Outcomes-Based Experience Design


Chris O'Leary

Bridging the Gap Between Customer Experience and Business Outcomes

by Chris O’Leary, COO, Customer Innovations, Inc.

In the 25 years we’ve been helping companies design customer experiences, one of the consistent challenges has been to estimate the business impact of specific experiential improvements.  The fact is that many customer experience (CE) programs simply fail to make a compelling argument about the business value that will be generated by specific CE innovations. In the absence of a compelling business justification, executive support and sponsorship may be weak or even absent, orphaning the CE program and robbing it of the executive leadership it needs.

In their efforts to generate a business justification, Customer Experience (CE) managers frequently try two approaches.  Neither approach has been consistently effective in earning senior management support and sponsorship.

First, they may choose to rely on generally held beliefs about the value of customer satisfaction, engagement or Net Promoter Scores (NPS).  Often, this reliance highlights a correlation between these indices and some business outcome (e.g., revenue growth or market share), but treats it as though it was a causal relationship. (see: Keiningham et al., “A Longitudinal Examination of Net Promoter and Firm Revenue Growth,” J. Marketing, Vol. 71  July, 2007, pp. 39-51)

In addition to the confusion of correlation and causation, we’ve also seen many cases in which high satisfaction or NPS scores actually co-exist with declining revenues, market share, and profitability.  These measures reflect how customers feel about the company and not how the company may make customers feel about themselves.  As a result, they are poor predictors of how customers will actually behave.

The second approach, of course, focuses on generating cost savings and efficiencies, most often at the service touch points.  Unfortunately, service efficiency is almost always more important to the company than to the customer, and efforts to streamline or automate the touch points typically end up working against the quality of the overall customer experience.  (See:  The Customers’ Experience Does Not Happen at Your Touchpoints).

What is needed is a fundamentally new approach to focusing and justifying investments in customer experience innovation, one which directly addresses the core challenge of connecting specific experiential innovations with measurable business objectives.

For some time, we have been using a new approach to CE business justification called Outcomes-Based Experience Design, which represents a 180-degree change from common practices:

  • Rather than trying to justify potential CE innovations by predicting or projecting hoped-for business outcomes, this approach starts by clearly defining the desired measurable business outcomes and working backward to identify the innovations required to generate those outcomes.
  • Rather than relying on self-reported satisfaction, loyalty and NPS scores, this approach targets concrete business and customer behavior outcomes, both of which are measurable at the individual and the aggregate level.  Satisfaction, loyalty and NPS are interesting, but should NEVER be used to justify investment in experience innovation!

Rather than competing for attention, funding and time with other business initiatives, this approach anchors CE to the existing strategic priorities, which is where CE should have been all along.

Figure 1: Outcomes-Based Experience Design

As illustrated in Figure 1, the Outcomes-Based Experience Design approach introduces a new measurable outcome, Behavioral Outcomes that connects Experiential Outcomes and Business Outcomes.  Linking Experiential Outcomes and Business Outcomes in this manner enables CE program leaders to define and measure the specific business value that is being created, and this provide a rigorous business justification.

The model works in two directions.  The first direction, going right to left, illustrates the design relationship. When designing the experience innovation, one starts with the business outcome of interest, then determines the specific customer behavior that needs to be influenced, and then designs the specific experiential interventions that are required.

Second, the model illustrates the causal relationship going left to right.  The only way that CE innovation can create a business benefit is by influencing a specific change in customer behavior and choice-making.  The difficulty in business justification discussed earlier arises from the fact that it is so difficult to predict how customers in general will respond to different CE innovations, and even more so for specific groups of customers,

Outcomes-based Experience Design generates a host of critical benefits.  First and foremost, it positions CE innovation as a tool for achieving the priorities of executives and senior managers, NOT competing with those requirements.  Second, it provides metrics and measurability at each stage of the causal relationship.

Third, it allows companies to invest only in those innovations that will influence the target customer behavior, and stop investing in potentially expensive initiatives which may not matter to customers or for which they are not willing to pay.  Identifying (and terminating) uneconomic CE investments will often fund new investments that are far more impactful and that generate meaningful business benefits.

One final note:  This model is effective only if we understand how and why customers behave as they do.  Without the ability to link individual characteristics to the decisions and choices a customer makes, there is no way to design experiential interventions that will be effective in influencing the target behavior.  More important, there is no way to assure that  an experiential intervention targeting undesired customer behavior (e.g., attrition), will not adversely affect desirable customer behavior (e.g., retention, growth).

The necessary foundation of Outcomes-Based Innovation, therefore, is the ability to understand how and why customers make the choices that they do, and to use that information to influence those choices.  The scientific and methodological basis for this understanding has been previously discussed here (Getting Beneath the Voice of the Customer) and here (Customer Experience:  Beyond Better Sameness); practical challenges and applications will be discussed in the future.

Roadmap to the Customer Innovations Blog

I’ve received several requests to put together a “roadmap” to the Customer Innovations blog posts I’ve done.   Here is an organized path through the material I’ve posted so far.  I haven’t tried to be all inclusive but have just the most substantial posts.   Grab a venti dark roast and enjoy!

Customer Experience Strategy:

Evocative Experience Design:

Integrating Customer and Employee Experience:

Other:

Adaptive Customer Profiling: Integrating Quantitative and Qualitative Customer Analytics

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.

Using Predictive Modeling to Optimize Customer Relationships

Predictive modeling uses a variety of analytical techniques to make predictions about the future based on current and historical data.  These predictions are expressed as numbers that correspond to the likelihood a particular event, opportunity, or behavior will take place in the future. Predictive modeling can be used in making increasingly effective and individualized decisions about the treatment customers.  These models analyze the customers’ past performance in order to assess how likely a customer is to exhibit a specific behavior or respond to a specific offer.

There are several mature predictive modeling applications.  One of these is credit scoring.  Scoring models estimate the likelihood that that a customer will make future credit payments on time based on their credit history and application information.  Another relatively mature predictive modeling application is in targeted marketing which involves using consumers’ past purchasing history and response rates along with demographic, geographic and other relevant characteristics in order to estimate the likelihood that customers will respond to particular marketing offers.

These mature applications represent the tip of the iceberg with respect to the overall opportunity for using predictive modeling for the optimization of customer relationships.  While most of these mature approaches have been run off line, leading organizations are beginning to embed predictive models in customer-facing processes in ways that generate revenue opportunities and control risks in real time during live transactions and interactions with customers.  Competitive pressures are driving companies to personalize the way they manage customer relationships.  This is increasingly possible as:

  • Companies have invested heavily in the integration and quality of their customer data.
  • More powerful predictive modeling tools are available including advanced statistical regression and time series approaches, as well as, emerging machine learning techniques such as neural networks, radial basis functions, and support vector machines.  These machine learning techniques provide powerful tools for automated pattern recognition and prediction.

We see five major application categories of predictive modeling for optimizing customer relationships:

  • Valuation.  Leading companies are beginning to actively measure and manage the asset value of their customer relationships.  The first and most basic question is:  what’s the lifetime value of this customer?  Based on a customer’s unique characteristics and transaction pattern what types and magnitude of investment is justified?  When we make an investment in this customer, does it generate transaction patterns that reflect an increase in their value?
  • Customization.  Uniquely targeting consumers with the products, services, and experiences they value and types of offers they are likely to respond to can lead to significant revenue growth while reducing acquisition costs.  This goes well beyond traditional targeted marketing and cross-selling.  Examples:  Amazon and NetFlix recommendation engines.  Intelligent wardrobing recommendations made by call center agents at Victoria’s Secret Direct.  Leading financial services company that predicts a unique “next logical sale” to offer to each customer when they call for any service issue or inquiry.
  • Pricing. Many businesses have to account for unique customer risk and price based on the cost of covering that risk.  For example, auto insurance providers must accurately determine the amount of premium to charge to cover each automobile and driver.  More effective predictive modeling can streamline the process of customer acquisition, by predicting the risks of a particular customer and making more effective pricing decisions.
  • Retention.  Too many businesses try to retain customers only after the customer attempts to terminate their service.  At this stage, changing the customer’s mind can be expensive.  In addition, silent attrition, where customers slowly but steadily reduce usage, is a problem faced by a wide range of companies.  Leading organizations are adopting more proactive retention strategies by creating early warning systems that detect any significant change is customer behavior that may indicate either a service or retention issue. These companies then take preemptive measures to retain customers and address any latent service issues.  These attrition models examine each customer’s transaction history, service usage, and service performance in order to estimate the likelihood that customer will want to terminate service in the near future.  Example:  We helped JM Family Enterprise develop an Early Warning System that signals changes in customer behavior… regular call reports are generated for each account manager indicating what action they should take with these customers.
  • Fraud Detection.  Fraud includes inaccurate credit applications, fraudulent transactions, false insurance claims, and identity theft.  Fraud undermines the profitability of companies and drives up the costs of goods and services for customers.  Property and casualty insurance fraud is approximately $30 billion a year.  Health care fraud is approaching $100 billion.  Credit card fraud is estimated to cost $1-2 billion a year.  Tens of thousands of consumers are victims of identity theft.  Increasingly effective predictive models are being used to help quickly identify fraudulent activity without increasing the number of false positives that impact the customer experience.

(Note:  I’ve posted a follow on to the above post:  Adaptive Customer Profiling:  Integrating Qualitative and Quantitative Customer Analytics)