SDL Debuts Customer Commitment Framework



SDL has launched the first social data-enabled analytics framework that predicts customer behaviors including likelihood to buy a product, evangelize a brand or share content.

SDL Customer Commitment Framework (CCF) allows marketers, brand makers, sales and service professionals to make informed, data-driven and real-time decisions about where to invest resources to increase revenues, reduce costs and improve marketing effectiveness across all customer touch points. Insights gained using the CCF enable execution of effective Customer Experience Management (CXM) programs.

The SDL CCF captures social media conversations that are most important to understanding and enhancing the three fundamental customer journeys all businesses need for success – shopping, sharing, and advocacy – and applies patented algorithms to model the customer experience. It works in conjunction with SDL SM2 social media monitoring application, in addition to SDL’s other customer analytics applications. The CCF assembles the social datasets and structures them into a series of predictive measures that C-level executives can understand, relate to and trust as a decision making tool.

The CCF release also features the SDL Customer Commitment Dashboard (CCD), an intuitive online interface and executive dashboard that enables users to easily access diagnostic tools and view CCF scores. The CCD delivers a near real-time and predictive view into the experiences that make up the customer journeys, so organizations can course-correct and continually optimize programs, processes and outcomes.

“SDL developed the Customer Commitment Framework to provide business decision-makers with direct knowledge of their customers to enable awareness, acquisition, advocacy and retention,” said Mark Lancaster, SDL CEO, in a statement. “By truly leveraging social data, CCF provides insights into the offline and online customer experiences that previously could only be obtained through traditional measures such as market research that require lengthy timeframes and large-scale budgets.”