A few months ago, I gathered tweets and evaluated them to see what people thought of the Storm, the iPhone imitation from Blackberry. David Pogue of the New York Times had panned the device in his review, yet I found that the stories told on Twitter weren’t so bad.

Then today I found this entry on the Crackberry blog (via FierceWireless), a Blackberry users’ site unaffiliated with the company. The entry posted (then took down) preview pictures of the planned Storm II.

What’s left, even though the pictures are gone, are the comments. And in this set of comments, you’ll get a very interesting, 360 degree picture of what Storm users (and Blackberry fans) think of the device.

Some examples:

looks like the same crappy device that the first storm was in my opinion.. i am neither impressed

What?!? When I’m on break at work, I actually use my friend’s iPhone because it’s 10 times faster than my Storm.

(You can even treat yourself, partway down in the comments, to an impromptu discussion of the latest episode of “Lost”–part of the beauty of the open forum!)

Related posts:
The Blackberry Storm/Twitter project
“Lost” as a metaphor for the dysfunctional company

Earlier this week, I posted on ways businesses can monitor what’s being said about them in various social media outlets. Perhaps in a challenge to myself, I promised a follow-up dealing with what businesses can do with this information. Finally, egged on by Amber and David from Radian6, here it is.

Companies are getting good at quickly responding to, and engaging in, conversations that others start about their products. For example, Amber and David quickly submitted thoughtful, interesting comments to my post. Dell is also very responsive, and so is Comcast. Your company should participate in these conversations as these companies do. You should be authentic and respectful and all that. Many social media consultants can help you do this. I am after something else.

Specifically this: it’s time to find useful, actionable patterns out of those gigabytes of chatter–Tweets, blog posts, comments–you’ve collected about your products, company, customer service from all these sources. And while these snippets may not follow a complete story format (i.e., “this happened, then this, then this”) per se, I treat them as stories and recommend using narrative sensemaking approaches to find the patterns.

For customer narratives, companies I’ve worked with have had success in finding deep customer values within these stories, using an exercise called emergent constructs. [Cynthia Kurtz's free e-book Working With Stories has been a critical resource to me.] By values I mean things customers find value in, or don’t find value in (or even find negative value in).

An example: I worked with a B2B online services company to help them determine what their customers valued/didn’t value in the industry segment the company operated in. We collected 50 or so stories from their customers, and ran the emergent constructs exercise with them to find the important customer values therein. They found that customers really liked responsive, personalized service, but didn’t like suppliers who appeared too small. They also didn’t have enough time so they valued time-savers of any kind. Plus they liked a supplier helping make them smarter, in other words extending their capability. They liked low prices, but were concerned that low price might indicate a supplier that wasn’t “industrial strength.”

These values, as you can see, aren’t straightforward to deal with. Anything the company did to enhance one value had some counter-effect. Amplifying a set of values might drive a customer segment away entirely. So they had to make hard decisions about things they were going to do and things they would do away with; customers they’d welcome, and customers they’d turn down. Once these difficult decisions were made, however, executing the plan wasn’t that difficult, and they could do it with confidence, given that they had a deep understanding of how customers really felt, grounded in the actual stories they told.

Sensemaking exercises like creating emergent constructs involve groups of people reading stories, answering questions, collaborating on meanings. Therefore, it’s difficult to do them with thousands or millions of transactions. How then do you narrow down the data? One simple way is to sample. Another way is to allow people who review the customer data to flag those that stand out in some way (perhaps using an Eureka button approach). Either way, gathering a bunch of stories and sending them through this process (see yesterday’s post for more on this) can illuminate very complex and nuanced issues for your company, products and brand, as illustrated above.

And once you have a grip on those, you’re prepared to use your existing decisionmaking processes to do something about them, and make real, vital improvements in your products and services.

Related posts:
Reading Between The Lines of Customer Stories
The Blackberry Storm/Twitter Project

I’m working on a project to listen to telephone sales calls and help the client find patterns explaining why some calls end up in a sale and others don’t. Each call is a story, complete with emotion, conflict, and turning points. Listening to dozens of these, pictures begin to emerge of how people buy, and how, even when they like the product and may want to buy, don’t. And it has nothing to do with logic.

One turning point I’ve experienced is the moment when a call turns from being headed to a close, to not. On the calls, it’s very subtle: a pause, a change of subject, perhaps an additional question from the prospect. But afterward, a call that seemed to be heading toward a sale instead is, at best, a promise to call back.

The best way to explain it is to relate a personal story.

I’ve been a customer of Verizon Wireless for more than five years. I got a telemarketing call from them today, offering inducements to renew my service contract early. I’ve been evaluating this for a while now (this is a subtext of my posts on the Blackberry Storm), and after discussing it at some length with my wife, we’re headed toward renewal.

This call, then, could have been Verizon’s way of closing the deal. I was pretty ready, although I was thinking of doing this in March. If the deal was good enough, perhaps I would pull the trigger today. The call went something like this:

“Mr. Caddell,” the rep said, “we are offering some extras today if you want to renew your contract early. You might be able to get a discount on a new phone.”

“When does my contract expire?”

“The end of July.”

“I thought it was the end of March.”

(turning point 1) “That’s the time when you are eligible for an early equipment upgrade. Your contract expires in July.”

“OK, what are you offering?”

(turning point 2) “100 extra minutes per month.” (This wasn’t attractive to me at all. We don’t use the minutes we have now.)

“How much off the phone?”

(turning point 3) “Well, you’ll be eligible for that at the end of March.”

“Earlier you said I could get a discount off a phone.” (I didn’t tell her that Verizon had already sent me two mailings offering me phone discounts for renewing now.)

“I said you might be.”

There was no way was I going to renew then. At each turning point, in fact, I became farther from renewing than I had been before the call. Instead of feeling happy, encouraged, eager to get a new phone, I felt frustrated, annoyed, and that I had wasted time even picking up the phone.

It wasn’t the rep’s fault. She was given a difficult product to sell (competing, in fact, with the company’s own mailings). When I began to ask pointed questions, the pitch fell apart. There was probably no rep on earth who could have closed me with that offer.

Which is a significant learning from this project for me. Selecting and training reps is only a part of the formula for success in telesales. The product must be useful, and the offer must be made attractive. And that work happens far outside the call center.

Like a lot of people, I’ve been trying to get a handle on what Twitter means for businesses. My professional interest is in finding unsolicited customer stories and making sense of them–wherever they are. In this, Twitter has a lot of promise. It’s easy to use, brief and spontaneous. So are customers using this forum to talk about products? I decided to find out.

My test case was the Blackberry Storm. It received an absolutely terrible review from David Pogue, the New York Times’ consumer-electronics columnist. It also had very good early sales numbers–500,000 units the first month of its release, according to the Wall Street Journal. The combination of these made it an irresistible subject to study: would the Twittersphere be flooded with posts from enraged buyers?

The project was made more interesting today, when the Wall Street Journal published an article entitled, “Bumpy Start for Blackberry Storm,” which referred to complaints of early Storm users (but not Pogue’s review), including this vibrant quote: “I found myself wanting to throw it in the ocean due to my frustration with its overall usability.” The article also referred to a release of firmware soon after launch intended to address some of the early complaints, particularly response time.

I used Twitter Search to look for messages containing “Blackberry Storm” and a happy or sad emoticon (there’s a button on the advanced search page that enables you to restrict searches this way). I looked at 88 English-language tweets going back to December 27. Here’s what I found:

The biggest surprise to me was: where were the complaints from users? While half the Tweets were from Storm users, as opposed to people commenting on the Storm, or thinking about it, only 4 out of 44 (9%) of the users’ tweets were negative, while 23 (52%) were positive.

(If you want to check out the searches I created for this project, they are here: happy search, sad search. Twitter Search has been acting funny the past few days–I’m only able to get one page of recent results, and can’t search farther back. I used an RSS feed of the search over a period of weeks to gather the entire list of 88,)

From a customers are talking perspective, this isn’t a terrible outcome at all for the Storm. Whether the firmware change made that much difference, or the Blackberry brand loyalists are immune to hardware glitches, or simply that devices like this aren’t perfect and users expect that–they are not saying this is a terrible device. Many are saying that they like it. If I’m Blackberry and Verizon, I’m not discouraged by the Storm’s initial reception.

By the way, the WSJ has already started to backtrack. On the web site, the article is now entitled, “Blackberry Storm Is Off To A Bit of a Bumpy Start.”

(Disclosure, I am a Verizon customer and a Blackberry 8830 user. If you think I am a shill for Verizon, please don’t make up your mind until you read this post, or this one.)

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.

Sorry for this somewhat awkward syntax of this post. I am composing it on my Blackberry 8830. This is significant because the post concerns David Pogue’s eviscerating review of the new touchscreen Blackberry Storm mobile phone.

In response to the review, Pogue received more than 100 stories of people who also hated their newly-bought Storm. There were also dozens of defenses of the Storm and RIM, its maker. And many more comments in response to the post.

Pogue’s post served as an attractor, stimulating all sorts of vibrant customer feedback. As a product manager and someone interested in innovation, this example is fascinating and illustrative of how social computing is revolutionizing market & customer intelligence.

What does all the feedback mean? Probably lots of things: the Storm has serious issues; RIM has committed, passionate users; Apple is a hard act to follow, etc.

Whatever it means, let’s hope that RIM is listening, sensemaking, and acting. If they want some guidance, tell them to shoot me an email.

By the way, composing this post on the 8830’s thumb keyboard & tiny screen has been agonizing. I was hoping the Storm might be my next step. Now I’m not so sure.

market research, narrative, product management, sensemaking, wireless, social media