Most business leaders now recognize that organic growth is a direct result of their ability to deliver a differentiated, compelling, and increasingly personalized customer experience. Effectively delivering such an experience is dependent on the organization’s ability to understand what attracts customers’ attention and what drives customers’ behavior.
As you know, recent advances have lowered the investment threshold for consolidating and analyzing the massive amount of data that most organizations’ have about their customers. Predictive modeling can then be used to make increasingly effective and individualized decisions about the treatment of customers. For example, these approaches can be used to leverage customers’ past behavior to predict: the value of each customer, how likely that customer is to respond to specific offers, that customer’s price sensitivity, or how likely that customer is to attrite, as well as, what retention actions are likely to be effective. (See: Using Predictive Modeling to Optimize Customer Relationships)
Despite the enormous potential, purely quantitative approaches are insufficient. In particular, quantitative customer analysis has natural limitations, including:
- Trying to predict the future based on information about the past
- Data gathered at a limited number of customer touch-points rather than an end-to-end understanding of the customers’ experience, including the more important non-touch-points
- Surface level behaviors rather than a deeper perspective on customers’ motives, goals, plans, as well as, how they think and feel about their experiences
Trying to understand the customer based purely on quantitative analysis can feel a little like trying to determine how the furniture upstairs is arranged… by tapping on the ceiling! Obviously, you’d get a much clearer picture if you just went and took a look… rather than trying to infer what’s going on through indirect and limited data sources.
In addition, inferences drawn from purely quantitative approaches are prone to interpretation errors. Without an adequate qualitative context for understanding the data, we’ve seen too many organizations draw conclusions akin to “Our customers in South Florida are born Hispanic and die Jewish.“
The most powerful results come from the synergy between qualitative insight and quantitative analytics.
- Qualitative Insight: Leveraging knowledge from in-depth research, observation, elicitation, as well as, listening to the conversations that take place between customers in emerging social networks. This qualitative insight is used to frame and guide quantitative analysis.
- Quantitative Analytics: Leveraging patterns in demographic and transactional customer data in order to predict, classify, and optimize elements of the customer experience. This quantitative analysis is to validate, refine, and populate the context created via qualitative insight.
In practice, organizations and the functional departments within them tend to have a strong bias for one of these modes. More “left brained” organizations or functions emphasize the quantitative approach and feel uncomfortable with going out to actually observe what’s happening with customers. More “right brained” organizations or functions emphasize the qualitative approach, are out living with their customers, but also tend to make decisions that aren’t supported by sufficient analytical rigor. As a result, it’s difficult for organizations to put together the pieces in a way that generates a holistic perspective on the customer.
In our customer experience work with clients we are beginning to create Adaptive Customer Profiles that can be used to integrate quantitative and qualitative knowledge about the customer.
An Adaptive Customer Profile is…
… a formal knowledge representation structure used to capture the customer intelligence necessary to effectively customize communications, effectively assign service resources, optimize the presentation of high probability offers, and adapt pricing to customers’ price sensitivity.
Adaptive Customer Profiles for a given business situation generally include:
- Descriptive Information: Identifiers, demographic characteristics, etc…
- Potential and Current Value: The expected and current value of this customer.
- Customer Network Information: The customers’ role and placement in an influence network of customer relationships.
- Personae Classification: The degree to which the customer demonstrates an affinity for one or more personae classes that exist in the marketplace. These personae classes are an extension of psychographic segments that define the predominant “mental models” in the marketplace. These personae are characterized by shared customer goals and preferences, goal-directed behavioral patterns, cognitive schema, and temperamental characteristics. These temperamental characteristics include the customers’ orientation towards novelty seeking, harm avoidance, reward dependence, and persistence. (See Cognitive Ergonomics: Designing Experiences that Fit the Customers’ Mental Model)
- Relationship State: The level of attachment this customer feels towards our business as evidenced by their transactional and interactional behavior.
- Context Sensitive Behavioral History: key behavioral indicators derived from inquiry and order history, service records, etc…
Adaptive Customer Profiles are derived through an integrated set of qualitative and quantitative activities. Qualitative work includes customer observation and elicitation (See: Observation and Elicitation: We Like to Watch!) in order to uncover insight that is used to develop an effective personae classification scheme. Quantitative work involves predictive modeling focused on the leading indicators of customer behavior and measuring the affinity that customers demonstrate for one or more personae.
For example, we are working with a leading healthcare organization to design an integrated patient-physician experience that can adapt to the fact that different patients have fundamentally different mental models associated with their health and the consumption of health related services. Some customers will be high novelty seeking naturalists; some will be low persistence avoiders; some will be more high harm avoidant active consumers, etc… The experience design integrates an Adaptive Customer Profiling module that identifies the extent to which each customer fits one or more of the common personae that exist in the marketplace. Based on that Adaptive Customer Profile, we can then customize patient communications, instructions on courses of treatment, the presentation of wellness programs, etc…
We are also developing a similar personae classification scheme focused on Mass Affluent consumers of financial services. Almost every financial services company is currently targeting this valuable “segment.” The issue is that, by its’ very nature, the “Mass Affluent” segment is an exceptionally diverse group of individuals that only share the fact that they have assets and/or income above a certain level. Companies that attack this market with a mass market mentality will almost certainly lose. However, financial institutions that can target meaningful sub-segments of this market with a highly differentiated offer can create an experience that is attractive and differentiated with a substantial group of these customers. You might imagine a hip and differentiated “I Hate to Plan” themed experience for the sub-segment of Mass Affluent customers that are Avoiders… or a more conservative, goal-driven experience customized to the customers that are Achievers. A financial institution that embeds an Adaptive Customer Profiling process in their interactions with customers could more effectively customize the experience to the customers’ goals, behavior, mental model, and temperament.
Filed under: Cognitive Ergonomics, Customer Analytics, Customer Experience, Neuroeconomics | Tagged: adaptive customer profiling, Customer Analytics, Customer Experience, customer persona, customer personae, customer temperament, harm avoidant, novelty seeking, psychographic segmentation, qualitative and quantitative, reward dependent, schema | 2 Comments »