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Choice-Based Conjoint(CBC) 選擇聯合分析軟體

Choice-Based Conjoint(CBC) 選擇聯合分析軟體

  • Choice-Based Conjoint(CBC) 選擇聯合分析軟體
  • 編號
  • 類別
    研究分析軟體
  • 介紹
    CBC/ACA是Sawtooth softwaret旗下久負盛名的聯合分析(Conjoint Analysis)軟體,聯合分析也稱為結合分析。CBC/ACA可以獲得水平的效用值,屬性的相對重要程度,市場細分,預測市場偏好份額等分析結果。
  • 價格

Choice-Based Conjoint(CBC) software

Introduction to conjoint analysis
Conjoint analysis is a category of research methods, among which choice-based conjoint is the most popular, that mimics the respondent’s real world tradeoffs when making decisions. It is used for pricing studies, product optimization, healthcare options and many other things. To get a more indepth understanding of conjoint analysis, refer to our page on conjoint analysis.

What is choice-based conjoint (CBC)?
When you want to understand and predict how people make choices when facing challenging tradeoffs, Choice-Based Conjoint (CBC) is the most widely-used survey-based approach.
CBC also is known as discrete choice modeling (DCM) or
discrete choice experiments (DCE).

How does choice-based conjoint work?
Choosing a preferred product from a group of products is a simple and natural task that everyone can understand. The difference between choice-based and the earliest approaches to conjoint analysis is that the respondent expresses preferences by choosing from sets of concepts, rather than rating or ranking them.
The combination of attribute (feature) levels we ask respondents to evaluate is critical to making conjoint analysis work properly. Fortunately, Sawtooth Software takes care of those details (though you may import your own designs if you wish).
Each level appears nearly an equal number of times and appears with levels from other attributes nearly an equal number of times. This makes for a fair and balanced (orthogonal) experiment where the utility value (the preference) of each attribute level can be measured independently and with high precision.
We can assess the relative impact of each attribute level on choice just by counting "wins." But, it’s more precise to fit models such as multinomial logit (MNL), latent class MNL, and hierarchical Bayesian (HB) estimation.
HB is the most popular approach and leads to a set of utility scores for the attribute list for each respondent. Latent class can find groups of respondents who are very similar to one another in their choice preferences, while being very different between the groups. Thus, latent class is an excellent approach to leverage CBC data for needs-based segmentation and strategy.
Use Cases for CBC
Researchers often use CBC to study the relationship between price and demand, especially when the price-demand relationship can differ from brand to brand. One of the strengths of CBC is its ability to deal with interactions, such as between brand and price, or when different colors work better with different styles.
Choice-Based Conjoint (CBC) is used in marketing and economics applications across a variety of cases and industries, including:

• New product design, existing product redesign or line exten
   sion

• Pricing studies
• Market segmentation
• Healthcare choices, including cancer regimens
• E-commerce
• Employee research (health plans, benefits)
• Transportation choice
• Green energy, electric vehicles, ride sharing
• Public health
• Education
• Environmental impact
• Finance, banking
• Technology and innovation
 

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Windows, Windows 95, Windows 98, Windows 2000, Windows XP, Windows Vista, Windows NT, Excel, PowerPoint, and Word are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries.

 

Choice-Based Conjoint(CBC) 選擇聯合分析軟體

聯合分析導論
聯合分析是一類研究方法,其中基於選擇的聯合最為流行,它模仿了決策者在現實世界中的權衡取捨。它用於價格研究,產品優化,醫療保健選擇和許多其他事情。要更深入地了解聯合分析,請參閱我們的聯合分析頁面。

什麼是基於選擇的聯合(CBC)?
當您想了解和預測人們在面臨艱難的權衡時如何做出選擇時,基於選擇的聯合(CBC)是使用最廣泛的基於調查的方法。CBC也稱為離散選擇建模(DCM)或離散選擇實驗(DCE)。
基於選擇的聯合如何工作?
從一組產品中選擇一種首選產品是每個人都可以理解的簡單自然的任務。基於選擇的方法和最早的聯合分析方法之間的區別在於,受訪者通過從概念集中進行選擇來表達偏好,而不是對其進行評級或排名。
我們要求受訪者評估的屬性(特徵)級別的組合對於正確執行聯合分析至關重要。幸運的是,Sawtooth Software會處理這些細節(儘管您可以根據需要導入自己的設計)。
每個級別出現的次數幾乎相等,並且來自其他屬性的級別出現的次數幾乎相等。這使得公平和平衡(正交)的實驗成為可能,每個屬性級別的效用值(偏好)都可以獨立且高精度地進行測量。

我們只需計算“勝利”就可以評估每個屬性級別對選擇的相對影響。但是,更適合模型,例如多項式logit(MNL),潛在類MNL和分層貝葉斯(HB)估計。
HB是最流行的方法,可為每個受訪者的屬性列表提供一組效用得分。潛在班級可以找到在選擇偏好上彼此非常相似的一組受訪者,而這些群體之間卻有很大差異。因此,潛在類是利用CBC數據進行基於需求的細分和策略的絕佳方法。
CBC使用案例
研究人員經常使用CBC研究價格與需求之間的關係,尤其是當品牌之間的價格需求關係可能不同時。CBC的優勢之一是它具有處理相互作用的能力,例如品牌和價格之間的相互作用,或者當不同的顏色以不同的樣式更好地工作時。
基於選擇的聯合詞(CBC)在各種情況和行業中用於市場營銷和經濟學應用程序,包括:

• 新產品設計,現有產品重新設計或產品線擴展
• 定價研究
• 市場細分
• 醫療保健選擇,包括癌症治療方案
• 電子商務
• 員工研究(健康計劃,福利)
• 交通選擇
• 綠色能源,電動汽車,乘車共享
• 公共衛生
• 教育
• 對環境造成的影響
• 金融,銀行
• 技術與創新
 

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