Attention is increasing in relative scarcity (as explained in details in a previous post), and what is scarce is valuable. While any product, service or other activity costs attention to both the consumer and producer, attention is becoming increasingly valuable. It is hence essential to ensure that what you produce or consume provides your clients or your organizations with a high enough Return on Attention. This metric should be tracked by all businesses and its use adapted to each specific activity.

Measure and act on ROA for each role in the value chain

ROA should be tracked from the point of view of every participants in a specific value chain, and analyzed based on each roles and activities they undertake. The question each actor is asking is “Of the total attention I allocate to a particular source, what is the productivity of that attention in terms of value received for effort and time invested?”¹.

For instance, an organization is both a supplier and a consumer: the ROA should be measured for both roles, even if in a small organization the same person is doing both. ROA is a versatile tool that can be used in customer/producer relationships, from an organization or individual perspective. A couple of examples:

• Consumer: What does it cost me in Attention to consume this product or service?
• Producer: What is my ROA for each client? Which one is the highest? Which may be negative?
• Marketer: How to ensure we provide the highest ROA for each client? How to provide a good ROA through our communication, thus ensuring the messages keep reaching their targets?
• Organizations: How to maximize ROA for each employees? What can we modify to improve it or handle negative ROA situations? Are my communications and internal initiatives of a high enough ROA so that my employees will engage meaningfully with them?
• Individual: What are the activities that reward me the most for the time and effort spent? Are they aligned with the priorities of my organization?

  IT tools are key to improve ROA

Handling information and consuming mainly attention, IT tools are also a powerful means to improve ROA. Indeed, as with any ratio, there are two possibilities for improvement: increasing the returns or decreasing the amount of attention:

• Decreasing the amount of attention consumed: you can use workflow/process redesigns, along with automation to decrease the cost in terms of attention of a specific activity. For example, going from an approval process with 15 clicks on 3 different tools and manual entry to 3 clicks in one tool with automated data entry would improve the ROA.
• Improving the returns on the same amount of attention spent: this is the single largest opportunity. Take advantage of innovative IT tools that maximize the returns you get from the time you use them. Going from emails to information flows customized and prioritized based on your social network and relevant objectives will raise dramatically the returns an employee gets from spending 15 mins trying to get up to speed with a new project or client. Along this line, it is also possible, again by using the right technology, to increase altogether the total amount of attention an individual has to spend. “Flow applications” for example have the potential to allow an employee to dedicate not just 0 or 100% of her total attention to the activity at hand, but to start splitting it across several activities.

Where to start?

ROA is not easy to put in place and use effectively. Start by identifying the critical areas of:

• your business model and processes
• your clients portfolio
• your talent pool

Then, separate the different activities of each cycle and for each actor, analyze the ROA from their perspective (including yours as an organization). You will quickly zero in on the areas at risk due to poor ROA, and the diagnostic should provide the necessary indications for improving the situation.

Photo credit: vishy-washy

References

1. 1 John Hagel, Mastering New Marketing Practices

The Gist

Gist

Neuroeconomics, neurofinance, behavioral economics and so on… All the fields at the crossroad of cognitive or neuro– sciences and other scientific disciplines are increasingly productive. The academics in these fields seek to understand how the brain is processing the information needed to make a decision. As it turns out, we must do with scores of “Cognitive Biases” that stem from our brain design and shape all of our perceptions and choices.

Impossible to eliminate, hard to even consciously correct, you can no longer ignore them: an increasing proportion of businesses have incorporated them into their decision making processes (ie these processes have been designed to correct any bias). If you don’t do it as well, you will soon lag your competitors.

Origins

Our brain perceives the world around us through heuristics, which are generally helpful but often lead us astray. Think of a heuristic as a shortcut your brain is taking to reach a decision. For example, your eyes send 2 distinct images to your brain, but what you perceive is a single one. It does the same with choices, though, and can mislead us.

At Stake

If you are involved in decisions or choices, you are influenced, often in the wrong way, by these biases. Their knowledge should be required for anyone involved in strategy (at the firm, business unit or small business level). Professionals in finance and asset management should also master this field. In both cases, this is increasingly happening and is now a required element to achieve a leadership position.

Actionable ?

Learn, this is the easy part. Than modify your decision-making processes to account for the known biases and heuristics. Any deep strategic choice or investment decision should be insulated, as much as possible (won’t be 100%), from these.

More Details

Origins

Let’s use McKinsey’s Charles Roxburgh introduction to Cognitive Biases:

The brain is a wondrous organ. As scientists uncover more of its inner workings through brain-mapping techniques, our understanding of its astonishing abilities increases. But the brain isn’t the rational calculating machine we sometimes imagine. Over the millennia of its evolution, it has developed shortcuts, simplifications, biases, and basic bad habits. Some of them may have helped early humans survive on the savannas of Africa (if it looks like a wildebeest and everyone else is chasing it, it must be lunch), but they create problems for us today. Equally, some of the brain flaws may result from education and socialization rather than nature. But whatever the root cause, the brain can be a deceptive guide for rational decision making.

These implications of the brain’s inadequacies have been rigorously studied by social scientists and particularly by behavioral economists, who have found that the underlying assumption behind modern economics “human beings as purely rational economic decision makers” doesn’t stack up against the evidence. As most of the theory underpinning business strategy is derived from the rational world of microeconomics, all strategists should be interested in behavioral economics.

At Stake

We’ll use one example to convince you.

Consider this little experiment, imagining your the President of the US and have to make a choice:

Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequences of the programs are as follows:

If Program A is adopted, 200 people will be saved.

If Program B is adopted, there is a 1/3 probability that 600 people will be saved and 2/3 probability that no people will be saved.

Which of the two programs would you favor ?

Now imagine, one year later, you’re faced with another tough choice:

Imagine that the U.S. is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimate of the consequences of the programs are as follows:

If Program C is adopted 400 people will die.

If Program D is adopted, there is 1/3 probability that nobody will die, and 2/3 probability that 600 people will die.

Which of the two programs would you favor ?

This is one of the experiments you’ll find in the seminal article in Science by Amos Tversky and Daniel Kahneman (full details in the references) on framing effects. Now, in reality, this experiment was done on different peoples to avoid them seeing that the probability structures are similar. The only difference is in the framing of the choice: problem 1 is framed in terms of gains (lives saved), and problem 2 is framed in terms of losses (deaths). Let’s consider the answers in the actual experiment:

Problem 1: A = 72% ; B = 28% / Problem 2: C = 22% ; D = 78% .

What does this means ? That when we are presented with a choice framed in terms of gains, we tend to be risk-adverse, and when presented with a choice framed in terms of losses, we tend to be risk-seeking. This cognitive bias is actually so powerful that it even makes surgeons choose a different type of operations based on how the probabilities of success are framed when presented to them. You read that well: surgeons will choose to operate you or not, based not primarily on the life and death probabilities but on how they’re framed. If for life and death matters, by highly trained physicians, the framing of the choices make them choose different solutions, ask yourselves how powerful it may be when considering your next business strategy choice (or how this person on your team is presenting the different options available…).

Actionable ?

Simply knowing the common biases is a start but obviously not enough. You have to incorporate them in your business processes. This is what Bridgepoint, a major private equity manager, did for their most important decisions: to invest or not in a deal.

When considering a major investment decisions, managers tend to discuss among them and to reach a consensus on what’s the best choice. This leaves them vulnerable to a number of cognitive biases such as groupthink, the confirmation bias, overconfidence, pseudocertainty effect, etc.

To counteract this, Bridgepoint’s investment process has been designed to minimize their effect. For each deal, they hold their meeting in “court room” mode, with two sides: the proponent of the deal and the devil’s advocate. The proponent is a partner supporting the investment. The devil’s advocate is another partner randomly chosen and tasked to convince the others NOT to invest in the proposed deal. The two sides have equal air time and the remaining partners may question them before voting. Of course, the devil’s advocate is actually judged and has objectives on how well he does a job essentially destroying the investment thesis.

This results in a brilliant way of taking decisions over major investments. One that should be followed even within large corporations on material investment decisions.

Example(s)/Exhibit(s)

I won’t go in details here. Instead, here are some resources where you’ll find scores of interesting and entertaining examples:

• Roxburgh. Hidden flaws in strategy. The McKinsey Quarterly (2003) (2): provides a nice business intro
• Lovallo et al. Distortions and deceptions in strategic decisions. McKinsey Quarterly (2006) (1): good follow-up with Dan Lovallo, an academic on the subject
• List of Cognitive Biases: wikipedia page listing the different cognitive biases, good reference list but each page is a bit light
• Kahneman et al. Delusions of success: How optimism undermines executives’ decisions. Harvard business review (2003) vol. July: very good as well
• Horn et al. Learning to let go: making better exit decisions. McKinsey Quarterly (2006) (2): good use case for a specific situation
• Horn et al. Beating the odds in market entry. McKinsey Quarterly (2005) (4): same for another case
• Predictably Irrational, Dan Ariely, 2008: great “entertaining” book by a top scholar. Start with this one if you like behavioral economics.

References

More on the academic side:

• Kahneman et al. Prospect theory: an analysis of decision under risk. Econometrica (1979) vol. 47 (2): seminal but not so easy
• Tversky et al. The Framing of Decisions and the Psychology of Choice. Science (1981) vol. 211 (4481) pp. 453–458: seminal and easy to read
• Maps of bounded rationality, Daniel Kahneman’s Nobel Prize lecture: very good and easy
• Judgment under Uncertainty: Heuristics and Biases, Daniel Kahneman (editor)
• Choices, Values, and Frames, Daniel Kahneman and Amos Tversky (editors)
• All the works of Daniel Kahneman, Amos Tversky, Colin Camerer, Dan Ariely, Georges Lowenstein, Drazen Prelec, etc. (google them!)