- Intelligent chatbots help create a personalized customer experience
Fast resolution and quick, efficient responses are one of the most important things that customers look for when interacting with a brand. With the introduction of AI-driven chatbots, delivering such an experience to customers has become much more convenient. Chatbots can give a more “human-like” feel by using everyday conversational language. It can answer basic questions, solve simple issues, and track and fulfill orders. They are available 24/7 and can significantly decrease customer wait times and result in better customer satisfaction.
With the help of intelligent chatbots, companies are providing customers with intuitive, responsive, and dynamic communication that addresses customers needs effectively.
KLM; a Royal Dutch Airline recently invested in this technology by introducing KLM’s BlueBot, which provides a highly personal and timely service to its customers. As a result, KLM’s passengers enjoy excellent service, while the company gets more insights it needs to succeed. On the home-front, chatbots like Lara.ng helps city dwellers in Nigeria determine the best routes and prices for various forms of transportation.
- Predictive analysis can help determine customer purchase patterns
AI personalizes a customer experience based on their past behavior and can also guess behavioral patterns for new and existing customers. With a myriad of data management platforms collating second and third-party data, AI can now crawl the internet collecting information about your customers.
This data can help to personalize services to customers’ needs automatically, through user-journeys and profiles. This enables marketers to target potential leads and rule out those who will be unlikely to convert. With these processes in place, businesses can spend more time creating and executing effective marketing strategies.
Companies such as the American retail store chain; Target have shone the spotlight on predictive analytics to masterfully predict the purchasing patterns of buyers. In 2012, the retailer made headlines when they were able to correctly predict the pregnancy of a teenager based on her purchasing habits, before she had even discovered she was (unexpectedly) expecting.
Read more on this bizarre but very real applications of artificial intelligence via: https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#62f9e8806668
- Content personalization
AI-based clustering and interpretation of consumer data together with customer profile information, demographics, etc. enable systems to continuously adapt to customers’ likes and dislikes. Recommendations are, therefore, tailored in real-time.
The viral video sharing app; Tik Tok has taken this application and ran further with it. The app is known for its content recommendations which closely match user behavior patterns and interest and keeps more viewers glued to their screen.
Companies like Amazon, Netflix, YouTube, Spotify, and many others have also built their product offerings in a way that provides the most relevant and personalized product or content recommendations.
- More reliable audience targeting and segmentation
Ad targeting matters, often as much as the actual ad copy and creative. Platforms such as Facebook, LinkedIn, and Google offer data-rich algorithms to target audiences with incredible precision but doing this manually is often unsustainable. By leveraging AI, marketers can reach the right audience and understand how consumers react to campaigns, different types of creative, and different channels. Advertisers can hyper-target consumers, optimize targeting tactics in real-time, and tune the overall media delivery based on consumer behavior.
Moreover, AI has introduced dynamic segmentation that takes into consideration the fact that customer’s behaviors are changing constantly, and people can take on different personas at different times due to different buying needs and reasons.
Norwegian Airlines partnered with AdTheorent to use machine learning to drive flight bookings.
AdTheorent developed custom machine learning models to target users who were deemed most likely to engage with a specific ad and then complete a booking. To track customer actions after exposure to the campaign, AdTheorent placed pixels on the booking website, and leveraged the pixel data, to optimize campaign delivery towards consumers most likely to purchase tickets on the booking site.
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