Enhancing CX on Social Messaging Channels Using AI and APIs

    Enhancing-CX-on-Social-Messaging-Channels-Using-AI-and-APIs
    Enhancing-CX-on-Social-Messaging-Channels-Using-AI-and-APIs

    Delivering exceptional customer experiences (CX) that give consumers control over their purchasing strategies, finally comes down to the right tools and good data.

    Customers today expect a seamless, personalized experience across all of their preferred channels, and they want to be in charge of the story. By combining conversational artificial intelligence (AI) with strong data and analytics, brands can create an amazing customer experience (CX) based on the consumer’s past interactions and current session data.

    Natural language processing (NLP), automatic speech recognition, sophisticated dialog management, deep learning, and machine learning (ML) are all used in conversational AI, which is sufficiently advanced to be able to pass the Turing Test.

    Up until recently, conversational AI was not really conversational, and businesses had to develop strict rules to account for every conceivable user intent and reaction. Large, pre-trained language models are tremendously helpful tools today, but they cannot fully understand language.

    Brands can utilize these models in place of programming rules to discern meaning flexibly and accurately and communicate in a more casual, less scripted manner.

    How Conversational AI Enhances the Customer Experience

    AI-powered chatbots may make decisions that are more complicated and fluid than those made by humans, making them predictive and personalized. These AI bots can observe user-specific traits (location, age, mood, and gender), learn conversational styles from prior interactions, and even perform actions using tools like robotic process automation because they have access to a customer’s previous interactions, typically through customer relationship management (CRM) software (RPA).

    Depending on the unique use cases for each brand, conversational AI may or may not be appropriate for that brand.

    How Analytics Affect Customer Experience

    The first step in customer analytics is gathering and combining data from all relevant sources, such as websites, mobile apps, emails, chat, social media, support tickets, and in-person visits. Brands may utilize that data to provide a comprehensive 360-degree image of each customer once it has been unified and organized.

    Also Read: Top Three AI Marketing Challenges to Overcome

    The secret to creating the best experiences is using analytics to ascertain whether a user finds the solution they’re looking for. Because every user will have a different core perspective on conversing with a machine, the optimal data sets for this purpose combine quantitative measures and qualitative metrics.

    Customer analytics can be used by businesses to personalize customer interactions and support customer care requests. Real-time information can be used to direct live questions to the most qualified agents at the start of the client contact. Brands are able to take the “next best option” in the consumer journey by interpreting and evaluating this data. Conversational AI represents a significant advancement, but organizations must be willing to tap into the vast untapped resource of unstructured text data if they are to succeed. Customer data has the solutions to fresh engagement tactics.

    First-party data must be used wisely because third-party cookies are being deprecated continuously and more and more. Businesses need to take a close look at the main data repositories that they already have and that they are constantly generating in order to engage with customers in an environment that is becoming more competitive and noisy. Marketers are seeking creative ways to understand their audiences because third-party cookies will eventually become less and less prevalent.

    Challenges that brands face with implementing AI

    Today’s brands want to be known for the outstanding customer experiences they deliver, so they need to be able to track and enhance those experiences on an ongoing basis. This real-time customization fosters a favorable emotional connection and demonstrates the brand’s responsiveness.

    Also Read: Shifting B2B Marketing Focus from Solution Based to Customer Centric Marketing Approach with Personas

    Nevertheless, designing such an experience presents a number of difficulties, particularly for businesses utilizing different technologies. A connected personalization strategy is a far better option for organizations looking to create a seamless experience.

    This is accomplished by putting an agile SaaS layer between the existing backend systems of record and the piles of point solutions. Brands can stack and compare data to increase its value and acquire more complete and accurate perspectives of audiences. AI, ML, and NLP may analyze and apply consumer data that has been cleaned, organized, and optimized by marketers to improve the customer experience.

    While allowing customers to steer their own narrative, conversational AI enables businesses to offer customers digital agents who are familiar with their shopping, purchasing, and service history. This enables a personalized conversation while allowing live agents to handle more complicated inquiries.

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