Multichannel AI platforms and artificial intelligence are leading trends in technology. AI is growing in popularity for sales professionals, business owners, and marketers worldwide and is now an essential tool for brands that wish to provide hyper-personalized, exceptional customer experience (CX).
In fact, businesses must leverage their customer contact center data to be their fundamental CX differentiator. AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software are widely available to businesses without the high costs that were previously associated with the technology.
Research states that 39% of IT leaders are currently using AI or machine learning and we expect 33% to use AI within the next three years, and 19% expect to use it within five years. Undoubtedly, AI will be at the heart of your customer service contact center strategy and design.
Everything that is effective or not with your customer experience will manifest including every inquiry, compliment, and complaint. It is our goal to equip you with essential tools to ensure you create brand ambassadors rather than unsatisfied critics. Technology allows us to listen deeply to our customer’s voices but only if you are willing to listen.
Better yet, multichannel AI platforms have various applications for business owners and in this article, we will discuss 5 essential ways you can implement AI to boost your customer experience.
Multichannel AI Platforms And Why You Need Them
There is an invaluable amount of customer data being held in your CX strategy which flows in and out of your contact center every hour. Imagine how confusing it gets when you are trying to track all this data manually trying to figure out who they are, where they live, what they purchased, their reasons for making contact, whether their queries are being resolved, and what their pain points are.
That is why we suggest multichannel AI platforms to rapidly digitalize your business and service processes. Now, post-Covid it is even more important to leverage the value-added data science and analytics capabilities to provide deep insights into your customer journey from point of product origin to warranty fulfillment.
Multichannel AI platforms can boost customer experience significantly by being able to pre-emotively send out courtesy messages to advise clients of issues before they happen or help you foresee solutions to better meet customer expectations by changing the outcome of your services.
Using data and analytics to your advantage is gold for adding value across your business and to the customer. Look at these essential ways to turn your contact center into a highly strategic business partner and an enabler in a world where CX is fast becoming the only differentiator.
5 Essential Ways To Boost Customer Experience
- We know that contact centers have an infamous reputation for having interactions go around in circles with no actual resolution of customer problems.
- However, we can leverage technology to facilitate endearing client experiences so businesses can build a strong following of loyal customers.
- Therefore, businesses need to consider implementing multichannel AI platforms to build seamless backend processes, which help resolve customer problems expeditiously.
1. Understand Your Customer With AI
Using multichannel AI platforms and machine learning helps gather and analyze social, historical, and behavioral data which allows businesses to gain an accurate understanding of their customers. Unlike traditional data analytics, AI is continuously learning and improving from the data it analyzes and can anticipate customer behavior.
This is a powerful asset as it allows contact centers to improve their everyday interactions with customers. Effectively delivering excellent experiences happens when all business processes work together and efficiently by using AI tools for common goals. Therefore, businesses can connect with customers personally which builds loyalty and trust post-pandemic.
Better yet, we can also use AI to recommend the next best actions for the customer by learning how interests and insights reflect the needs of similar customers.
2. Real-Time Decision-Making & Predictive Behavior Analysis
Using multichannel AI platforms helps us to make decisions based on the most recent data that is available. This is called Real-Time Decisioning. For example, we can collect data from the current interaction that a customer is having with a business, with near-zero latency.
Businesses can use Real-Time Decisioning for more effective marketing to customers. For example, identifying customers who are using ad blockers to provide them with alternative UI (user interface) components that will continue to engage them.
Multichannel AI platforms can also provide personalized recommendations to present relevant information to the customer. Moreover, AI helps us to recognize and understand a customer’s intent through the data which helps businesses hyper-personalize relevant content and offers to customers.
Predictive Analytics In Real-Time
Multichannel AI platforms can analyze large amounts of data in a short time which allows contact centers to capitalize on predictive analytics. This is a process of working with statistics, data mining, and modeling to make predictions.
Using this technology allows businesses to produce real-time, actionable insights that guide the next interactions between a customer and a brand. This is often referred to as predictive engagement, and it requires the knowledge of when and how to interact with each customer, which Ai is excellent at.
Multichannel AI platforms and predictive analytics can go further than historical data to provide deeper insights into what has already happened. The software then displays what can be done to facilitate a sale through suggestions for related products and accessories which makes the CX more relevant and likely to generate a sale and provide the customer with an emotional connection to the brand.
3. The Future Of AI Chatbots
Research shows that CX via chatbot technology is the leading application in AI today. 73% of respondents indicated that by 2022, chatbots will still be the leading use of AI in companies, followed closely by sales and marketing at 59%.
In addition, 54% of customers said they have daily AI-enabled interactions with businesses, including chatbots, digital assistants, facial recognition, and biometric scanners. Moreover, 49% of those customers found AI interactions to be trustworthy which is 30% more than those in 2018.
However, multichannel AI platforms using chatbots recognize that they do not replace human contact. Instead, modern businesses must view them as a supplement to human employees which helps them to be as efficient as possible. There is an important balance between self-service and human interaction to deliver the most convenient experience possible.
Chatbots are a valuable tool that saves businesses money while allowing customers to take care of minor issues in their own time. Remember that you cannot expect chatbots to understand everything and still perform well. Rather, use multichannel AI platforms and chatbots to tackle a select number of topics such as invoice management, order tracking, and account management. In fact, chatbots can accelerate the handling of queries regarding invoice management by 2-3X.
4. AI for Hyper-Personalization
Multichannel AI platforms combine AI and real-time to deliver content that is specifically relevant to a customer. Businesses and consumers are embracing conversational AI because it provides personalized experiences that are quicker and more convenient than traditional ways of interacting with businesses.
Do you remember waiting on hold for a phone call or clicking through tons of pages to find the correct information? Now, personalized experiences and AI can help to eliminate the pain points in the customer journey.
Most clients’ frustration occurs when they need to repeat information they have already shared. For example, re-confirming a phone number or having to re-explain a problem to multiple agents. Tools like cloud services allow conversational AI to connect to conversation histories and customers’ previously stated intentions, and other data which offers a much more personalized service.
5. Overcome The Challenges of AI
Multichannel AI platforms sometimes face certain challenges like customer data being spread out among different channels and disparate systems which are mostly siloed. Before AI can analyze the content, all the data needs to be unified.
There is an exponential amount of data produced throughout the customer journey which is why we suggest using our Customer Data Platforms (CDP) to unify and analyze that data. Our multichannel AI platforms can enhance profiles with customer scores, create more effective customer segments, and design new data visualizations.
Sometimes costs are a challenge for businesses, however, they are under the misconception that AI is overly expensive. The truth is that when AI is used effectively for customer experience, be it for real-time decisions, personalization, or customer service, the return on investment can easily be validated through analytics.
More importantly, brands who want to use AI chatbots for handling customer inquiries and general CX requests must recognize that AI will never replace human interaction. You will always need a customer service agent to deal with requests that the AI chatbot cannot fulfill.
Analyzing customer data and listening to their voice across all the channels of service delivery in your contact center provides you with an opportunity like never before to build adaptive, responsive, and resilient customer service models that safeguard and enhance CX as the fundamental differentiator that it is.
The only way we can embrace multichannel AI platforms is by knowing where to draw the boundary with using AI in customer service. AI often lives as an external service and organizations are struggling to find the balance between sharing enough information to get a meaningful, positive impact on customer experience while respecting the privacy and data risk of their customers.
However, on the technology side, we know that AI needs to be tuned and optimized for each specific application which is where a lot of the investment is today.
In time, AI will be able to tune itself to the application and new challenges will start to arise.