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The Role Of AI In Customer Experience
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.
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.
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 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..
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.
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.
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.
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.
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.
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.
Artificial intelligence-enabled customer journey analytics can find answers to important CX queries like: 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.
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.
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.
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.
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.
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.
It involves modeling and algorithms and how we make sure we get the very best from them and capture the growth potential while protecting against the downside risks.
Of course, today we can collect, store and call up the data with the aid of technology that was not even available just five years ago.
It can dispatch messages when a payment is pending or when we need to settle an invoice, it can motivate clients to save more and detect unusual account movements.
Two examples of AI in action for our commercial clients include chatbot for digital banking—which helps customers make payments, check balances, save money and pay down debt—and intelligent receivables, a comprehensive invoice-matching service that brings together payment information from multiple payment channels and associated remittance detail from other sources.
Right now, we're focusing on conversational experiences, data science/machine learning, and decision intelligence—leveraging data and AI to create better, faster and fewer decisions—across industries like financial services, agtech, insurance, B2B, healthcare and more.
Venkataraju: CGN recently started focusing on automating work activities across multiple industries, monitoring social media to assess brand penetration, understanding customer interests and developing relevant marketing content for targeted audiences.
If it's in a customer-facing situation, companies can create impactful and differentiating experiences with their buyers and consumers—anything from using machine learning that anticipates a buyers' need, to natural language conversations with quickly advancing voice assistants and chatbots.
are leveraging AI internally, they have the opportunity to make better decisions using their existing data or even to automate decisions using techniques like intelligent robotic process automation.
Other benefits of AI include new revenue generation, cost reduction and insights leading to a better customer experience.
For example, our firm helped an online retailer analyze consumer behavior and recommend products aligned with consumer interest.
Other examples include virtual servicing /concierge, improvement of self-service tools, automation of back-office processes, and advanced portfolio diagnostic tools.
We've used AI to streamline client on-boarding, and for fraud detection, which continues to be a huge concern among commercial business clients.
DeCoste: Businesses that deal with vast amounts of data and rely on that data for servicing, production, marketing and product innovation will see the biggest impact from AI.
Top-line-oriented functions, such as in marketing and sales, and bottom-line-oriented operational functions, including supply chain management and manufacturing will also benefit.
It has the potential to improve functions across the entire business system from managing human capital to supply chain and customer service.
Our firm has led design and implementation strategies with Fortune 500 and mid-sized companies, successfully resulting in great sustainable efficiencies.
If you're working not only to experiment with technologies but also to gain buy-in from stakeholders, choose a relatively focused project that can get you a quick win within 12 weeks or so.
Whatever model is used should be vetted with a company's finance department to ensure it meets the accounting principles—fluff simply won't work.
Venkataraju: We recently provided visibility and insights into a customer's supply chain by helping them identify bottlenecks with their multi-tiered external and internal suppliers.
For example, through conversational user experiences and machine learning, we've trained AI engines to enrich customer support experiences and customer activities.
We've also used image recognition and sentiment analysis--visualize a camera that can see you and understand how you're reacting to things—to enhance our clients' products to give them a competitive edge.
For example, as we create more AI-driven tools like our virtual financial assistant Erica, new roles are opening up based on a better understanding of our customers' needs and how to advise them.
Artificial Intelligence and the Evolving Business Landscape
As an insurance Principal, Dave spent a majority of his 20-year career driving claims and underwriting operational effectiveness before taking on a cross-sector role driving artificial intelligence and conversational UI enabled transformation efforts.
Aside from insights, his focus is on performance improvement and operating model design across all aspects of front office and back office insurance operations including: predictive analytics and strategic pricing, process automation and business rules, claims operations, sourcing and procurement, and customer experience.
- On 15. oktober 2021
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