A plethora of data with a number of analytics options
There isn't a business today that doesn't collect data on the experiences, behaviors and buying habits of their customers.
Restaurants use surveys, mystery shopping audits and social media to better gauge the voice of the customer. E-commerce businesses monitor user accounts to track customer purchases and preferences. Casinos use loyalty cards to track the games that customers play, how long they play and how much they spend. And these data collection routes are just the tip of the iceberg.
But once all this information is gathered from multiple communication channels, what do you do with it? Consumer analytics tools powered by AI, machine learning, natural language processing and more can help discover and analyze patterns from all of these disparate sources to improve CX.
Customer analytics continues to be one of the biggest CX investments, according to Gartner's 2019 Customer Experience Innovation survey; 52% of 244 respondents say they're planning to increase funding in 2019. The survey also found that artificial intelligence, virtual customer assistants, chatbots and omnichannel engagement tools will have the greatest impact on CX projects.
This guide explores how consumer analytics uses AI to find patterns in the collected data and make predictions to help formulate sales and marketing strategies. We also look at how businesses use Salesforce, IBM and Acquia AI tools in the financial sector for added security and personalized insights. And finally, an intelligent ticketing agent from Broadw.ai shows how AI uses analytics to gauge customer responses when searching better ticket prices for users.
