Marketers sometimes have to be creative to offer customers something new without the luxury of that new item being a brand-new product or built-from-scratch service. In fact, incrementally introducing features is familiar to marketers of consumer goods. But what features are consistently being selected by customers? What should marketers look for to tell development teams, production teams, and partners to plan upon? Applying a regression may help.

營銷人員有時必須具有創造力，才能為客戶提供一些新東西，而不能將新商品的奢侈性視為全新產品或從頭開始的服務。 實際上，消費品的營銷人員已經逐漸引入功能。 但是客戶一直在選擇哪些功能？ 營銷人員應該尋找什么來告訴開發團隊，生產團隊和合作伙伴進行計劃？ 應用回歸可能會有所幫助。

It’s not an easy question to answer, even among startups that usually have a unique take on a market. Entrepreneurs other confuse features with products and services, leading to poorly crafted business models. Products and services solve problems for people. Features are characteristics that add value to the products.

即使在通常在市場上占有獨特地位的初創企業中，這也不是一個容易回答的問題。 企業家將產品和服務的其他功能混為一談，從而導致制作不當的商業模式。 產品和服務為人們解決問題。 功能是增加產品價值的特征。

The major challenge for managers is determining what features would add value in a sustainable way. It’s easy to brainstorm ideas about what features a product or service should contain. The real challenge however is selecting that features that will draw customers consistently. This is the essence of building a business model.

管理人員面臨的主要挑戰是確定哪些功能將以可持續的方式增加價值。 就產品或服務應包含的功能集思廣益的想法很容易。 然而，真正的挑戰是選擇能夠持續吸引客戶的功能。 這是建立業務模型的本質。

The Chevrolet Corvette Grand Sport is an example of selecting features to produce a different product offering. The Grand Sport trim level was introduced in 2017 for the C7 Corvette. C7 is a nickname for the seventh generation of Corvette. The latest version, a mid-engine is a C8, was introduced in 2019.

雪佛蘭Corvette Grand Sport是選擇功能來生產其他產品的示例。 Grand Sport內飾級別于2017年為C7 Corvette引入。 C7是第七代Corvette的昵稱。 最新版本是中置引擎C8，于2019年推出。

The Grand Sport is a hybrid of exterior and chassis features from a more expensive model, the Corvette Z06, with the engine and core interior features from the entry-level Corvette. The Grand Sport reached a 30% share of overall Corvette vehicle sales in its first year, so from an automotive industry perspective, it successfully contributed significant sales for the model.

Grand Sport混合了更昂貴的Corvette Z06的外觀和底盤功能，以及入門級Corvette的發動機和核心內飾功能。 Grand Sport在第一年就占據了Corvette整車銷售的30％，因此，從汽車行業的角度來看，它成功地為該車型貢獻了可觀的銷售。

Combining features are not too complicated, a good brainstorming session with insightful customer comments can yield some ideas as to what features may be worth investigating. But you may want to see if those choices seem really correlated and sustainable. The last part is the most important. The last decision you want to make is one where features indeed seem correlated but are more of a blip rather than a predicted trend.

組合功能并不太復雜，一個很好的頭腦風暴會議以及有見地的客戶評論可以就可能值得研究的功能產生一些想法。 但是您可能想看看這些選擇是否看起來確實相關且可持續。 最后一部分是最重要的。 您要做出的最后一個決定是，其中的功能看上去確實相關，但更多的是暫時的，而不是預測的趨勢。

Regressions can back up a professional intuition by asking what sort of features are sustainable enough to support sales. A regression determines if data associated with the features reflect a relationship that represents sustainability. Regressions can add a logical influence on a business decision, especially on products that receive a ton of “fanboy” social media commentary but remain unclear if that interest turns into real sales. Fanboy comments on iconic sports cars like the Corvette are no exception.

回歸可以通過詢問什么樣的功能足以支持銷售來支持專業直覺。 回歸確定與要素關聯的數據是否反映了代表可持續性的關系。 回歸可以對業務決策產生邏輯上的影響，尤其是對收到大量“粉絲”社交媒體評論但對這種興趣是否轉化為實際銷售尚不清楚的產品。 Fanboy對像Corvette這樣的標志性跑車的評論也不例外。

A useful first step in planning data is to consider a feature and how its observations are being measured. For example, if you want to compare engine size for a powered object like a lawnmower or motorcycle, then you would have a column with engine sizes for observations.

計劃數據的有用的第一步是考慮一個特征以及如何測量其觀測值。 例如，如果要比較割草機或摩托車等電動對象的發動機尺寸，則將有一個列，其中包含用于觀察的發動機尺寸。

Determining a correlation is the next step. Gaining an idea of how the data roughly takes shape can be done in different ways. One quick-and-dirty way to create a scatter plot to see how the data takes a shape. A correlation is a more specific way to see if there is a linear correlation. The range of correlations is from -1 to 1. How close each value is calculated demonstrates the strength and direction of the correlation. You can then begin to examine a regression method that will likely provide the best fit that represents the correlation calculated.

確定相關性是下一步。 了解數據如何大致成形的想法可以通過不同的方式來完成。 一種創建散點圖以查看數據如何成形的快捷方法。 相關性是查看是否存在線性相關性的更具體方法。 相關范圍是-1至1。計算每個值的接近程度證明了相關的強度和方向。 然后，您可以開始研究一種回歸方法，該方法可能會提供代表所計算相關性的最佳擬合。

A regression treats each feature as independent variables, then determines which one correlates to a dependent variable: the output we want. That output is usually sales, but the regression can also be created for a variation of sales, such as purchase orders or market share. Linear regressions are the simplest regression models. They can be made with just a few columns of data. They also assume a linear relationship between the outcome and the predictor variables.

回歸將每個特征視為獨立變量，然后確定哪個特征與因變量相關：我們想要的輸出。 該輸出通常是銷售額，但是也可以為銷售額的變化(例如采購訂單或市場份額)創建回歸。 線性回歸是最簡單的回歸模型。 它們可以僅由幾列數據組成。 他們還假設結果與預測變量之間存在線性關系。

For our Corvette example, a logistic regression can be useful. In a logistic regression, the dependent variable represents a binary choice (yes or no, approve or rejected). In this instance, it is easy to imagine columns of features and the dependent variable represents a binary choice, a sale or no sale. Dependent variables can also be represented as a lift in sales or sales decrease or an increase or decrease in market share.

對于我們的Corvette示例，邏輯回歸可能會有用。 在邏輯回歸中，因變量表示二元選擇(是或否，批準或拒絕)。 在這種情況下，可以輕松想象特征列，并且因變量表示二進制選擇，出售還是不出售。 因變量也可以表示為銷售額上升或銷售額下降或市場份額的上升或下降。

The method of regression can be influenced by the normality of the observations. The shape of the scatter plot can be a simple reveal. Does the data imply that a line best represents a regression, or is there a general curve that suggests a logistic regression? In most instances where the shape is not immediately obvious, you would apply a test such as Q-Q test.

回歸方法可能會受到觀測值的正態性影響。 散點圖的形狀可以很簡單地顯示出來。 數據是否暗示一條線最能代表回歸，還是有一條總體曲線表明對數回歸？ 在大多數情況下，形狀不是立即顯而易見的，您可以應用諸如QQ測試之類的測試。

No matter how you set up a regression, a good model reveals a strong correlation among the features represented as the influential independent variables, which means finding the right combination of features that would likely entice a sale. A regression model may not be needed in every instance of combining features. But they are valuable in instances of uncertainty, particularly as a service adds an offering.

無論您如何建立回歸，一個好的模型都會揭示以有影響力的自變量表示的特征之間的強相關性，這意味著找到可能會吸引銷售的正確特征組合。 在每個組合特征的實例中可能都不需要回歸模型。 但是它們在不確定的情況下非常有價值，尤其是當服務添加產品時。

Overall regressions can provide a robust means to reinforce intuition on feature combinations or enlighten opportunities for new services. Either case can lead to better business choices for products and services that can incrementally increase sales while minimizing investment.

整體回歸可以提供一種強有力的手段來增強功能組合的直覺性或啟發新服務的機會。 不論哪種情況，都可以為產品和服務帶來更好的業務選擇，從而可以在不增加投資的情況下逐步增加銷售額。