“Martech” is the combination of marketing and technology to achieve marketing goals and objectives. Marketing used to be regarded of as the data center of a business since it was their duty to gather, organize, and communicate data about their consumers to internal stakeholders. It was an easy coupling to take marketing to the next level as technology evolved and grew more embedded in society.
Today, recommender systems, digital marketing, conversational AI/chatbots are all prevalent on websites that offer a service for consumption. Wearable devices, IoT, sensor technology, Internet and website tracking cookies, and more help companies to collect vast amounts of data from everyday consumers which can then be used to understand consumer behavior better and to create new products and services. Companies will be searching for new ways to achieve their marketing goals as privacy concerns continue to grow, so they can continue to track customer behavior.
One of AI’s most unique features is its capacity to collect and evaluate data very instantly. Key data from all phases of the customer experience may be organized using martech platforms, such as:
  • Web and app interactions
  • Customer service experiences
  • Social media engagement
  • Product usage – trials and post-purchase
  • Returns or complaints
  • Changes to a user profile or account information
This creates a full, robust picture of how customers get from point A to point B, along with all of the stops (and marketing opportunities) in between. This results in data unification for a more singular customer view which can help marketers truly understand their target audience.
This is crucial for the B2B customer experience, as it is rarely straight forward. It generally takes up to eight separate interactions before a prospect becomes a sales lead. In order to push that lead to become a paying customer, marketers can use AI-based tools to analyze data and set the course of action for all of the above types of interactions, such as:
  • Product recommendations
  • Natural language processing in conversational chatbots for customer service
  • Advertising and content delivery
  • Predictive modeling of demand and usage
  • Dynamic pricing and credit scoring for loans
  • Estimating transaction volumes
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