Predicting Profits With Models
Understanding which customers will buy which product or service is at the heart of personalization, a booming and still evolving industry. In addition to better leveraging existing data and analytics, numerous new and rich sources of information are available to support predictive models that target the right consumers with the right products and offers.
The first place to investigate is still your customer data and interaction history. While this may not seem new to most marketers, it’s still amazing how underleveraged this information is.
The biggest issue is a lack of coordination between transactional, CRM and Web data repositories. In addition, most companies are still trying to mine this information for fairly static decisions focused on finding the right distribution channels or the right product bundles.
Personalization algorithms offer a significantly larger opportunity, but also require a more robust view of the data generation strategy to include situational information, such as time of day, location, customer data, purchase history, etc. Furthermore, how most companies leverage their Web properties to make better marketing decisions remains a huge opportunity for matching up customers to the right products or services.
Assuming companies have online ordering and fulfillment capabilities, information, such as historical purchases, individual market baskets, common market baskets and ratings/reviews can all lead to better alignment of offers to prospects. For companies that use their sites primarily to support the customer’s information-gathering needs, activities such as product or service viewed, newsletter sign-up and content sharing are also vital information for remarketing and personalized offer development.
In all cases, advanced personalization or targeting algorithms require significant effort around classification and categorization of products, services and content. If this seems like a significant challenge due to breadth of products or services, segment-level personalization engines can be used to present some level of customization.
Shiv Gupta is a principal at Quantum Sight LLC. He helps clients develop data, analytics and digital technology strategies to drive compelling relationships with customers. In this blog, he'll discuss ways in which marketing organizations can regain their strategic bearings and leverage their tech stack for both short-term and long-term gains. Reach him at firstname.lastname@example.org.