科技美学用户研究 | The Kano Analysis 卡诺模型解析

今当Medium看到同样首用研方法的牵线—— 卡诺模型(The Kano
Analysis)。
用来分析一个产品效果是否会如用户满意,这种办法的独到之处是会查获一个翔实的多寡结论,帮助决策。计算方式吗死简单的,问卷操作也比易于简单,成本大没有。我本人特别欣赏这种会迅速验证的小本钱措施,能在简单的辰及人力资源条件下,也能高效得出可靠的多寡参考和清的定论。正使己直达同首文章介绍的点子同一,能大大提高沟通效率,减少无谓的口角。本方对职能的五栽划分为蛮有意思,让自身回忆锤子科技,锤子的无绳电话机就是是特意讲究软件上的Delightful
Features而忽略任何硬件及之Required
Features。这就算正好解释了为何锤子销量与认知度无成为正比。

原稿我不怕无备翻了,我光拿关键的内容翻译下,由于Medium需要梯子,所以我于征询作者同意的图景下把原本和粘贴于这。有楼梯的同班要点此传送。 

为行为科学家赫兹伯格的双双素理论的诱导,东京理工大学教授狩野纪昭(Noriaki
Kano)和他的同事Fumio
Takahashi于1979年10月登了《质量的调理因素跟振奋因素》(Motivator and
Hygiene Factor in Quality)一文。

欠方法把产品功效分也以下五种:

Desired Features 期望功能 

当提供者作用,用户满意度会提升,当不提供者功能,用户满意度会下跌;

Required Features 必备功能 

当优化是作用,用户满意度不见面升级,当不提供者功效,用户满意度会大幅下挫;

Delightful Features 魅力功能 

用户意想不到的,如果非提供者功效,用户满意度不见面降低,但当提供者意义,用户满意度会有死挺提升;

Indifferent Features 无差异功能

凭提供或无提供者意义,用户满意度都未会见时有发生改动,用户向未注意;

Anti-feature Features 反朝作用

用户向还尚未是意义,提供后用户满意度反而会跌。

法运用问卷提问的措施收集数据,最后经数量整合得出一个效能的点滴单系数:满意系数
Satisfaction Coefficient 
匪乐意系数 Dissatisfaction
Coefficient。假设满意系数超过非如意系数,该意义值得做。

以下是原文内容 Let’s Go:

The Kano Analysis

A Better Way Discover What Users Really Want From Your Product

by Brian
O’Neill

You’re on the design team for Crunchrr, a new app that helps users
discover cereals they’ll love. Users can:

– Create a profile and connect with others

– Discover cereals based on their preferences

– Rate and review cereals

Crunchrr is in the hands of some early adopters who are loving its core
features. Things are going great. That is, until the requests start
rolling in.

Annelise from marketing says: “Crunchrr needs a map view so users can
see where each cereal is made. People are really interested in where
their food comes from nowadays, so this is really a must! Besides, every
app has a map view.” Kevin from sales was at a meeting with a potential
advertiser who asks: “Where’s the chatbot? You can’tnothave a chatbot.
Conversational UI is the future!”

One of your early adopters pings you to suggest: “There should be a
button so I can email the cereal maker to request a gluten-free
version.” Another one says: “Maybe there could be something like Shazam
for cereal. That way, if I’m in a restaurant I can take a picture of
what the person at the next table is eating and it’ll show me what that
cereal is.”

The next thing you know, your backlog is a gaggle of suggestions,
requests, and demands. It seems that everyone has brilliant idea that
justhasto go into the next release.

This can’t be avoided. Everyone has an opinion and given the
opportunity, they’ll express it. And people easily fall into a “more is
better” mentality. More features equals a better product, and the more
of each feature, the better.

The obvious problem is that you can’t deliver on every request. Not only
that, but all ideas aren’t created equal, and users are often at a loss
as to how to articulate what they really want and need. On the other
hand, internal stakeholders tend to view features in the narrow context
of their own interests. How do you stop the madness?

“The most important thing that a team can do to help their design is
to say no to almost any idea for a feature”

— Jared Spool

You need a way to predict user satisfaction that lets you prioritize
feature releases and even re-evaluate existing features. And you need
hard data to support your decisions about what goes into Crunchrr and
when. That’s where theKano
Modelcomes
in.

The Kano Model

In 1984 professor Noriaki Kano presented a model that predicts how
satisfied people will be with a product based on its features. Since
then, the Kano Model has become a standard design tool because of how
effectively it can make typically invisible ideas about quality visible.
The core principle of the model is that satisfaction can be plotted
along five distinct
curves.[1]

Curve 1: Desired Features

Remember when I said more isn’t always better? Well,sometimesit is it
is. More storage space or battery life is better. Faster download
speeds? Better. These are all examples of where the user will usually
express greater satisfaction in direct proportion to how much of the
feature they get.

With desired features, satisfaction is directly proportional to feature
implementation

In the case of Crunchrr, desired features could be:

– Speed and responsiveness

– Number of users to connect with

– Suggestions based on stated preferences and past browsing behavior

– Options for quickly zeroing in on a kind of cereal (sorting,
filtering, etc.)

– Size of cereal selection

Curve 2: Required Features

Required features are the ones users expect and take for granted.

With required features satisfaction levels off once the basic need has
been met

Users are dissatisfied when a required feature is not present and
satisfied when it is. But that satisfaction levels off after a certain
point. This makes sense when you think about it. If a wheel doesn’t
roll, it will cause dissatisfaction. If it does roll, it will cause
satisfaction. But it’s hard to get anyone excited about a wheel that
rollsreally, reallywell. In the case of Crunchrr, as with most other
apps, this could mean things like:

– Reliable uptime

– Search

– Ability to create a profile

– Easy log in/out

Curve 3: Delightful Features

Delightful features are the ones that make an app fun to use and give it
a personality. They’re the features you love, but don’t expect. It could
be as simple as when the login form appears to shake its head when you
enter the wrong credentials. Or it could be the tone of the writing or a
fun mascot character or some unique interaction.

Users are satisfied with delightful features, but are not dissatisfied
when they are absent

As you can see from the graph, users express increased satisfaction with
delightful features. But there’s no dissatisfaction when they’re not
present. Also, as with required features, there’s a limit to just how
delighted a user can be. After a certain point, there are diminishing
returns. ­

Annelise’s map view is probably an example of a delighter because it’s
little more than eye candy, and it certainly isn’t solving any of the
currently defined business needs for Crunchrr.

Delightful features are an important part of the user experience, and
shouldn’t be ignored. Butthey come with a shelf
life,
in part because they’re so easily imitated. For a while, the swiping
interaction was a big part of Tinder’s unique identity. Now, Tinder is
justone of many
appswhere
users can swipe left or right. In other words, over time, delightful
features go on to become desired or even required features.

Curve 4: Indifferent Features

These are features the user simply doesn’t care about either way.
Whether they’re implemented fully or not at all, they won’t change
users’ opinions about the app, or change how they use it.

Neutral features don’t affect satisfaction one way or another

Curve 5: Anti-features

Anti-features are the features that users actively dislike.
(Remember Clippy?)
And the more these features are implemented, the greater the
dissatisfaction. Anti-features are like the mirror opposite of desired
features.

Anti-features are the ones that frustrate or annoy users.
Dissatisfaction is directly proportional to implementation

Putting it All Together

Looking at all of these features together not only provides a clear
pictorial representation of how features will be perceived, but also
helps you figure out strategic direction.

The complete Kano Model diagram

Desired Features:Resources should be invested heavily in these features,
because they are key to user adoption and retention, as well as
competitive advantage

Required Features:Resources should be invested heavily in these
features, but only until basic needs have been met.

Delightful Features:It’s fine to invest resources here, but not at the
expense of desired and required features. However, delightful features
are often key differentiators that can build loyalty and buzz.

Indifferent Features and Anti-features:Resources should be invested only
in identifying these so as not to waste cycles on building and
implementing them.

By now I hope you’re sold on the Kano Model. Then the next question is:
How do you find out which features belong to each category? That’s where
the Kano Analysis comes in.

The Kano Analysis

To find out which features belong where, we need to ask our users. But
remember, users are not usually great at identifying or expressing what
they really want and need. The Kano Analysis accounts for this by asking
questions in pairs: afunctional questionfollowed by adysfunctional
question. Let’s go back to Annelise’s suggestion of a map view for
Crunchrr. We could ask a question pair about this feature like this:

If Crunchrr let you see on a map where a brand of cereal is made, how
would you feel?

If Crunchrr did not let you see on a map where a brand of cereal is
made, how would you feel?

For both functional and dysfunctional questions, users must choose one
of the following answers:

– I like it that way

– I expect it that way

– I am neutral about it

– I can live with it that way

– I dislike it that way

You would prepare an entire questionnaire in this style for each of the
features in your backlog. Each user’s answers can then be analyzed by
plotting its outcome in the following table.

The analysis table tells you where a user would place a feature in the
Kano Model based on how the functional and dysfunctional responses
compare

It should be clear that if a user likes it when the feature is present
and dislikes it when it’s not, then that is a desired feature. The
designation ofquestionablehappens when the answers apparently contradict
each other. (This is often the result of the user not understanding the
question.)

Great. We’re almost done. The final piece is to aggregate all of the
survey responses to find the overall results for each feature.
(Alternatively, you could break this down even further and aggregate
responses based on personas.)

Coefficients

After you’ve aggregated all of the responses, you’ll calculate the
satisfaction and dissatisfaction coefficients. The satisfaction
coefficient is a number between 0 and 1: the closer to 1, the stronger
the influence on satisfaction. The dissatisfaction coefficient is a
number between 0 and -1: the closer the closer to -1, the stronger the
influence on dissatisfaction. We calculate the coefficients with these
formulas:

Let’s say that the aggregated responses for the map view breaks down
like this:

Desired: 5%

Required: 12%

Delightful: 4%

Indifferent: 23%

Anti-feature: 31%

Questionable: 25%

That would give you these results:

Satisfaction: (4 + 5) / (4 + 5 + 12 + 23) =0.2045

Dissatisfaction: (5 + 12) / (4 + 5 + 12 + 23) * (-1) = -0.3864

As you can see, the map view feature is having a significantly stronger
influence on dissatisfaction than on satisfaction. This clearly
indicates that we should leave it out of Crunchrr. Sorry, Annelise!
(Actually, if you saw these results in the real world, you wouldn’t even
need to calculate the coefficients. Seeing 31% anti-feature and 25%
questionable is enough to tell you not to include this feature. I used
these exaggerated figures to highlight the differences produced in the
coefficients.)

Other times, the coefficients will show little difference in influence.
Cases like those will require a judgement call or re-testing.

In Closing

A Kano Analysis is cheap and easy to perform and provides clear vision
into what users actually want and expect from your product. It also
provides hard data, which breaks everyone out of the trap of biased or
shortsighted thinking. There’s no need to argue and debate with internal
stakeholders about which features are in or out. The numbers don’t lie!

Brian O’Neill @brianeoneill is a designer in the San Francisco Bay Area,
currently at NVIDIA.

[1]These
curves go by many different names, depending on the source. I picked
these names arbitrarily. In the end, it doesn’t matter what they’re
called.