Luc Girardin of Macrofocus contacted us in response to our post "When Will Survey Analysis Grow Up?" to point us to their SurveyVisualizer analysis tool. I had a little time this weekend to download and play with this application. There is a lot to like.

SurveyVisualizer is designed for surveys that have a hierachical or tree structure. Luc describes the relevant data structure in a background paper about the product:

The questions—also called quality criteria—are then aggregated into 23 quality dimensions (e.g. network quality, ticketing, cleanliness, security, reliability). They represent the level of satisfaction with a whole group of questions pertaining to a particular issue. The quality dimensions themselves are further aggregated into three different customer satisfaction indices, reflecting the different areas of responsibility.

The free download has multiple satisfaction "criteria" (e.g. friendliness of crew) roll up to "dimensions" (e.g. cabin crew) which fall under "indices" (e.g. index of flight services). This may be an appropriate structure for a satisfaction survey—but it isn't one I've encountered before.

Despite this limitation, the analysis capabilities delivered by SurveyVisualizer are intuitive and innovative. For example, all your survey data is displayed at once in a kind of relational map. This lets users visually identify patterns in the full set of results. Each of the vertical hashes represents a question or roll-up of questions. Clicking on any one of these hashes highlights the hierarchical relationships. The "ghost" lines represent the results across questions for a multitude of dimensions or respondent types.

Users have the ability to select specific dimensions to identify patterns in the corresponding results. An easy-to-use interface lets you choose a dimension then apply a color to the line within the relational map.

Also, users can click on individual display lines to investigate the results (e.g. I wonder who had that particularly crappy score for flight delays?)

If your analysis requirements don't fit this particular structure, Macrofocus has a more general-purpose tool called InfoScope.

At the 2004TED conference, Malcolm Gladwell tells the story of Dr. Howard Moskowitz, a man who revolutionized the prepared food industry through a new kind of analytical thinking. Long story short: Dr. Moskowitz was one of the first people to argue that companies should pursue multiple products targeted at customer subsegments rather than try to create the perfect product for all customers. He realized that an attempt to create a "platonic ideal" —whether it was pickles, mustard, or pasta sauce—would be a suboptimal result for most consumers. Consumers are individuals with preferences that are better clustered than averaged. Mr. Gladwell states that this change in business thinking (spurred by Moskowitz's study of pasta sauce) mirrors a more general scientific shift from a focus on universal truths to the study of variation.

The prepared food industry gets it—as evidenced by nine variations of Ragu sauce on the grocery shelves—but I'm not convinced that these lessons have permeated the rest of the business analytics landscape. In particular, I am struck by the inability of most survey analyses to reveal insights about respondents.

The tools may be part of the problem. Here's an example of what WebSurveyor provides its users to help them analyze online surveys:

Their site tells us:

"Each question is graphed independently allowing you greater flexibility in customizing the layout of reporting for each question...Filter results based on specific responses or cross-tabulate results from two different questions, giving you powerful tools for detailed analysis."

Powerful? Flexible? More like barebones. WebSurveyor is putting the analyst in a very constrained box that won't help deliver an better understanding of respondents. WebSurveyor's tool demands "question-centric" not "customer-centric" analysis.

Consider how this typical survey approach would serve you in an effort to understand the passengers of Noah's Ark. A surveyor would ask each animal to fill out basic information about their height, weight, number of legs, food preference, etc. The results would then let us know that the average animal weights 23 pounds, has a height of 1.2 feet, 5.6 legs, 30% omnivore and so on. All of which would miss the essential insight about the animals on board: there are two of each.

Unfortunately, the kind of analysis needed to reveal personality / needs / behavior clusters in your respondent population isn't well supported by out-of-the-box analytical tools. One approach is factor analysis—a statistical technique that is used in marketing to "identify the salient attributes consumers use to evaluate products in a category" (Wikipedia). Another approach is to examine individual visual representations of individual respondents—a technique that we term (rather clumsily): customer flashcards.

Funny thing happened to me last week on my way to an oil change - my car's engine was destroyed.

It all happened in a blink: I stopped by Jiffy Lube on my way to the bakery and swung up to the garage entrance, first in line. If you've ever had an oil change, you've probably experienced the old "preventative maintenance" up-sell: a technician pulls you out of the waiting room, gives you a grim, disappointed look, then explains the various parts of your car that are in severe need of service. In the past, I've been good at standing up to these automotive authority figures. I'd mumble "no thanks, maybe next time," not daring to look into the eyes which so clearly said: Don't you care about your own safety? This time, however, I broke down and gave the go ahead for an engine flush. I was assured that any sane car owner would have this procedure done every 15k miles; here I am at 80k without my first flush.

I knew something was wrong when I saw them pushing my car out of the garage half an hour later. I was assured it was no problem; they just needed to dry off my spark plugs. Two hours later I was calling for a ride.

All of which would have been a small inconvenience if I hadn't gotten a call the next morning letting me know they would need to replace my engine. Clearly something had gone terribly wrong with that engine flush.

I should say: I have little reason to gripe about Jiffy Lube. They are covering the engine replacement and a rental car. That said, there are a few lessons Jiffy Lube management might take from this situation:

• The edge cases matter. A while back we wrote (here and here) about analysis of anomalies and the opportunity for learning. One point that applies in this case: Collectively, outlier customers provide a service: they stress test the product and highlight unrealized strengths or weaknesses. In its desire to relentlessly upsell, Jiffy Lube has extended its service outside its comfort zone to a point of weakness.
• Data can make you smarter. I had an interesting conversation with the outside mechanic that is installing the replacement engine. He said I was lucky. My engine has a known problem with high levels of sludge build-up. He has seen other instances where an engine can be so full of sludge that an engine flush is incapable of breaking through the muck (like clogged arteries, I imagined) and the result is ruin. I get a refurbished engine with 80% new parts in place of an engine that was like the heart of an overweight cholesteral-holic. Maybe Jiffy Lube shouldn't be indiscriminantly upselling every customer. It wouldn't be difficult to build some filters into their system for high risk maintenance.
• Communicate with unhappy customers. Most companies would benefit from a simple alarm system for catching and responding to customers with particularly bad experiences. Something to appease them before they tell all their friends, family, and co-workers about their crappy experience (heck, they might even blog about it). All I ask as a customer is: a) recognize that you have created an inconvenience for me; b) convey that this isn't a status quo situation; and c) assure me that you will make me whole. Jiffy Lube wasn't effective in communicating any of these. They had an odd nonchalance that suggested this happens all the time, no single point of contact to speak to, and no apology for the inconvenience.

To a stranger, my two dogs look alike. To me, they couldn't be more different. They came from different dog shelters and are more than two years apart (that's 14+ human years). Here they are: Maddie has her chin resting on Ally.

Ally is twitchy, a mama's girl, frightened of loud noises, and getting creaky. Maddie is confident, independent, curious about the loud noise, and energetic. Ally loves other dogs and distrusts new people. Maddie adores all people and is suspicious around certain dogs. Their features and personalities couldn't be more different. I've had some time to get to know them.

When we meet a stranger on a walk (particularly one who isn't a dog owner), we often get: "They must be related." Our denials don't seem to phase these people as they point to the obvious evidence: "...but they are exactly the same size and color."

Superficial judgements are natural - a first level of defense to categorizing and manage a complex world. However, it's unhealthy to not try to dig deeper. For some businesses, superficial characteristics are as far as the analysis goes when segmenting or profiling customers. A better approach is to look at customer behaviors which provides a much more accurate reflection of interests and needs. Jim Novo, marketing consultant, agrees:

Customer behavior is a much stronger predictor of your future relationship with a customer than demographic information ever will be

Simple customer characteristics can be easy to come by; age, income, zip code are probably part of your basic customer database. In contrast, behavioral segmentation is a more initimating analytical challenge. Here's the approach we've used successfully at Juice:

1. Create individual pictures of customers that visually show their behaviors over time. The trick is to create a "visual language" that represents actions and is intuitive
2. With a dash of Excel, SAS, and python code, we generate thousands of these pictures of individual customers
3. We visually scan for common patterns of behaviors and the associated success/failure points (e.g. repurchase, upsell, churn, etc.)
4. Finally, we work backwards from our new understanding of behaviors to segment customers based on statistical measures of behaviors.

This approach differs from traditional data mining-based approaches that drill down from the top looking for patterns. We start at a very granular level and looks for patterns (using the power of the human visual system). It may sound a little crazy, but we've found that it can be both insightful and highly predictive.

The best businesses connect with their customers. They build intimate relationships, learn, and extend their products using this knowledge. After Apple learned that customers were using iPods to save addresses and data, they incorporated this feature into their next release. Intuit heard their small business customers saying, “I need to keep the books without the complexities of accounting” and QuickBooks was born.

Many companies have a different story. For them, technology has been a killer app—it’s killed the ability of individuals in the company to see their customers as individuals. Customers are a list to be manipulated, a total in a spreadsheet. They aren’t seen as people, much less as potential innovators. Dependence on big information systems is a source of the problem. These technology solutions are built to be comprehensive; built for speed; built for anywhere, anytime access. They aren’t built to understand individuals one at a time.

Sometimes the inability to understand customers stems from a business' impatience and short-term focus on ROI. Tom Asacker pulls out an early marketing guru to make his point:

Abraham Lincoln on chopping down a tree: "If I had six hours to chop down a tree, I'd spend the first four hours sharpening the axe".

Instead, what do most marketers do? They take a whack at the tree, put down the axe, measure the cut, pick up the axe, whack the tree in a different spot, and repeat ad nauseum. Exhausting, to say the least.

If you are in an information-rich business with many customer interactions—you can know your customers intimately. You can look at individual customer behaviors and start to recognize important and startling patterns. It will take some time, but Abe would say it is time well spent.