AI-Driven Customer Journey

Introduction

Thanks to low-cost cloud technology and recent achievements in computer processing power, artificial intelligence matured out of the academic sphere to take part in several business processes by executing smart tasks, making predictions or taking actions based on predictions.  Deployment of artificial intelligence modules in marketing and sales processes lead to better customer promotion services and support, resulting in increased prospect conversion rates.

Customer Journey

Your customers experience can be likened to that of a journey. They start out as visitors to your B2B website. They become leads when they provide you their identities through forms and other communication channels. They then become qualified, and finally customers, when they purchase solutions from you.  Structured data coming from backend systems -such as past invoices or site activity- along with unstructured ones -such as social media postings- may give you their user intent. Today, big data processing and analysis allow you to cleanse and consolidate techniques to make this data more useful.

Based on this user intent prediction, you can apply a scoring system to increase your lead conversion rate. You may also increase customer engagement with marketing methods such as account-based marketing based on predictive models. Personalized promotion, content, and after-sales services  become feasible due to predictive and prospective analysis tools and techniques.

Machine Learning and Artificial Intelligence

Artificial intelligence is present throughout the whole customer journey:

  • Rule-based systems such as chat bots or natural language process modules that accelerate pre-sales and after-sales services,
  • Recommender systems that categorize user intent, predict user preferences, and provide personalized services,
  • Machine learning models that provide predictions and successive actions over the user intent

All the above have become feasible due to computational statistics. Instead of applying a “global” if-then-else approach, you can now take a historical set of data and find similarities amongst the information. You can then categorize or find a model that describes the best data in the set. You can then compare new comer intent data against the model, categorize them, and then make predictions based off their content. Using this prediction, you can deliver personalized content, advertising, notifications, and other customer support services. In the end, by defining metrics, you can monitor your customers to make any necessary adjustments.

Table of Contents:


    1. Predict User Intent with Machine Learning (AI, Recommender Systems)
    2. Building an ABM Foundation Data-Driven account selection and modelling
    3. Predictive lead scoring features and challenges compared to traditional
    4. Predictive prospecting in B2B space and how can we assess user intent using 1st party, 2nd party and 3rd party data
    5. AI-Powered B2B Content Personalization Based on Live User Intent
    6. Artificial Intelligence in Digital Process Automation

Conclusion

Despite certain challenges and requirements, artificial intelligence-driven analysis can boost the efficiency of your marketing and sales processes. Today, more and more marketers seek prediction models for user intent, so they can deliver personalized marketing services.

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