LINESCALE METHODOLOGY
PREFERENCE SEGMENTATION AND DRIVER ANALYSIS

PREFERENCE SEGMENTATION.  The Linescore[reg] INSTANTLY REPORTS HOW MANY people, WHO, and WHY they score as:
ACCEPTORS - Completely happy with you, your idea or whatever you are researching.
BORDERLINE - Potential Acceptors, and what benefit or issue needs attention.
INDIFFERENT - Not interested or generally not satisfied. 
REJECTORS  -  Not in the target market, dissatisfied

DRIVER ANALYSIS shows HOW and WHY each Preference Segment differs from the others quantitatively and in each person's own words.

First, A very brief history of marketing research and the Linescale Breakthrough

 Marketing Research traces to Curtis Publishing in the late 1930's and the Quartermaster Corps in the mid 1940's. Researchers used psychological scales developed for the technology of the time. These paper and pencil tools survived the era of telephone and direct mail interviewing, and most online research today still traces its lineage to classic Likert, Guttman and Thurstone scales. With good reason. These classic scales measured reliably and well. They do the best measurement job possible short of using a computer for input.

But there is now a better way.

Classic scales measure one item on one scale. Identical, but separate scales are used for testing different items. The difficulty comes in interpretation. The researcher wants to compare two or three or ten "cucumbers" rated on independent rating scales to each other although they were not actually compared to each other. So we use statistical methods to "make the comparisons" of preference and difference, to infer what would have happened if the items had been able to be compared directly.  But, other than head-to-head product tests, it isn't practical to do paired comparison testing of attributes, imagery items, service features, satisfaction levels or Web site experiences. So we use statistics to infer what the preferences would have been if people tested each cucumber against each other cucumber. And we establish "norms", or "What's a good mean or top box score for a cucumber versus a good mean or top box score for a potato - or a fast food service experience, etc."

There is now a better solution. And it is not conjoint testing, which exhausts both consumers and research budgets. The simple solution is the Linescale.

The Linescale is a simple scale that easily allows multiple "cucumbers" to be placed on the same scale. Each cucumber rated is rated not only on a very discriminant, fine-gradient scale, but each cucumber is directly compared against every other cucumber previously placed on this same scale.

It's as if the cucumbers were boys in a gym class told to get in line by height. "If the guy in front of you is taller than you, step in front of him." The Linescale asks the consumer to do the same thing to cucumbers, except we line them up by preference - or texture, or color, or perceived healthiness, or flavor, or likelihood of recommending it to friends and family.

Best of all, we can line the cucumbers up and see how they rate compared to the cucumbers you eat most often. And at the same time we can compare them with your rating of what you expected of this cucumber. And we don't need to infer direct comparison. We directly compare.

This is a liberating concept. Not only does it improve measurement, but it creates something new. We know how each individual rated your cucumber compared to the cucumbers she usually eats, and her expectation for this cucumber based on what you promised about it.

This let's us easily calculate whether Susan loves the test cucumber and will probably eat it in preference to the cucumbers she usually eats. Or whether Susan really likes the test cucumber, but is missing something in it she wants - or it has one negative feature she wished it did not. Or, whether Susan generally isn't happy with the test cucumber. Or whether Susan really, really dislikes the test cucumber. Rather than just sample averages, we can now have a quantum, a count of Individuals in each satisfaction segment.

Satisfaction, or Preference Segmentation

This creates a new kind of Segmentation: Satisfaction Segmentation.

We call those who love the cucumber compared to both expectation and competition, Acceptors.

We call those who generally like the test cucumber but either miss a benefit or have a concern, Borderline.

We call those who are generally not happy with the test cucumber, but don't feel strongly about it, Indifferent.

And, we call those who don't like the test cucumber, Rejectors.

This makes for a marvelously simple and practical analysis. "How many people who tried the cucumber are Acceptors." And of course, "Who are they and what do they eat today?"

Since we are using the computer it's quick and easy to do. We are able to calculate instantly whether a person is an Acceptor, Borderline, Indifferent or a Rejector - and show them their score and its meaning, and then ask if they agree with their score. Most do. Then we ask them, in their own words, to tell us why they rated the cucumber that way. We get a pretty good verbatim.

Since no one can code, or divine the meaning of an utterance as well as the utterer, we ask this same person to then select up to five main reasons for their rating from a list of positive and negative possible reasons for their rating.

Direct comparison of how many Acceptors versus how many Rejectors selected each reason lets us instantly calculate a Driver Analysis showing why the Acceptors loved the cucumber, and why Rejectors hated the cucumber. Same for Acceptors versus Borderline, etc. And all this instantly and without cluster, regression or factor analysis.

The good news is this. At the same time the Linescale creates all those direct ratings and comparisons, it creates the familiar six (or five or ten) point scale means, top box scores, next box scores, etc. So, you can have your familiar cake while enjoying a tasty new one.

As an aside, a powerful thing happens when the test cucumber is rated against each person's favorite cucumber. You've got to be good to beat her favorite cucumber, and to be better, your cucumber must be placed higher on the preference scale than her favorite cucumber. A tough bar to cross. But, if she crosses that bar, you'll earn a spot in her refrigerator (res paribus, as they say).

This is also true for ice cream sandwiches. Ice cream sandwiches will usually be rated higher on a preference (or hedonic) scale than cucumbers. But, we don't have to worry about how high or low the favorite is rated in different product categories. The test item has the same challenge. Will the test item beat the favorite? It's as difficult for a test cucumber to beat her favorite cucumber as it is for a test ice cream sandwich to beat her favorite ice cream sandwich.

So, the powerful outcome is 30% of a sample being Acceptors of a test cucumber means remarkably the same thing as 30% of a sample being Acceptors of an ice cream sandwich. Category norms disappear. There are Concept Norms, And, there are Product norms, Ad norms, Web site norms, Customer Satisfaction norms. But, while norms differ for types of research, they do not materially differ for the same research type in different product categories.

Let us show you. Click here for a few demos.