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
Today, leading enterprises have sorted through the hype surrounding artificial intelligence (AI) and are investing in sophisticated technology to augment their customer experience efforts.
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 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
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 management solutions 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, actionable insights must be shared in real time. Typically, these insights are inaccessible to departments across the enterprise, so customer experience, marketing and other teams flood analysts with question after question.
AI accelerates analysis, finding meaningful relationships within even the largest data sets. The speed at which AI can reveal insights allows analytics teams to answer internal requests faster than ever before.
Of course, all of this is predicated on the idea that customer journey data sources are unified. But most enterprises rely on a myriad of on-site, home-grown and legacy touchpoint data sources—voice of the customer (VoC) feedback, payment platforms, point-of-sale systems, customer care, etc. This reality creates a challenge for delivering insights, which is still very much a custom, laborious affair.
However, customer journey analytics platforms are now filling this gap with the ability to connect the dots between customer behavior and business outcomes, enabling cross-functional teams to collaborate on journeys and better manage, measure and optimize those journeys.
Business Context
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
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
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.

[Image Source: forbes.com]
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.

AI-enabled customer analytics mines a vast, dynamic sea of data for actionable insights
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.
Take a look at the following use case, which is a great example of how customer journey analytics leverages AI to identify the root cause of a problem and predict which customer behaviors result in undesired actions, such as a support call.
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.
Thank you for this article. AI helps in answering basic queries which allow humans to tackle more complex problems and improve the speed and efficiency of decisions.
Artificial Intelligence is one of the most trending topics in today’s date. It has its application in all industry.
I like this blog very much. It gives me really great information. Keep sharing this. Thank you….!!!!!
Artificial Intelligence seems to be one of the most interesting and revolutionary concepts that can effect the business world. I look forward to seeing how these systems reach the forefront for more and more companies.
I simply wanted to write down a quick word to say thanks to you for that wonderful information you are showing on this site.
nice post!
Great read!
Nice post. AI technology is used in every field including for industry, home use and also for others. Therefore it is a Hot topic in these days.
AI is surely the future in technology as it has a combined form of capabilities such as machine learning, natural language processing, knowledge management, and machine vision. Thanks for the post!
The technology of Artificial App Development has completely matured to realize the concept of self-sustainable and context-aware systems. Thanks for your valuable info.
Thanks for sharing an informative content on AI and indeed AI is in every field including for industry, home use and also for others
Wow, it is a really great article to read. Thanks for sharing such good informative information about the AI.
Thanks for sharing great information about the AI. It is more helpful to us.
Inspirational, I am feeling motivated and now work harder to start the career in Artificial Intelligence, hope will get similar success. Thanks for sharing your Artificial Intelligence experience.
Excellent blog.
Hey Will! It is a really great article to read. In my point of view, the development of AI is not about replacing humans but creating a partnership between digital capability and human ability. AI is changing the customer experience and no doubt, AI will be the cornerstone in the future of customer service for customers and call center agents. What say?.
Who doesn’t appreciate customer support with fast response and uninterrupted service? One of the surprising benefits of using AI for automating responses is its independence from time constraints and holiday offs. This means that at any given moment customers will be able to interact with AI robot to resolve issues. Such uninterrupted customer service helps organizations stay responsive 24/7 to address incoming customer inquiries. As there will be an assurance of consistent support, problems faced in case of human customer service reps will be effectively eliminated.
Thank you for the great post.
I agree with your points, the greatest customer experience that AI can give is the real-time delivery of data they needed. Thanks a lot for the informative blog.
Chatbots are AI-based conversation agents that 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.
Thank you for the post! I was wondering while reading the post which companies are the leaders in offering AI solutions for CX?
Hi Angy. Vendors generally offer two kinds of solutions: 1) generic AI toolkits that require software development to customize them to your specific needs, and 2) AI and machine learning capabilities that are embedded into their existing CX tools. The question to ask yourself is what are you looking for AI to do? If it’s something unique, then look at generic AI toolkits. If your need is to improve the insights you get from an existing CX tool, then look to the vendor of that kind of tool. Pointillist’s AI and machine learning capabilities reveal customer insights that can help you better understand the customers and specific behaviors that are driving your KPIs (for better or worse).
People who work for tech companies tend to offer the most positive outlook on the future opportunities that will be afforded by AI. AI will be an essential factor in helping people to feel comfortable with adopting these new technologies.
Thanks for sharing the good information about the AI. It is an Excellent Blog.
Great article I learned so much thank you. I will never look at Sephora’s webpage the same way anymore.
Great Stuff. Thanks for sharing
Hey, very nice site. I came across this on Google, and I am stoked that I did. I will definitely be coming back here more often. Wish I could add to the conversation and bring a bit more to the table, but am just taking in as much info as I can at the moment. Thanks for sharing.
Dear Will,
Nice article. I enjoyed reading it. Keep rocking!
Hi Will,
Such an great post. In this competitive world it has been such an tough task to influence the customer to become an client. So, AI really place an important role in this process.
It is really a fantastic blog post and i have learned so many new things today. Thanks for sharing
Good information… I have learned a lot from this blog.
such a great article.
Businesses are increasingly relying on AI to capture data, analyze it, get real-time insights into customer behavior, and use it to personalize communication and enhance the customer experience. However, determining how to adopt AI into business processes – developing the right process as well as a model to reap all benefits is challenging and requires expertise.
informative post, thank you for sharing.
Nice article, Thanks for sharing
Awesome blogs Will, I enjoyed reading it as well as get some new information too. Thank you so much for sharing. Keep writing
This is so nice, thanks for the article.
fantastic information. thanks for sharing
Really nice post. I rarely comment on any article but I think this deserves a BRAVO.
I love your blog, Very informative post that resolved all me queries, keep updating, Thanks
Thanks for sharing nice information and nice article and very useful information…
Excellent post. Gained a lot of knowledge from it. Looking ahead for more of such interesting postings
Good Post. Thanks for sharing
AI chatbots aren’t a replacement for real human interactions, though. Bots are better at augmenting these interactions and are best utilized to simplify tasks and remove repetition from workflows. Human agents should handle conversations where someone is navigating a complex purchase or upgrade or is feeling frustrated or confused.
Excellent Article/Blog, Very informative post that resolved all me queries, keep updating, Thanks
Was in search for this information from a long time. Thank you for such informative post. Looking forward for more of such informative postings
informative Blog that Helped Me Alot, keep updating, Thanks
Blog that Helped Me Alot, keep updating, Thanks
Hey Will,
According to a recent Zendesk study, as much as 42% of B2C customers showed more interest in purchasing after experiencing good customer service. The same study also goes to claim that 52% of them stopped purchasing due to a single disappointing customer support interaction.
Really Good Read.
Keep Posting such article.
Great Post. Very informative and keep sharing.
Thank you for sharing this informative post. Looking forward tor read more.
Hi, Thanks for sharing nice articles…
As AI is increasingly incorporated into CX it will become necessary to trust the AI’s decisions. Imagine missing out on a profitable customer segment because a customer lives in a zip code that is known for being disadvantaged and the model is predicting that because of this feature they are less likely to buy a product or use a service. This is not necessarily true and because of this bias the business looses revenue from an underserved segment. Explainable, trusted AI will be necessary to fully realize the benefits of AI in CX.
The zip code bias example is one of many, are their other examples that quickly come to mind in CX?
I feel very grateful that I read this. It is very helpful and very informative and I really learned a lot from it.
view more
Very nice info would love to read such kind of blogs was very helpful indeed
I just got to this amazing site not long ago. I was actually captured with the piece of resources you have got here. Big thumbs up for making such wonderful blog page!
visit us
You actually make it look so easy with your performance but I find this matter to be actually something which I think I would never comprehend. It seems too complicated and extremely broad for me. I’m looking forward for your next post, I’ll try to get the hang of it!
view more
Truly, this article is really one of the very best in the history of articles. I am a antique ’Article’ collector and I sometimes read some new articles if I find them interesting. And I found this one pretty fascinating and it should go into my collection. Very good work!
view more
I just got to this amazing site not long ago. I was actually captured with the piece of resources you have got here. Big thumbs up for making such wonderful blog page!
visit us