So, back in September I wrote about one basic bias: context-dependent preferences. At its core, context-dependent preferences mean that consumer choices are based on relative value, instead of absolute value. So, instead of assessing which option is best on its own, we tend to look at which option is the best, relative to others in the choice set. With strategic selection of the items, a marketer can thus heavily drive us to choose a middle option that sits between a cheap and an expensive one. This is known as the compromise effect.

Now, Simonson is saying that the compromise effect isn’t real anymore! I picked up his claim from his talk at TEDxBayArea. Here’s the video:

So, Simonson has replicated with Taly Reich his previous experiment that showed the existence of the compromise effect. He shows that the new study was pretty similar to the old one, and that the compromise effect just wasn’t there. Unfortunately, since the study hasn’t been published yet, we just have to take Simonson’s word of this. But let’s do that, and see what the implications are.

His main point is that the compromise effect doesn’t surface because modern technology helps us estimate absolute value instead of relative value. Twenty years ago, we would proxy quality with attributes such as brand, country of production and so on. These days, we have access to a host of online services that look at the product quality. Some of them directly assess products, while others provide just customer reviews. Irrespective of the way of assessment, both are pretty decent ways of assessing quality.

So what does it mean? Well, Simonson comes up with a list of commonly believed statements about marketing that he proceeds to debunk:

1.       A company’s brand is more important today than it has ever been

Quite a few people say that precisely because there’s so much information out there, branding is even more important today. Because of information overload, consumers can either process all the information, or ignore the information and rely on brands. However, Simonson argues that we can combine the approaches: quickly glance at reviews to assess quality. Brands will still exist – for example as status markers – but they will not be effective proxies for quality.

2.       Nurturing customer loyalty is the best investment

Customer loyalty is usually thought to be a sound investment. Satisfied customers will buy your products again, and thus loyalty will act as a barrier against competitor. Simonson thinks this is less likely to be true now. He claims that each product is evaluated on its own: he’s not going to buy a Toyota just because he liked the previous car. I think the example is correct, but the lesson’s not. Sure, when you buy a big pure product, you’ll evaluate the attributes with care. But with smaller purchases, brand surely matter. I seem unable to find the paper, but I remember reading that when buying groceries, customers tend to go for the products they usually buy (classic System 1 way of action – status quo).

3.       Market research can predict what consumers will want

Simonson says that since our choices are these days driven much by social media and we rely on opinions of others, then market research should focus on that part, instead of looking at wants and preferences today to predict the future. Simonson argues that the prior wants are really bad predictors – what matters are the sentiments in the social environment.

All in all, I think Simonsons ideas are pretty thought-provoking. There’s a lot riding on the replication failure of just one experiment, which seems a pretty shaky justification for a new theory. However, it is just one TED Talk, so I hope there’s more to it. Apparently, Simonson has also written a book about this. Well, that’s one more book for my pile of books to read...

The old saying everybody has heard says “don’t believe everything you read”. Current research shows while this is true, we also need a saying instructing us “don’t believe everything you see – especially on a chart”.

In their recent article Blinded with science: Trivial graphs and formulas increase ad persuasiveness and belief in product efficacy Tal and Wansink experiment with different ways of providing information about medical drugs to consumers. They have two groups of people: the ones seeing just text, and the ones seeing also a chart. The graph was trivial: it added no new information at all. The control group saw a text saying that

The result? Participants who saw the graph believed the drug to be more effective than the control group.

As it is, this result could be just due to increased information retention: graphs make it easier to remember things. Well, the authors thought the same, and replicated the study by checking for retention 30 minutes after the study. It turned out there was no difference in memory, but the same effect persisted. What could be the cause? Looking at the data, the authors found out there’s a significant interaction of belief in science and the chart effect – people with more belief in science ended up being more convinced because of the graph! Uh oh, this is worrying for us science buffs.

In their third experiment, they changed the setting by replacing the bar chart with a chemical formula. What happened was that once again, a scientifically-looking item increased the effectiveness rating. Unfortunately, the authors neglect to mention whether the interaction with belief in science was significant here 8probably not, since it wasn’t reported).

It’s understandable that a chart would make a claim seem more scientific. After all, one of the hallmarks of pseudoscience is hiding all the results behind vague words – a chart is at least clear. Unfortunately, by choosing the right measures and axes, one can design compelling yet false claims with graphs quite easily. A chart does not ensure sound science, or sound data. I guess we’ll just have to stick to trying to evaluate the actual claims carefully. Don’t believe me? Well, here’s a chart to convince you:

The past few days I’ve been spending at INFORMS Annual Meeting at the beautiful San Francisco. Due to the planning fallacy, I never had the time to write something new and proper for this week - so you if you’ll excuse me, I’ll just use this week’s post to share a few interesting insights from the talks I’ve seen here during the past few days.

Statistics.com

I’ve never heard of them before, but apparently these guys offer stats courses on crisply defined domains. Each course costs a small amount of money, and lasts for four weeks. So completing one of them may be a good idea if you want to know about one specific area, rendering full-semester MOOCs somewhat useless.

Cost Transparency Increases Purchase Intention

Bhavya Mohan, from Harvard Business School, showed in their group’s experiments that – at least currently – a company can increase customers’ willingness to buy in an online store by providing information on the product’s costs.

Emotional-Motivational Responses Predicting Choices

Outi Somervuori, a colleague of mine from Aalto University, showed that negative emotions and frontal lobe asymmetry predict the endowment effect.

My Own Talk

My own presentation (available here) was about using a linear value function model to predict choices when choosing student apartments. As it turns out, whether a subject is consistent with a linear value function had very limited impact on how well the model can predict choices – meaning that assuming a linear value function is an ok starting point for a model.

ProbabilityManagement.org

These guys have a fantastic, completely free SIPmath standard for communicating uncertainty between Excel and different pieces of software. It for example enables easy calculations with distributions in native Excel, something that’s notoriously difficult. For example, in the screenshot I’ve just calculated U+U, U*U, and U^cos(U) with  a sample of 10 000 for the uniform distribution. I think this system is nifty enough to maybe warrant a full post in the future.

Decision Analysis in Life and Business

Larry Neal told about his personal experience on how business and life decisions differ. A decision analyst at Chevron (one of the most decision analysis –friendly companies out there), he faced cancer a few years ago, and ended up helping several other patients to analyze and decide on their treatment. He concluded that DA in personal decisions is much harder, because the goals are more ambiguous and the situation much more complex. Moreover, in his view our behavior is heavily influenced by cultural assumptions, and it is often impossible to recognize them yourself – but that’s exactly something as an analyst he was able to help with.

MOOCs

A panel by four professors from Stanford, MIT and Columbia was talking about MOOCs. Their response was very favorable. They all agreed that the traditional way of teaching with a professor speaking and the students listening is not adequate. MOOCs can be used to deliver value to a large audience, and even though the completion rate is low (less than 10 %), they still reach thousands of students per course.

Teaching Decision Analysis

There has been several amazing sessions about Decision Analysis. I just came back from a session about teaching and the legacy of Ron Howard, one of the giants of DA. It was certainly very inspiring and there were some good points about how DA teaching need to stay connected to the actual practice of decision making. After all, we don’t want to produce just new researchers who produce papers – the point of research is to impact the world in a larger way.

Well, that's a summary of the past couple of very exciting days! I'll be back on track with more decision making thoughts next week.

You wander around at the store and see a nice looking pair of speakers. You plug in you iPhone and test them out, rocking it out to your favorite tunes. The speakers are very enticing already, but you decide to test out the next, slightly more expensive set just to be sure. Comparing the sounds, the more expensive set sounds just so much clearer, with better bass punch, too… Oh, it’s just so much better! Walking out from the store with a pair of speakers that are way better than your needs, you’ve just exhibited a prime example of context-dependent preferences.

In its simplicity, this bias may sound like an old truth. Sure, our preferences are changed by the context, so what? Unfortunately, in its simplicity lies the problem: this bias has the potential to affect us in almost any situation involving comparisons. And in the modern information era - with comparisons just a few taps away – well, that’s just about any situation. So what’s the bias? To be concise, the point is that choices are affected by changing the choice set, for example by adding new irrelevant alternatives. In effect, this can mean than whereas you this time preferred the high-grade speakers, adding a third middle option might have pushed you to choose the most cheapest, lowest quality ones instead. I’ll explain the theory with the help of a few images, borrowed from Tversky’s and Simonson’s paper.

If you look at the figure above, it shows three products that are quite different. Product Z is high in quality (attribute 2) but unattractive due to high price (ie. low on “affordability”, attribute 1). Product X, on the other hand, is very cheap but low quality. Product Y is somewhere in between.

The worrying part in the context-dependency is that our choices between options can be largely influenced by adding or removing options. For example, if we start with products X and Z and then add Y, by strategically placing it our choices can be heavily influenced. If Y is placed like in the next figure, a large proportion of decision makers will tend to switch to preferring Z, despite preferring X when they just had a choice between X and Z. Let’s look at a figure that shows this better.

The reason for the bias is that quality (attribute 2) now seems much more important after seeing that Y has a lot of that, too. X, on the other hand, is still cheap but looks much worse in terms of quality. After all, you don't want to get the lowest quality option. Depending on the setup, this will either lead to picking Y (which is not a bias, since you couldn't choose that initially) or picking X (which is, if you preferred Z initially). If you want to see the equations that clarify how the placement logic works, they are in the Tversky and Simonson’s paper. In addition, the same effect with a different example is very nicely explained here in Dan Ariely's lecture.

The gist of the issue is this: context-dependency means that with some set of options, we would choose X over Z, whereas a change in the option set – for example adding Y – may nudge us to choose Z instead. What you see influences you heavily.

So what’s the problem in preferring X in some situation and Z in another? Well, the problem is twofold. First of all, if our choices are affected by options we don’t even pick in the end – so they should be irrelevant – it can be argued that our sense of what we actually want is problematically limited. Admittedly, this is a big concern in its own right. A bigger issue is the fact that often we don’t get to pick the options we see. What this means is that our choices can be influenced by marketers and other people who have the power to set up the choice situation.

Thankfully, I think there’s a remedy. Contextual choice works in both ways, so you can use it to your advantage, too. When considering what you’d prefer, you can play out the situation by creating different alternatives – even irrelevant ones –and reconsider your choice. This kind of thinking will not only make you less susceptible to choosing on a whim as you consider things more carefully, thinking about other alternatives may give ideas on what’s actually possible in the situation and what you actually value.