Customer Behavior Analytics
Data analytics as a key component of high quality and transparent customer lifecycle management
If embedded in the organization and its business processes, effective Customer Behavior Analytics can enable powerful customer interaction strategies along the various dimensions of the customer lifecycle (CLM). Based on our past experiences, Deloitte identified major pitfalls and milestones on the way towards an efficient, data-driven CLM.
Today, markets develop with increasing pace and customers become increasingly demanding. As users of online services such as Facebook and Google they have become accustomed to top-notch service quality and generous offers. They expect businesses to approach them in an equally personalized manner. Caring about one’s customers and effectively managing their experiences with the company has become the new differentiating factor in a highly competitive environment. Therefore, customer lifecycle management (CLM) plays a crucial role for online retailers. It enables them to actively manage profitability by closely monitoring customers’ individual steps. Analytics is a key component of high quality and transparent CLM, which enables businesses to draw the right conclusions to keep customers satisfied.
1 in 5 customers interested in personalized products are willing to pay a 20% premium.
Deloitte Consumer Review 2015
It empowers companies to react individually to requests or even to take proactive action. If embedded in the organization and its business processes, effective CLM can enable powerful customer interaction strategies along the various dimensions of the customer lifecycle.
Based on our past experiences, we have identified major pitfalls and milestones on the way towards an efficient, data-driven CLM. Typically, three building blocks exist when working with clients on optimizing their CLM:
- Creating the foundation – gathering appropriate data along the touchpoints and define business critical metrics.
- Customer insights – leveraging customer data, enriched by various other data sources to understand customer journeys analytically and derive valuable insights about customers’ behavior when searching or usage of products and services.
- Constructing engines to foster cycle repetition – analytics engines such as recommender systems or process support on next best offers refine the implementation of personalization strategies.
Key questions to consider the implementations of analytical customer lifecycle management:
- How well are purchases analyzed in an objective, data driven manner?
- Do you have consistent definition of metrics for effective customer analysis?
- Are retail processes supported by data analysis for offline/online approaches?
- Do you have a consistent view throughout the customer journeys?
- Is your product typical for regular usage to empower cycle repetition?