Customers are talking: The Eureka Button

I was talking to Cynthia Kurtz once and she mentioned, "If I were developing a piece of software I would always want to put a Eureka Button on every page."

A Eureka button is this: if while using the system a user just figured something out that others might benefit from, he/she would click the button and be presented with a page where she could enter:

What Happened?

Where does this apply?

When should people read this story?

This input and information about where they were in the system (page & data) would be uploaded to a database. The database can be searched for patterns or browsed periodically, looking for bugs or unexpected uses of the system.

It's easiest for me to think about the Eureka Button in the context of enterprise software. Having worked a lot with CRM systems for telephony, I know that these systems have hundreds of user pages, with a virtually infinite number of paths through the system.

In these environments, product managers may know in theory how people should use the system. But their knowledge is quickly overtaken by experienced users, who learn how to apply the system to their jobs, often finding tricks or shortcuts to make the system work better for them. ("Eureka! I just figured out that if I dummy out some data items, I can capture information & save information from a prospect before they decide to make a product purchase. If they call back, I can look them up by their phone # and I don't have to start all over again.")

In this situation, a Eureka Button has great value for the product manager and the users. Product managers can learn about difficulties users have and how they overcome them. The tricks can be incorporated into the product, or deficiencies addressed. Users can learn from each other--perhaps Eureka Button entries can be blogged automatically and read by other users, dispersing tips and tricks and encouraging others to share their stories.

I can't even begin to catalog how a Eureka Button could benefit consumer sites, where (especially recent) products follow an emergent, iterative development approach and patterns of usage can affect the entire purpose of the product (e.g., Twitter). There are people much better suited than I to discuss some of these implications. If you're one of them, please let us know in the comments how the Eureka button could be used with these products.

(In searching for prior references to a "Eureka Button," I discovered this NY Times article from 2004. The article mentions that "'It's amazing how many people there are who find pleasure in sharing the little discoveries they make.'" The article focuses on undocumented features in PC software and in consumer electronics. The article references a site that publishes user stories of hidden Windows features.)

Sometimes crowds aren't wise

I like Surowiecki's book, a lot, and I have experienced many instances where the collective judgment of a group was far better than even an informed individual. But the "wisdom of crowds" catchphrase is dangerous--oftentimes crowds are not wise at all.

We are experiencing right now an era in which crowds are really dumb. I'm referring to the financial markets and the related economic recession. The financial markets and news affecting the financial markets have merged into a massive echo chamber, wherein bad news begets pessimism which keeps prices down which begets another cycle of bad news.

We've seen this in reverse, of course. Do you remember 1998-1999, during which time everyone was watching CNBC or checking Yahoo Finance all day long, in real time assessing the value of their stock portfolios? Oversubscribed IPOs begat good news, which kept prices high, which begat more buying, etc., until it all came crashing down.

I thought it was clear to everyone that market groupthink, which afflicts us in good times and bad, obscured the true value of securities, and therefore paying close attention to news items in order to make sense of the markets and our economy was, at best, a waste of time.

But no. Felix Salmon, in his Portfolio Market Movers blog, points to a Financial Times article introducing us to a service from Reuters that collects news items and alerts traders when news trends indicate potential market movements.

In other words, lean into the echo chamber, and listen real hard for signals you can use to make decisions. Um, it's only January, but I will bet there's not a stupider product idea introduced for the rest of 2009.

Customers Are Talking: Reading Between The Lines

One of the important insights in looking for meaningful stories in customer interactions is the following: you can't read a story by looking at metrics. That is to say, how long someone talked, what time of day it occurred, etc., has no relationship to the content itself. In my work, I listen to lots and lots of customer stories, and I have experienced this very thing. If you want to understand the story, you have to read, or listen to, the whole thing.

It's unfortunate that this is so, because the quickest way to absorb information is to read it in summary. It's also the easiest way for computers to process information. Computers are excellent at counting, measuring, etc., but terrible at reading and interpreting.

I hear you already: what about semantic analysis? Good: doable by computers. Bad: doesn't provide much insight. Here's an example: evaluate all customer service calls longer than 8 minutes and containing the word "unhappy." Let the computer pull out two sentences before and after that word. Won't that sort out all the unhappy customer calls and allow us to analyze a manageable data set? [If you think this is difficult to do, I can point you to a slew of vendors who are dying to talk to you about their products.]

The problem is, "unhappy" is context-dependent. The caller may be unhappy with the quality of her service. She may also be unhappy she forgot to pack her son's lunch that morning, Someone else may be unhappy for a completely unrelated event.

[As an experiment, I've been monitoring Tweets referring to the Blackberry Storm using the happy :) or unhappy :( emoticons--easy to do with Twitter Search. With more than 100 tweets examined, very few of the emoticons represented satisfaction or dissatisfaction with the device itself--they were related to wanting the device and not getting it, or hoping to get it, for example.]

In a recent discussion, a friend talked about word clouds as very useful summaries of social media data. I pointed out to him that the appearance of a word in a story doesn't create significance. Similarly, the absence of a word doesn't mean that word is insignificant. (What's unsaid may, in fact, be the most important words in the entire dialogue. Harold Pinter won a Nobel Prize for his mastery of this truism.)

In sum, at present, the intervention of a person close to the customer interaction at the time it occurs is the best way to determine if a communication is significant or not. If it's someone looking at it after the fact, that person will have to read the entire story, not a summary. I wish there were a shortcut, but there's not.

Are keyword searches or word clouds useless? No. If you are a cable company, searching for specific, unambiguous words like "DVR" in your customer communication is likely to be useful. Searching for context-dependent items like "unhappy" or "delighted" is not.

DARPA seeks algorithms to create stories from info fragments

One of the nicest aspects of blogging is when a reader points you to an interesting article you hadn't seen. I'd like to thank reader S.E. August for this reference.

Wired's Danger Zone blog reported last week that DARPA is looking to sensemake various forms of data by combining them into stories.

The Cognitive Edge training I took last week discussed (among many other topics) gathering narrative fragments into composite stories as a way to make sense of a situation and convey that information to others. Similar thus far. A possibly reality-defying assumption follows, though. According the Danger Zone post:

The author of this tale, however, would be a series of intelligent algorithms that can pull all of this information together, tease out its underlying meanings, and put it in a narrative that's easy to follow.

In the Cog Edge method the sensemaking is done by a group of humans, not a computer. The assumption is that distributed cognition of a group of people can elicit meaning where a single person, or a computer, cannot. I'm not up on the latest in artificial intelligence, but I'm doubtful that an entirely computerized approach can yield anything of use.

Perhaps a partially-computerized method, where fragments were gathered (sampled?) by machine and sensemade by humans, would work better. Or if the fragments could be human-coded as they were captured the significant or related ones might be easier to isolate. I don't know. Can any readers weigh in who are more optimistic that a totally-computerized approach might work?

A link to the DARPA RFI is here.

(Photo by cote via Flickr creative commons)

sensemaking, narrative, stories, DARPA, communication