Digital Experience design: risk, satisficing & opportunity

by Foolproof

I regularly have the opportunity to observe user research across web, mobile, enterprise and embedded applications. I have observed two trends that have practical implications for experience design and digital product development.

  1. Expectations of digital experiences are increasingly shared by people across social and age demographics, as well as people at varying stages along the technology adoption curve.
  2. The digital landscape has matured to the point where we can identify a broad set of product attributes that are generally perceived as hygiene factors.

So what?

These two trends are linked, and together have practical implications for the optimisation of digital experiences and new digital product development.

Design decisions can now be guided by an essentially known set of parameters when it comes to the quality of features, functionality and overall user experience, and decisions with regard to trade-offs can be informed by an understanding of an average user’s expectation.

With this in mind, I propose a simple framework for managing the development and optimisation of digital experiences – the Average Market Expectation (AME) model.

AME model for a fictional digital product:

Digital Experience Design lge

…so let’s take a look in more detail:

Quality of experience

Although experiences are subjective, we often share an expectation around the quality of an experience. For example we tend to share an expectation that a more expensive restaurant will give us a better experience. As the market matures, we have increasingly shared expectations around the quality of digital experiences.

Priority vectors

Priority vectors in the AME model build off the strategic management concept of vectors of differentiation:

“A Vector of Differentiation enables sustained competitive product [or service] differentiation by continuous improvement along a specific path with a distinct benefit or value proposition.” – Mike McGrath

Knowing the broad set attributes that determine the relative success of digital products and services means we can identify and prioritise product attribute, enabling the effective allocation of resources.

I propose we can now map priority vectors against an Average Market Expectation for the quality of a digital experience. This will allow us to make informed judgements about how to develop digital products, and more specifically, where to make trade-offs in the face of inevitably limited resources.

To implement this approach a product development team would need to conduct research and analysis to ascertain both the priority vectors for the product in question, and the average market expectation for the relevant product attributes.

Broadly speaking however, we can identify product attributes within product domains that are valued by users.

Example attributes:
eCommerce focused: frictionless path to purchase
Entertainment focused: effective content discovery mechanisms

The Risk Zone

The Risk Zone denotes the delivery of a product attribute below the average user’s expectation (e.g. area S1 in the AME model shown above). Reduced customer loyalty and reduced advocacy are inevitable outcomes, and if the vector represents a key priority attribute for users, then the complete loss of a customer is possible.

At this point it is worth noting that our shared experiences of many features and forms of functionality have been defined by specialist companies with vast resources (e.g. Google for search, Amazon for personalisation). Creating interactions that at least resemble those we carry out day to day as we use mass services is however becoming increasingly important.

Although attempting to recreate the quality of experience of, say Google search, is clearly an unreasonable objective, the AME model does assume that you will at least strive to achieve the Average Market Expectation quality of experience within your product domain.

Being seen to be ignoring the quality of experience of fundamental product attributes will be increasingly punished by the market – where these attributes are priority vectors, this could be fatal.

The Experience Satisficing Zone

The experience satisficing zone denotes the region where average expectations are met, relative to an axis of low to high. As such, experience satisficing can be seen as achieving an average measure of the quality of experience, or in other words, satisfying an average expectation of hygiene factors.

The model implies that even if achieving a level of quality within the satisficing zone is deemed difficult, this task is only neglected as a result of a conscious decision to focus resources on other areas.

In finding the average perceived expectation of quality, we create a research-based reference point within a continuum of poor to excellent user experience.

The degree to which the average expectation can be specified will depend on the gap between the resources required and the resources available for research.

The Opportunity Zone

Here is where we can begin to differentiate our product or service, finding opportunities to wow users by delivering an experience above expectations for the key features and functionality that are important to them. To achieve this for all product attributes is unlikely to be feasible in the medium to long term and to attempt to achieve this is not the strategy that is implied by the AME model.

The intended strategy to be applied through the use of the AME model is to achieve a superior experience across key priority vectors that combine to form a distinct and value-adding proposition, while ensuring that perceived hygiene factors are not neglected when allocating resources and planning future development.

Final thoughts – the AME model in practice

The AME model is a simple framework that can be seen as a practical response to the trends noted above, here’s a reminder.

  1. Expectations of digital experiences are increasingly shared by people across social and age demographics, as well as people at varying stages along the technology adoption curve.
  2. The digital landscape has matured to the point where we can identify a broad set of product attributes that are generally perceived as hygiene factors.

In terms of applying the AME model, I suggest there are two extremes. One is to apply the framework through a robust programme of research and ongoing measurement, rigorously testing user expectations and perceptions of quality and refining priority vectors as technologies evolve. A cheaper, more immediate approach would be to get together in your team, brainstorm what you feel make up the priority vectors – either for a new or existing product (service) – print off a copy of the AME model you have created, and have a collaborative discussion around where you think you are, and where to focus moving forward. Once you start to use the model for investment decisions the rationale for validating your model with user research would become strong.

What do you think?