Esko Kilpi on Interactive Value Creation

The art of interaction, the design of digital work and the science of social complexity

Month: April, 2012

Network design

In a typical large organization everybody is a long way away from everybody else. As a result the individual perception of the world is narrow and confined to a small group of immediate acquaintances.

That did not matter in factory-type of settings because physical tasks could be broken up. Bigger tasks could be divided by assigning people to different, smaller, fairly independent parts of the whole. Hierarchies made sense as a way to modularize work. The worker did not need to communicate with many people. The downside was a lack of flexibility. Reconfiguring a hierarchy always created a mess for a long time. And if you had a lot of interaction going on in a hierarchical structure, with many steps going up and down, it was slow and prone to misunderstandings.

For intellectual tasks, it is much harder to find parts that make for an efficient division of labor. Intellectual tasks are by default linked and complex creating an increased need to interact. Knowledge workers are often put in a position where they have to negotiate some understanding of what they face. The same event means different things to different people. The cognitive opportunity lies in the fact that as we don’t all select the same things, we don’t all miss the same things. If we can pool our insights in a creative, enriching way we can thrive in the complex world we live in.

New technologies give an organization the ability to reconfigure its form any way it desires. We are not confined to any one structure any more. The smartphone revolution has changed the logic of the network. The Web is no longer about linked pages but about connected purposes. We want to do something – with the help of information and other people. Often this means wanting to learn and respond in a situation.

Most often we seek two things: information and interaction.

For information the best structure would be a random, contextual network. A random network has the shortest possible path lengths. An example of this is performing a search. The key measure here is path length. That indicates how far everybody is, on average. The path length measures how many steps a piece of information has to go through between people. To create short path lengths in a typical hierarchical or process based structure you would need to know almost everything and everybody included in the hierarchy/process chart.  You would need to have access to information that we typically don’t have. Hierarchies and process charts are thus not efficient ways to organize knowledge work. They are not transparent enough.

For interaction, the challenge is engagement. Widening the circle of involvement means expanding who gets to participate. It is about inviting and including relevant, new and different voices. The measure is built on the social graph: how many of your friends know each other?

The network design principles successful organizations follow are: ( 1 ) shortening the distance between two randomly picked files/nodes/people; ( 2 ) getting more people who you know personally, to know each other.


Complexity – Numbers that fool us

One of the basic ideas of modern science is that the laws of the material universe can only be meaningfully understood by expressing quantified measurements. Numerical terms are needed, not just words and stories. The belief was that instead of ordinary sentences we must use mathematical equations.

The values of the measurements at a given starting time are called the initial conditions for that system. The Newtonian, deterministic claim is that for any given system, the same initial conditions will always produce an identical outcome. Life is like a film that can be run forwards or backwards in time.

One thing we have learned is that no real measurement is infinitely precise. All measurements necessarily include a degree of uncertainty. The uncertainty that is always present arises from the fact that all measuring devices can record measurements only with finite precision. To be able to reach infinite precision, the instrument we use should be able to display outputs with an infinite number of digits.

By using very accurate devices, the level of uncertainty can often be made acceptable for a particular purpose, but it can never be eliminated completely. It is important to note that the uncertainty in the outcome does not arise from randomness in the equations, but from the lack of infinite accuracy in the initial conditions.

It used to be assumed that it was theoretically possible to obtain nearly perfect predictions by getting more precise information. Better instruments would shrink the uncertainty in the initial conditions, leading to shrinking imprecision in predictions. The lack of infinite precision was thought to be a minor problem. Well, our belief systems are still mostly based on the idea that very small uncertainties don’t matter.

Possibly the first clear explanation of a very different kind of understanding was given in the late nineteenth century by the French mathematician Henri Poincaré. He was the founder of the modern dynamical systems theory. His claim was that there were systems that followed different laws: the tiniest imprecision in the initial conditions could grow in time. Two nearly indistinguishable sets of different initial conditions for the same system would then result in two developments that differed massively from one another. This is the reason why seemingly random behavior can emerge from deterministic systems with no external source of randomness.

Poincaré was way ahead of his time. His early thoughts gained evidence in 1963, when Edward Lorenz found, by accident, that even computer models of the weather were subject to very sensitive dependence on initial conditions.

Numbers fool us and quantified measurements are very rarely the whole picture. Stories matter more than we think.

More on the topic: The End of Certainty: Time, Chaos, and the New Laws of Nature by Iliya Prigogine. 1998. Chaos: Making a New Science by James Gleick. 1987


Networks of learning and networks of products

Products that are manufactured in the same way and with the same product features are often used differently by different customers. Just because a product is a commodity doesn’t mean that customers can’t be diverse in their needs and the way they use the product.

Companies used to have no mechanisms for connecting with the end users in order to understand and influence this. Social media and mobile technologies are now changing the model.

The relationship between a customer and an enterprise can get smarter with every interaction. Consider a service as routine as grocery shopping. Suppose that you could turn to your mobile phone and come up with a graph of last month’s or last year’s grocery purchases. Every time a customer buys her groceries, she is not only showing herself and the firm the products she buys, but also teaching the firm the pattern with which she consumes/uses them and implicitly the complementary products she perhaps does not yet know of. The service is creating a history of this particular customer that is virtually impossible for a competing shopping service to replicate.

Interactive value creation is about two new capabilities: the firm needs to be capable of networking with individual customers, and behaving somewhat differently towards a particular customer on the basis of communication and learning.

If a firm wants to create learning relationships with its customers, it must first create links to end users. The starting point is not a company site any more. Linking needs to start from where the people already are and what they already do: the main starting/connecting points are social media platforms, stores and ads.  Also every product needs to be seen as a network node. By listening to customers individually, interacting with them, and then treating different customers differently, the modern retail firm can change the nature of competition and generate customer loyalty as well as higher unit margins.

What often happens is that enterprises view customers through the lens of a fairly uniform set of products leading to their seeing customers as having relatively uniform needs. But even commodity products are always a bundle of use contexts, buying patterns, complementary goods and delivery options.

A product or a service should be pictured as a node in a network with links to ancillary services and complementary features surrounding the product. The more relevant links are considered, the richer the product will become. The task is to visualize the product in the broadest sense possible.

As the customer’s need set is expanded beyond a single item, the definition of the product changes and becomes more complex. The more complex the product, the more possibilities there are for the company to remember something that will later make a difference. When a customer teaches a firm what she wants or how she wants it, the customer and the firm are cooperating on the sale of a product. It is about interactive value creation.

A learning relationship ensures that it is always in the customer’s self-interest to remain with the firm that developed the relationship to begin with. Loyalty then creates more value and is more convenient than non-loyalty.


More on the topic: “Smart disclosure“. Smartphones as health aids. Everyday health.