ai人工智能收入

介紹 (Introduction)

Pricing optimization is a powerful lever for revenue growth, yet it’s too often put in the too-hard basket by too many companies.

定價優化是實現收入增長的強大杠桿，但太多公司經常將定價置于過于艱難的境地。

This is because traditional pricing optimization methods can be both complex to implement and limited in their ability to accurately capture the full range of factors that can impact pricing.

這是因為傳統的定價優化方法實施起來可能既復雜，又無法準確地捕獲可能影響定價的所有因素。

Machine learning (ML) is well-suited to pricing optimization problems — both for its ability to handle complex features, as well as its ability to generalize to new situations. Moreover, recent advances in managed services has put these ML solutions within reach of virtually any organization.

機器學習(ML)非常適合定價優化問題-既具有處理復雜功能的能力，又具有將其推廣到新情況的能力。 此外，托管服務的最新進展使這些ML解決方案幾乎可以在任何組織中獲得。

In this anonymized example we explore how a company with no data science expertise was able to use managed ML services to implement an ML-powered pricing strategy that performed 2x above traditional approaches and resulted in estimated revenue growth of 11%.

在這個匿名的示例中，我們探索了一家沒有數據科學專業知識的公司如何能夠使用托管的機器學習服務來實施以機器學習為動力的定價策略，該策略的執行效率是傳統方法的2倍，并且估計收入增長了11％。

情況 (Situation)

FitCo is a premium fitness brand, based in Los Angeles, that operates a portfolio of over 600 gym and fitness center locations across the United States.

FitCo是一家總部位于洛杉磯的高級健身品牌，在美國擁有600多個健身房和健身中心。

Having grown rapidly by acquisition over the past several years, management attention had now turned its attention to boosting organic revenue growth, which had been stubbornly flat on a per-studio basis.

在過去幾年中，通過并購獲得了快速增長，現在，管理層的注意力已經轉移到了促進自然收入增長上，而這種增長在每個工作室的基礎上都持平。

FitCo had identified FitClass — its suite of specialty fitness classes — as a prime source of organic growth. Specifically, it had identified pricing of these classes as a major potential area of improvement.

FitCo已將FitClass(其專業健身課程套件)確定為有機增長的主要來源。 具體而言，它已將這些類別的定價確定為主要的潛在改進領域。

Photo by Geert Pieters on Unsplash Geert Pieters在Unsplash上的照片

FitClasses are a popular offering across FitCo’s brands. They are premium experiences catering to niche fitness demand and sold on a pay-per-class basis on top of standard memberships.

FitClasses是FitCo各品牌的熱門產品。 它們是滿足小眾健身需求的優質體驗，并在按標準付費的基礎上按等級收費銷售。

Whilst FitCo had ensured a consistent user experience across its portfolio, local operators were still able to set schedules and prices for FitClasses in their studios with nearly total independence. As a result, prices varied widely between classes and locations.

盡管FitCo在其產品組合中確保了一致的用戶體驗，但本地運營商仍能夠在幾乎完全獨立的情況下在其工作室中為FitClasses設置時間表和價格。 結果，價格在班級和地點之間差異很大。

Whilst FitCo understood that some of this variance reflected local conditions, they also suspected there was considerable room for improvement in the way prices were set across its portfolio.

FitCo理解這種差異反映了當地情況，但他們也懷疑整個投資組合的定價方式仍有很大的改進空間。

并發癥 (Complication)

FitCo had undertaken a pricing exercise two years ago in which prior management had opted to centralize FitClass pricing and institute a blanket price increase of between 10% and 20% across the board.

FitCo在兩年前進行了一次定價活動，先前的管理層選擇集中化FitClass定價，并全面實行10％至20％的全面價格上調。

This blunt approach had not been successful. It had failed to take into account the price elasticity of demand of its customer set across the wide range of classes and locations, and the price increases actually resulted in overall revenue declines of 2% as the subsequent reduction in demand in many classes outstripped the increases in price. They were forced to unwind the price changes a couple months later.

這種直率的方法沒有成功。 它沒有考慮到不同類別和地區的客戶需求的價格彈性，價格上漲實際上導致總收入下降了2％，因為隨后許多類別的需求下降超過了漲幅價格。 幾個月后，他們被迫取消價格變動。

Though painful, that experience had at least been useful in giving FitCo a pretty solid dataset on price elasticity of its FitClass customer set. It could chart how class demand changed in response to price increases for each of three utilization bands — high (>85%), medium (50–85%) and low (<50%). Modeling out the various impacts of price increases on demand, FitCo estimated a revenue full potential of 15% from more efficient pricing.

盡管很痛苦，但這種經驗至少在為FitCo提供有關其FitClass客戶集的價格彈性的相當可靠的數據集方面很有用。 它可以繪制出三個使用頻段(高(> 85％)，中(50-85％)和低(<50％))中的每個使用頻段隨價格上漲而變化的圖表。 通過模擬價格上漲對需求的各種影響，FitCo估計，更有效的定價可帶來15％的全部收入潛力。

To capture that higher revenue potential, FitCo now only needed to be able to accurately predict demand for classes into the future — whether current or new — to accurately model price impacts on revenue. This would enable FitCo to determine whether and how much of a price increase each class could profitably sustain.

為了捕獲更高的收入潛力，FitCo現在僅需要能夠準確預測對未來類別的需求(無論是當前的還是新的)，以準確地模擬價格對收入的影響。 這將使FitCo能夠確定每個類別的價格上漲是否以及能夠維持多少利潤。

FitCo had initially attempted this using traditional rules-based methods — effectively a series of if/then statements to set bands based on certain conditions. With some extensive trial and error they had managed to write a function that was estimated to generate about 5% in additional revenue. This wasn’t bad, but this approach had two primary limitations: (1) it failed to fully account for interrelationship of the wide variety of factors relevant to each class — it incorrectly predicted too many classes in the wrong band, causing a decline in usage — and (2) it failed to generalize to new class schedules or details at any given location — it couldn’t adequately account for new combinations of factors or scenarios.

FitCo最初使用傳統的基于規則的方法進行了嘗試，實際上是通過一系列if / then語句來基于某些條件設置頻段。 經過大量的反復試驗，他們設法編寫了一個函數，該函數估計可產生約5％的額外收入。 這樣做還不錯，但是這種方法有兩個主要局限性：(1)它未能完全說明與每個班級相關的多種因素之間的相互關系–錯誤地預測了錯誤頻段中的過多班級，從而導致用法-(2)無法在任何給定位置推廣到新的課程表或詳細信息-無法充分考慮因素或場景的新組合。

Looking for alternative approaches, FitCo turned to ML.

在尋找替代方法時，FitCo轉向了ML。

機器學習解決方案 (ML solution)

ML is well-suited to these types of classification problems precisely because of its ability to process a wide range of factors and generalize to unseen or new situations.

ML非常適合這些類型的分類問題，這是因為ML能夠處理各種因素并將其推廣到看不見的情況或新情況。

However, like most organizations of its size and in its industry, FitCo did not have ML capabilities or a team of data scientists on hand to design, build and deploy an ML solution. This had previously been a major barrier to ML adoption. Today though, the availability of ML managed services have largely democratized access to ML capabilities.

但是，像大多數同等規模和其行業的組織一樣，FitCo沒有ML功能，也沒有一組數據科學家來設計，構建和部署ML解決方案。 以前，這是采用機器學習的主要障礙。 但是，如今，ML管理服務的可用性已大大普及了ML功能的訪問。

For their solution, FitCo chose Amazon SageMaker, which featured among other things, an AutoML capability called AutoPilot that could take a simple tabular dataset and automate the process of building an ML workload around it.

對于他們的解決方案，FitCo選擇了Amazon SageMaker，該產品除其他功能外還具有一項稱為AutoPilot的AutoML功能，該功能可以獲取簡單的表格數據集并自動圍繞其構建ML工作負載的過程。

AutoPilot takes a simple tabular dataset and builds an ML workload around itAutoPilot采用簡單的表格數據集并圍繞其構建ML工作負載

With AutoPilot, FitCo no longer needed a team of data scientists to get the benefit of ML. Instead, they were able to drive this initiative with a three-person project team consisting of the CFO as business owner, CTO as technology owner, and a single back-end developer responsible for building and integrating the solution.

有了AutoPilot ，FitCo不再需要一個數據科學家團隊來獲得ML的好處。 取而代之的是，他們能夠由三人組成的項目團隊來推動這一計劃，該團隊由CFO(業務所有者)，CTO(技術所有者)以及負責構建和集成解決方案的單個后端開發人員組成。

訓練數據 (Training data)

To build their training dataset, FitCo gathered historical utilization data for each of their classes over the past two years.

為了建立他們的訓練數據集，FitCo收集了過去兩年中每個班級的歷史利用率數據。

Utilization for each class was expressed as a percentage of total places filled. FitCo converted the data in this column into ‘high’ , ‘medium’ and ‘low’ based on the utilization bands above and named this column ‘target’. This would be the column to be predicted by the ML model.

每個班級的利用率表示為所占總席位的百分比。 FitCo根據上面的使用范圍將此列中的數據轉換為“高”，“中”和“低”，并將此列命名為“目標”。 這將是ML模型要預測的列。

They then combined this data with a set of internal features they thought likely to indicate utilization. They also added a range of external data that they expected to be relevant. The result was a dataset with 800K instances and which contained the following features:

然后，他們將此數據與他們認為可能表明利用率的一組內部功能結合在一起。 他們還添加了他們期望相關的一系列外部數據。 結果是一個包含800K實例的數據集，其中包含以下功能：

• type of class (categorical)
  類的類型(分類)
• location (categorical)
  位置(類別)
• day of week (categorical)
  星期幾(分類)
• time of day (numerical)
  一天中的時間(數字)
• instructor (categorical)
  指導老師(分類)
• studio brand (categorical)
  工作室品牌(分類)
• is a public holiday (binary)
  是一個公共假期(二進制)
• is a school holiday (binary)
  是學校假期(二進制)
• external temperature (numerical)
  外部溫度(數值)
• target (categorical)
  目標(絕對)

FitCo did some basic feature engineering to better organize and format this data set, converted it to csv format and saved in an S3 bucket. They now had a dataset with which they could train their ML model.

FitCo進行了一些基本的功能工程設計，以更好地組織和格式化此數據集，將其轉換為csv格式并保存在S3存儲桶中。 他們現在有了一個可以訓練其ML模型的數據集。

亞馬遜SageMaker AutoPilot (Amazon SageMaker AutoPilot)

FitCo chose Autopilot for its ability to simplify and streamline the core components of the machine learning process. AutoPilot automates the process of exploring data, engineering features, testing different algorithms, and selecting the best model. All it requires is that you provide a tabular dataset.

FitCo之所以選擇自動駕駛儀，是因為它能夠簡化和簡化機器學習過程的核心組件。 AutoPilot自動執行探索數據，工程特征，測試不同算法以及選擇最佳模型的過程。 它所需要的只是提供表格數據集。

In addition, it automatically surfaces the code base it used, adding visibility and reproducibility into the process. This was an important differentiator for FitCo’s CTO because it gave FitCo the opportunity to explore, and learn from, the steps that had been taken to generate the model, as well as giving it a code base that it could modify and optimize it into the future.

此外，它會自動顯示所使用的代碼庫，從而在過程中增加可見性和可重復性。 這是FitCo CTO的重要區別因素，因為它使FitCo有機會探索和學習生成模型所采取的步驟，并為其提供了可以在未來進行修改和優化的代碼庫。 。

To start the AutoPilot process, FitCo used the no-code interface available within Amazon SageMaker Studio. This required three key steps:

為了啟動AutoPilot流程，FitCo使用了Amazon SageMaker Studio中可用的無代碼界面。 這需要三個關鍵步驟：

Name their experiment

為實驗命名

Point SageMaker to the s3 bucket where their training file was located, and

將SageMaker指向其訓練文件所在的s3存儲桶，然后

Define the variable to be predicted

定義要預測的變量

AutoPilot’s no-code interface puts ML within reach of any organizationAutoPilot的無代碼界面使ML不受任何組織的影響

Once these details were entered, they simply hit Create Experiment and FitCo’s ML model build was underway, running a range of trials to determine the best performing ML approach.

輸入這些詳細信息后，他們只需點擊“創建實驗”，FitCo的ML模型構建便開始了，并進行了一系列試驗，以確定性能最佳的ML方法。

Complete list of trials run by AutoPilot to determine best performing model由AutoPilot運行以確定最佳性能模型的試驗的完整列表

This process took about an hour to complete. Once it had concluded, FitCo could then simply sort the list of trials to find the best performing model. They were able to generate the notebook that contained the code of this model, and they were also able to deploy the model to a SageMaker endpoint that enabled them to further test the inferences (predictions) generated by the model on new data, or even put it into production.

此過程耗時約一個小時。 一旦得出結論，FitCo就可以簡單地對試驗清單進行排序，以找到性能最佳的模型。 他們能夠生成包含該模型代碼的筆記本，還能夠將模型部署到SageMaker端點，這使他們能夠進一步測試該模型對新數據生成的推論(預測)，甚至可以將其放入它進入生產。

結果 (Results)

When modeled against FitCo’s test dataset, the ML model outperformed their rules-based approach by 2x, increasing revenue by an estimated 11% overall.

如果以FitCo的測試數據集為模型進行建模，則ML模型的性能要比其基于規則的方法高出2倍，從而使整體收入增長約11％。

The ML model outperformed rules-based approaches by 2xML模型的效果比基于規則的方法高2倍

This performance improvement stemmed primarily from the higher precision of the ML predictions vs the rules-based approach. In a multi-class classification problem, the challenge is not only to predict the right class, but to minimize the cost of inaccuracies. For example, it is less costly to inaccurately predict a low demand class as ‘medium’ than as ‘high’. Specifically with FitCo’s price elasticity profile, the cost of that particular error was approximately 4x greater in the form of lower demand.

相對于基于規則的方法，這種性能改進主要來自ML預測的更高精確度。 在多類別分類問題中，挑戰不僅在于預測正確的類別，而且在于最大程度地減少不準確性的成本。 例如，準確地將低需求類別預測為“中”比“高”類別的成本更低。 特別是在FitCo的價格彈性曲線中，以較低的需求形式出現的特定錯誤的成本大約高出4倍。

A comparison of this performance between traditional and ML approaches can be seen below, and illustrates that the traditional approach actually outperformed ML in accurately predicting high demand classes. The issue is that it was unable to do so while also accurately predicting medium and low demand classes. Moreover, it made costly errors in inaccurately predicting low classes as high demand classes. The ML model was better able to more holistically map the shape of the data to account for both situations.

傳統方法和機器學習方法之間的性能比較如下所示，它說明了傳統方法在準確預測高需求類別方面實際上勝過機器學習。 問題在于，它無法做到這一點，同時也無法準確預測中，低需求類別。 此外，它在錯誤地將低等級預測為高需求等級時造成了代價高昂的錯誤。 ML模型能夠更好地從整體上映射數據的形狀以解決這兩種情況。

Rules-based approaches incorrectly predicted low classes as high in 25% of cases基于規則的方法在25％的案例中錯誤地預測了低等級

These errors came at a considerable cost to revenue for the rules-based approach that the ML model could avoid. For example, inaccurately predicting a low demand class as high demand resulted in a demand decline of nearly 50%, far offsetting the 30% higher price paid by the remaining members.

對于ML模型可以避免的基于規則的方法，這些錯誤要付出可觀的收入成本。 例如，由于高需求導致需求下降了近50％，因此不準確地預測了低需求類別，從而遠遠抵消了其余成員支付的高出30％的價格。

The matrix below shows the revenue growth impact of each type of prediction, expressed as the difference in performance between traditional and ML methods.

下面的矩陣顯示了每種預測類型對收入增長的影響，表示為傳統方法與機器學習方法之間的性能差異。

ML model was able to generate 5.5% higher revenue by more consistent accuracy overall機器學習模型通過更一致的整體準確性能夠產生5.5％的收入

Though traditional methods managed to beat the ML model in correctly labeling a higher proportion of high classes as high (a metric known as ‘recall’), it did so by also inaccurately labeling many more ‘medium’ and ‘low’ demand classes as high too (known as ‘precision’).

盡管傳統方法在正確地將較高比例的高等級標記為高(一種稱為“召回”的指標)上成功擊敗了ML模型，但這樣做的方法是不正確地將更多“中”和“低”需求等級標記為高也稱為“精度”。

As a result, although its accurate high predictions generated 1.4% higher revenue, that came at a cost of losing 2.8% of revenue from the demand declines of incorrectly charging higher prices to higher elasticity medium and low classes. A similar pattern emerged in low predictions; the ML model’s greater accuracy meant that it only reduced the price of classes for which one would expect to see higher demand.

結果，盡管其準確的高預測產生了1.4％的收入增長，但其結果是由于需求下降而導致收入損失了2.8％，這是由于不正確地向較高彈性的中，低階層收取了較高的價格。 低預測中也出現了類似的模式。 ML模型的準確性更高，這意味著它只降低了人們期望獲得更高需求的類的價格。

As a result of this higher precision —the more accurate prediction of both high and low classes — the ML model was able to generate 5.5% higher revenue overall, more than double that of rules-based approaches.

由于具有更高的精確度(對高低階層的預測都更加準確)，因此ML模型總體上可產生5.5％的收入，是基于規則的方法的兩倍多。

結論 (Conclusion)

Pricing optimization is a powerful lever for revenue growth, and the application of ML provides a powerful solution, which can often outperform traditional approaches.

定價優化是收入增長的強大杠桿，而ML的應用提供了強大的解決方案，通?？梢詣龠^傳統方法。

In FitCo’s case, the application of ML to their challenge generated a 2x uplift in revenue growth vs their best performing rules-based alternative, and produced an estimated 11% uplift in revenue.

在FitCo的案例中，將ML應用于他們的挑戰使收入增長增長了2倍，而其表現最佳的基于規則的替代方法則使收入增長了11％。

FitCo’s example helps demonstrate both how ML can be applied to optimize pricing, as well as the way managed services like SageMaker AutoPilot are able to put these powerful ML solutions within reach of virtually any organization.

FitCo的示例有助于說明如何應用ML來優化定價，以及諸如SageMaker AutoPilot之類的托管服務將這些強大的ML解決方案置于幾乎任何組織都可以使用的方式。

Have an AI opportunity you’d like to explore? Get in touch with me on LinkedIn.

您有想探索的AI機會嗎？ 在LinkedIn上與我聯系。

翻譯自: https://towardsdatascience.com/ai-for-revenue-growth-using-ml-to-drive-more-valuable-pricing-89e8c790f795

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