Chapter 3. AI for sales and marketing

 

This chapter covers

  • Identifying which customers are most likely to abandon a service (churn)
  • Targeting customers who are most interested in buying more (upselling)
  • Using unsupervised learning for data-driven customer segmentation
  • Case studies: using AI on electric grid data and mining retail analytics

In the previous chapter, we explored the role of structured data in a variety of business applications. Even if sales and marketing can sometimes fit into the core business data category, they’re so important and peculiar that they deserve their own chapter. We’ll cover various marketing problems and explore how you can use artificial intelligence and data science to strengthen and improve the relationship between your organization and its customers.

3.1 Why AI for sales and marketing

3.2 Predicting churning customers

3.3 Using AI to boost conversion rates and upselling

3.4 Performing automated customer segmentation

3.4.1  Unsupervised learning (or clustering)

3.4.2  Unsupervised learning for customer segmentation

3.5  Measuring performance

3.5.1  Classification algorithms

3.5.2  Clustering algorithms

3.6  Tying ML metrics to business outcomes and risks

3.7  Case studies

3.7.1  AI to refine targeting and positioning: Opower

3.7.2  AI to anticipate customer needs: Target

Summary