Attitudinal Data: CRM's Crystal Ball
The clothier seems to operate on the cutting edge of CRM, but crucial information is missing, and key questions remained unanswered: Why do customers react as they do? What do they like or dislike about their interactions with this retailer?
The answers lie in something called attitudinal data. Incorporating this data into the analysis of traditional CRM data can make a relatively successful CRM initiative outstanding. Including attitudinal data and understanding the correlation between it and behavioral or transactional data is critical for obtaining a comprehensive understanding of the customer. Think of attitudinal data as the fabled crystal ball. It might not precisely predict the future, but it comes close.
To make attitudinal data more valuable, it should be paired with data from traditional CRM applications. This transactional information imparts important knowledge about customer behavior, and attitudinal data becomes much more reliable when supported and correlated with intelligence derived from the behavioral assessment of customers. A more competitive advantage can be obtained through CRM technology when using real-time attitudinal data that explains consumer action -- an explanation tied directly to how customers feel.
With the clothing retailer example, traditional data analysis showed that a certain demographic segment of historically profitable customers had stopped buying products. The retailer developed a costly campaign to combat this attrition, but it fell short of strategic goals. The campaign failed to address why the customers left, and instead focused on an unrelated issue. Money and resources were expended with no effect on the attrition rate and no benefit to the bottom line.
Had attitudinal data about the customer segment been a component of the clothing retailer's CRM analysis, it could have cut costs by identifying and addressing the probable attrition before it occurred. Or a better-targeted campaign could have been created.
To sustain the competitive advantage created by using CRM technology, the incorporation of online and offline attitudinal data becomes pivotal. Predictive analysis within analytical CRM strategies using traditional data can help companies identify what is the next most likely product a customer will buy. Survey research -- the backbone of attitudinal data -- coupled with traditional CRM tools collecting behavioral intelligence from other customer interactions can take it further and provide insight into customers' actual receptiveness to this cross-selling. It also helps companies better understand customers' likely lifetime value and the reasons behind customer attrition.
Once in place, attitudinal data translates customer feeling into likely customer action, giving an organization a more focused approach to marketing strategy and product development. For example, if a large financial institution wants to create tailored services for college students, customer attitudes add important strategic information to the project.
Using the segmentation of college customers as a starting point, the bank contacts each new customer, directing them to an online survey that documents information such as demographics, banking preferences and other portrait-painting characteristics. This attitudinal intelligence is analyzed along with transactional data and used to create more focused marketing and sales initiatives.
As marketing and sales initiatives are implemented, the attitudinal data collected and interpreted is quickly made available to strategists, providing them the knowledge to rapidly adjust their services and marketing programs and bolster recruitment of new customers or retention of existing ones.
Without insight into the feelings of the students, the bank could have lost money on an ineffective project. By including technology designed to improve intelligence based on customers' attitudes, the bank gained a well-rounded view of each customer.
To fully benefit from an analytical CRM application, a company needs a 360-degree view of customers. This view can be enhanced by the partnership between transactional data -- the who, what, when and where of customer relationships -- and attitudinal data -- the why and what is next.