
“By 2025, as many as 95 percent of all customer interactions will be through channels supported by artificial intelligence (AI) technology” – Microsoft
Customers live in an omnichannel world, but most enterprises still struggle to manage, measure and improve cross-channel journeys. Journey data is steeped in siloed, legacy systems and are inaccessible to the teams that require actionable insights to make data-driven decisions.
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 the role of AI in customer experience and explore use cases for how leading-edge enterprises 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. However, CX datasets are messy 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 consistent experience across all channels requires finding patterns across an overwhelming number of data points. This is the perfect role for AI in customer experience.
3 Building Blocks for Successful Application of AI in Customer Experience
The successful application of AI in customer experience requires three 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 encompassing millions of data points is now just background noise. Expect timelines of weeks, not months.
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 identify the root causes of CX issues and uncover the most predictive, exclusive and frequent journeys that customers take before and after an interaction, or between two interactions.
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 road trips. 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 improvements should you prioritize to improve CX and achieve business results?
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.
Using AI to Improve Customer Experiences
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 identify points of friction and predict future customer behavior. They are reaping the rewards through quantifiable improvements in customer experience, increased customer lifetime value and reduced churn.
Make NPS Actionable & Quantify the ROI of CX Initiatives
A leading bank uses AI within a journey analytics platform to identify CX issues in their customer onboarding process, launch a test initiative to improve the process and determine its impact and ROI.
Clients begin the onboarding journey by opening a new account before following the standard onboarding process for new customers. But during the onboarding process, many customers end up calling support. The NPS rating at the end of the process is lower than expected. It’s below both the bank’s average and the industry benchmark. Based on this result, the team is determined to further investigate and discover the root cause of the poor experience.
The team uses AI and machine learning to discover the most frequent and predictive customer behaviors that occur between the start of the onboarding process and the support call.
They find that the majority of support calls are coming from customers that asked for assistance at a branch location or received an error while using the web portal. After adding these two key interactions to the journey, the onboarding analysis contains behavioral data from 5 separate sources: a physical branch, web portal, call center, VoC and CRM system.
The team now has a full picture of the cross-channel journey that is driving high support call volume and low NPS.
The team launches a new project to improve the onboarding process by addressing these two issues. As the new onboarding process is tested, the team begins to analyze its impact in comparison with the original onboarding process.
They compare the NPS provided by customers who completed the test of the new onboarding process with the NPS provided by customers who completed the original process and find a 6-point increase in NPS for customers using the new process. The new process also leads to a reduction in the rate of support calls that could yield a $2.3M savings in support call costs if applied to all new customers. Here, the CX team has used journey analytics to directly tie the impact of a CX initiative to NPS, while quantifying the financial impact on cost savings to prove ROI.
Use AI and Customer Journey Analytics to Increase Cross-Sell Rates
A global telecom provider uses Pointillist to increase cross-sell of mobile, home phone and home security products to internet customers.
Using this journey, the team compares the churn rate for internet service customers that were not cross-sold any additional services (see top path) with the churn rate for internet service customers that were cross-sold at least one other service (see bottom path).
The team analyzes the annual churn rate and realizes that churn falls from 12% to 8% for customers that have purchased an additional service.
To increase cross-sell, the marketing team needs to determine which customers are most likely to respond to a cross-sell offer and when.
Using Pointillist, the team runs a quick analysis to identify the most significant behaviors of customers that converted on a cross-sell offer in the past. Leveraging machine learning, they find the 5 most frequent and predictive behaviors shown by Internet Service customers that later went on to purchase a home security subscription as shown in the orange box.
First, a high percentage were NPS promoters. Second, customers that converted on cross-sell offers hadn’t reported any support issues in the 12 months prior to the purchase. Finally, customers that bought additional services had either recently changed their address, upgraded their bandwidth or visited the home security page on the company website.
The team uses journey analytics to define this behavioral segment, which is much more likely to convert on a cross-sell offer. They start the journey with internet customers that are NPS promoters and haven’t reported any CX issues in the past 12 months and include customers that have also had a bandwidth upgrade, address change or have visited the home security web page within 30 days of responding to the cross-sell offer.
The team looks back at past conversion rates to find customers that showed any of these three behaviors within 30 days of responding to the cross-sell offer.
By linking together behavioral customer data from 6 different sources, the team quickly unlocks a valuable potential customer segment they can leverage to focus their cross-sell efforts – an opportunity which would otherwise be nearly impossible to identify, costly and time consuming.
The team takes action on these insights by setting up a cross-channel marketing and advertising campaign for the home security cross-sell offer. They concentrate activities, communication and spend on this behavioral segment during the 30-day window after a customer first exhibits the desired behavior.
As a result, they increase cross-sells of home security subscriptions by more than 18% quarter over quarter and decrease the average cost of a cross-sell conversion by nearly 40%. With this sophisticated targeting, the team was able to greatly increase budget and resource efficiency, spending less to yield more results, while requiring less time and effort.
Putting It All Together
AI presents an opportunity for enterprises to advance their understanding of customer goals and the journeys they take to accomplish those goals.
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 organizations 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 AI as a nice to have and recognize it as a major competitive advantage. With some imagination and application, artificial intelligence can and will enhance every aspect of customer experience.