Customer loyalty and buying behaviour depend heavily on customer experience. A recent research by Salesforce found that approximately 70% of people expect a consistent experience with businesses across various channels such as social media and mobile. The research also revealed that almost 60% of customers expect businesses to respond to them almost immediately.
Another study by Consulting firm Walker revealed that customer experience would be the most crucial factor influencing customer buying decisions by 2020, ahead of other considerations such as price and functionality.
With such heightened customer expectations, here are some examples of how businesses are leveraging AI and Machine Learning (ML) to deliver better and personalized experiences to their customers.
Leveraging AI and ML for Sentiment Analysis
What Is Sentiment Analysis?
Sentiment analysis refers to the use of natural language processing, text analysis and other AI/ML related techniques to determine the emotions of customers. This helps businesses understand individual and group sentiments towards their brand, products or services.
Sentiment analysis solutions can enhance a company's understanding of various business-critical aspects such as product perceptions, net promoter scores or customer satisfaction. A significant additional benefit of sentiment analysis solutions is that they collate unstructured data from various channels such as social media, websites or forums and deliver it as structured data to other applications, making it immediately available for analysis.
Advances in AI and Machine learning have created opportunities to apply sentiment analysis more widely and with greater accuracy to help businesses gain insights into their customers.
Here are some examples where Sentiment Analysis can be useful:
Sentiment Analysis can help businesses monitor what customers are saying about their brand across multiple channels. For example, when a business launches a new product or service, it is essential to know how customers are receiving it.
An excellent example of this is Nike's recent campaign with National Football League quarterback Colin Kaepernick. Nike received negative comments and feedback when they announced that Kaepernick was involved in their new advertisement campaign.
However, beyond the negative impact, there was a positive vibe. More people were paying attention to Nike’s new marketing campaign because of the controversy . Upon the release of the commercial, Nike sales increased more than 30%.
Taking Preventive Measures
By understanding customer sentiments towards products or services, marketing teams can take preventive measures to manage any negative impact on their brand.
Building Better Product Features
Tracking the sentiments of customers can also help product teams to determine if customers would love to see a particular feature or understand what customers think of an existing feature.
Leveraging NLP for Chatbots
What Is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on how to program computers to process and analyze human languages.
Chatbots increasingly play a vital role in customer service environments by leveraging Natural Language Processing (NLP) to provide automated, personalized responses to customers across various digital channels. Chatbots improve the overall customer experience and obtain valuable feedback on business products or services.
Here are some examples of how chatbots can improve customer experience:
Driving Engagement and Better Experience
Through the use of metrics such as sessions, bounce rate and exit rate, businesses have long sought to improve customer engagement on their digital properties. With Chatbots, businesses can interact with a customer when he/she visits your website and engage them right from the start. Since Chatbots can also be optimized to recall previous interactions made with a customer, it is easier to deliver personalized experiences to customers when they make return visits to your website.
Qualify New Leads And Prospects
Most B2B businesses use static forms to collect information from prospects. Upon collecting the information, prospects are required to wait for the company's sales or marketing representative to get in touch with them to receive the information requested.
Chatbots provide these prospects with the required information instantly and eliminate the waiting time. In return, this accelerates the process for qualifying leads.
Increase Response Rates and Qualitative Feedback on Surveys
Surveys done through traditional channels such as email, website forms and phone calls do not provide an interactive, personalized experience and result in a reduced response rate.
With chatbots, businesses can frame survey questions into more personalized, conversational messages which could help improve participant engagement and response quality.
Applying a Second Layer of AI With The Customer Happiness Index
We have seen that AI and ML are used to power sentiment analysis and chatbots, but they can also be used to analyze the output from these applications. For example, sentiment scores can be used to predict buying propensity, brand loyalty and many other business outcomes, helping to shape many business decisions. By the same token, interaction scores can be derived from chatbots to estimate customer satisfaction and perform quality assurance for customer service.
With the Customer Happiness Index, businesses are able to identify and measure key drivers that impact Customer Retention, ARPU, CLV and NPS. It automatically assigns individual satisfaction scores to the entire customer base which helps businesses drive smarter customer experience decisions.
The modern-day customer journey involves constant personalized interactions and speedy responses to customers across multiple devices and channels.
Thanks to AI & Machine learning, companies can better understand the needs and preferences of customers in order to deliver personalized content, address bad experiences quickly and drive better engagement.