A Guide to Marketing Technology and CX

This article was written for CustomerThink by Greg Kihlström. The strategic management of Customer Lifetime Value (CLV) has become increasingly critical for securing both short-term gains and long-term financial performance in the enterprise. Both first-hand findings and recent research sheds light on the nuanced relationship between effectively measuring and improving CLV and its consequential impact on an organization’s financial performance.

Customer lifetime value (CLV) is a crucial metric for businesses to understand and prioritize. It refers to the total revenue a customer is expected to generate over the course of their relationship with a brand.

Personalization enhances marketing effectiveness by creating a more personalized and tailored experience for customers. This is especially important in the B2B space, where sales and marketing need to work together to effectively reach and engage potential buyers.

As artificial intelligence grows in importance, it reinforces the idea that organizations must prioritize access, visibility, and action with data. It drives decision-making, informs strategies, and helps businesses better understand their customers.

Phone conversations can provide valuable data that helps businesses identify and qualify high-quality leads. By analyzing the content and context of these conversations, organizations can gain insights into the needs, preferences, and behaviors of their customers. This data can be used to determine which leads are more likely to convert and become valuable customers.

Customer-centric personalization using AI is a strategy that focuses on tailoring marketing and customer service efforts to individual customers based on their preferences, behaviors, and real-time context. This approach moves away from traditional product-centric campaigns and instead puts the customer at the center of the marketing and service efforts.

Omnichannel excellence has become a key focus for retailers in recent years and will continue to be in the future. This concept refers to the seamless integration of various channels, such as brick-and-mortar stores, online platforms, mobile apps, and social media, to provide customers with a consistent and excellent shopping experience.

Organizational alignment is key when it comes to the successful deployment of machine learning projects in the enterprise. As discussed in the podcast interview with Eric Siegel, the biggest mistakes that organizations make when it comes to machine learning projects are often organizational rather than technical. This highlights the importance of aligning the business and technical aspects of the project, as well as ensuring that all stakeholders are on the same page.