AI-Enabled Avatars, Robots and Door Greeters
Although some brands do use facial recognition to identify customers, the practice is viewed as invasive by the majority of consumers. Instead, many brands are using AI and location-based apps to be able to personally greet customers. As long as brands are transparent about their use of location information within their apps, most customers are happy to provide that information — and it is not shared unless they have personally given permission to the app. This enables brands to send notifications to their customers when they are near a brick-and-mortar brand outlet, as well as facilitating the personalization that customers expect across all of a brand’s channels — including its physical presences.
The use of AI and robots for greetings can get tricky because if a robot seems too human, it becomes creepy. Seth Siegel, managing partner and North American head of AI at Infosys Consulting, a management consulting service, shared that brands must be careful and tread lightly in these areas that tend to creep out customers. “There’s a concept known as uncanny valley, which is the negative emotional response felt by people when robots seem too human. Overall, people are quite comfortable, and often entertained, if they know it’s an automated response; we venture into super-creepy territory the further into the uncanny valley we venture,” said Siegel.
Personalized AI-Powered Chatbots
AI-powered chatbots are not limited to scripted or rule-based conversations. By using NLP and machine learning, AI-powered chatbots are now able to understand the context in a sentence and can carry on a full conversation with a customer.
Today’s AI-based chatbots have evolved exponentially since the first scripted, rule-based chatbots began to appear. “Instead of a multi-step process, AI can guide customers towards the content they need and provide targeted, personal responses to their queries. Personalized answers are efficient for the companies who don’t have to hire an army of customer support representatives, and for the customer. AI has evolved to the point where it can understand nuance, sentiment, and context in language, which has turned chatbots into a must-have feature,” said David Hegarty, VP of digital solutions at R2integrated, a digital experience agency.
“Wrapping aspects of your business in bot technology — that is, first contact by a lead, potential or existing customer or a customer service issue handled by a bot rather than a human is a growing priority for businesses. Beyond the obvious cost-reduction value of having bots versus humans handling engagements, the growth of bots aligns with the increasing trend, begun prior to the pandemic, that people really don’t want to talk to sales, service or marketing people at all anymore. They want to reserve their personal time for friends and family and would rather engage with a bot than have a conversation or even a chat with an individual,” said Rich Green, chief product officer and chief technology officer at SugarCRM, a CRM platform provider.
A report from Epsilon revealed that 80% of customers are more likely to do business with a brand when the brand provides them with a personalized experience. Additionally, a report from Accenture showed that 91% of those polled said that they are more likely to purchase from a brand that knows them and provides them with relevant recommendations and offers.
Hegarty said that AI can be used to facilitate self-service, something that many customers prefer over having to talk with an agent. “AI is ushering in the era of highly personalized customer experiences. Marketers used to target broad demographics with their campaigns: male-female, 18-35, urban-rural. With AI, that approach seems increasingly archaic. The next generation of marketing will involve intensely personalized experiences, made possible by the vast amounts of customer data out there. The businesses that are stuck relying on broad demographics will become irrelevant when put up against segments of one,” said Hegarty.
AI applications enable brands to speak directly to each customer, rather than generalizing audience segments. “Through the combination of the right tools, AI can be used to speak the language of individuals, instead of these broad demographics,” Hegarty explained. “With the combination of a CDP and AI/ML, this powerful personalization is possible at a reasonable acquisition and implementation cost, compared to conventional databases.”
Due to the ability of AI to process large amounts of data in real-time, brands are able to use AI to personalize content for each specific customer based on their purchase history, customer service tickets, and browsing patterns. By using this customer data, AI applications are able to provide customers with the content that would be most appealing to them, including factual information, images, videos, instructional material, or community discussion forums.
When asked how AI can effectively be used to deliver personalized content, Siegel said it comes down to getting the right offer to the right segment. “It’s not about individualization, it’s about mass customization. AI allows any data cohort to get the right offer at the right time, provided the models being used are created with an awareness of, and design to prevent, unseen bias.”
According to Jared Ficklin, chief creative technologist at argodesign, a product design, experience, and innovation firm, AI-based personalization is lowering the cost of healthcare, and that in future, AI-based personalization will play an even greater role. “In 2022 an AI will send you a text warning you to take your allergy medicines. Healthcare systems are finally using AI and those systems have been maturing rapidly. What once crunched data for insights or attributes on patients now can model care plans,” said Ficklin.
Along with reducing the cost of healthcare, AI is facilitating better treatment of patients, and a more personal touch. “This carries with it the promises of more personalized care, simple things like prescription reminders and seasonal alerts could be done on a per patient basis,” said Ficklin. “This tech ultimately could drive a constant refinement of treatment protocols, learning what works best through modeling and simulation. The net effect is fewer people needing last minute emergency treatment and better overall patient care outcomes.”
Personalized Ad Targeting and Product Recommendations
Through the use of AI, especially when combined with a CRM or CDP, brands are able to provide customers with personalized ads based on demographics, purchase history, and browsing habits. Machine learning algorithms are able to comb through very large, constantly changing historical data sets and by understanding that data, they are able to predict which products a user wants to see next.
Much like the recommendations customers see on Amazon or Netflix (i.e. “customers that bought this also bought these”), AI-based recommendation engines are able to provide recommendations for products and services based on each customer’s previous purchase history. In this way, by using information that customers expect a brand to know and remember, the recommendation experience is helpful, rather than creepy.
In order to accomplish this, machine learning models are trained to discover patterns within very large historical data sets. These data sets include online shopping behavior, offline point-of-sale data, along with the historical outcomes that are desired. The machine learning models then predict the next best outcome — typically the products and services the customer wants to purchase next.
Customer Sentiment Analysis
AI is being used for customer sentiment analysis by analyzing voice, image recognition, and behavior in order to better understand customers’ emotional states, and what they need and expect from the brand. Customer sentiment analysis refers to the automated process of interpreting emotions in communications in order to determine how customers feel about a brand’s products or services. Typically, the customer communications come from online surveys, product reviews, customer support tickets, and social media posts.
Green told CMSWire that another area of midterm likely hyper-growth is that of sentiment analysis. “That is, using AI to get a read of the emotional state of someone engaging with an individual (or a bot) at your company. Here’s an example: A customer service agent is having a voice call with a customer who is rather irate and, based on AI analysis, is conveying a growing level of dissatisfaction with the agent or bot,” Green explained. “With real time sentiment scoring, once a dissatisfaction threshold is reached, that call can be routed to someone who is better equipped to handle the discussion. Matching can be done based on the sentiment score and the breadth of agent profiles to match up with (or escalate to) the best individual to handle that individual’s concern.”
AI has evolved exponentially over the last decade and isn’t showing any signs of slowing down. By using AI and ML, brands are able to provide personalized AI-powered chatbots, deliver personalized content, messaging, and ads, along with product recommendations. Finally, AI is being used for customer sentiment analysis in order to gain a better understanding of how customers feel, and what they really want from a brand.
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