Working on Intuit’s Forecasting and Optimization team in Marketing Analytics, I’m well versed on forecasting and predictive modeling. Today, I’ll explain what that they are and how we use them to delight our customers. I’ll also discuss the role artificial intelligence (AI) and machine learning (ML) play in developing the models.
我在 Intuit 行銷分析預測和優化團隊工作，精通預測和預測建模。今天，我將解釋它們是什麼以及我們如何使用它們來取悅我們的客戶。我還將討論人工智慧 (AI) 和機器學習 (ML) 在開發模型中發揮的作用。
Predictive modeling vs. forecasting
預測建模與預測
At its core, predictive modeling is about building a mathematical abstraction or representation of reality, a model, that has inputs (something you know) and outputs (something you want to know).
預測建模的核心是建立現實的數學抽像或表示，即模型，該模型具有輸入（您知道的東西）和輸出（您想知道的東西）。
Predictive modeling is only possible through an abundance of data. Our team relies on data flowing from a wide variety of internal and external partners to establish a cohesive view of the nebulous world of inputs and outputs involved in media spend and fiscal year planning. On the input side, we work with numerous data scientists on the marketing analytics team across the web and online acquisition groups, strategic partners in marketing, and external advertising and agency partners. On the output side, we work with members of finance and marketing to help optimize their spending decisions.
預測建模只有透過大量資料才能實現。我們的團隊依靠來自各種內部和外部合作夥伴的資料流，對媒體支出和財政年度規劃所涉及的輸入和輸出的模糊世界建立一個統一的視圖。在輸入方面，我們與網路和線上收購團隊的行銷分析團隊中的眾多資料科學家、行銷策略合作夥伴以及外部廣告和代理商合作夥伴合作。在產出方面，我們與財務和行銷人員合作，幫助優化他們的支出決策。
To do our job, we use AI and ML, which are broad terms that cover a large selection of tools and applications we use for predictive modeling. Traditionally, AI is the ability of a machine to perform a task in a smart way. ML is a subset of AI algorithms that leverage data for problems such as classification, clustering, inference, and prediction. Let’s consider an analogy to explain how we use AI and ML.
為了完成我們的工作，我們使用人工智慧和機器學習，這些術語很廣泛，涵蓋了我們用於預測建模的大量工具和應用程式。傳統上，人工智慧是機器以智慧方式執行任務的能力。 ML 是 AI 演算法的子集，它利用資料來解決分類、聚類、推理和預測等問題。讓我們用一個類比來解釋我們如何使用人工智慧和機器學習。
We want to take a trip to the beach, and want an AI to solve this task: Get us to the beach. The beach is our model output — we arrive at or near the desired beach location. There are a number of vehicles – predictive models – we could use to get to the beach. We want to choose our vehicle based on the design criteria for your trip. How many friends are we taking, what supplies are we taking, and how fast do we want to go? In the modeling world, we may be more concerned with how accurately we arrive at the beach (predictive accuracy), than we are with the individual roads or understanding the inner workings of the car engine we took to get there (model inference).
我們想去海灘旅行，並希望人工智慧來解決這個任務：帶我們去海灘。海灘是我們的模型輸出——我們到達或接近所需的海灘位置。我們可以使用多種車輛（預測模型）前往海灘。我們希望根據您的旅行的設計標準來選擇我們的車輛。我們要帶多少朋友，要帶什麼物資，要走多快？在建模世界中，我們可能更關心到達海灘的準確度（預測準確性），而不是單獨的道路或了解我們到達那裡的汽車引擎的內部工作原理（模型推理）。
We have a bunch of options for our vehicles. We might first want to borrow our friend’s new Tesla (for example, deep learning and neural networks) that has lots of shiny features. We might get wonderful predictive accuracy and arrive exactly at the beach, but our understanding of the electric engine might be obscured compared to an internal combustion engine. Or, perhaps the self driving feature led us to not recall which roads connected to get us to the beach. In other words, we might not understand how we got there or how the features contributed to our end result.
我們的車輛有多種選擇。我們可能首先想藉用朋友的新特斯拉（例如深度學習和神經網路），它有很多閃亮的功能。我們可能會獲得出色的預測準確性並準確到達海灘，但與內燃機相比，我們對電動引擎的理解可能會模糊。或者，也許自動駕駛功能導致我們不記得哪些道路連接到海灘。換句話說，我們可能不明白我們是如何到達那裡的，或者這些功能如何對我們的最終結果做出貢獻。
Instead, we could take our sibling’s Subaru WRX (ML, random forest). Or, we could take our parent’s classic 1969 VW Bus (regression). With these latter options, we may have more power to understand how the individual pieces of our trip contributed to the end result (model inference) at perhaps a loss of predictive accuracy compared to the Tesla. Finally, all the cars require some kind of fuel (clean data to run well). Without a clean fuel supply, you won’t be accomplishing your task.
相反，我們可以選擇我們兄弟的斯巴魯 WRX（ML，隨機森林）。或者，我們可以乘坐父母的經典 1969 年大眾巴士（回歸）。有了後面這些選項，我們可能有更多的能力來理解我們旅行的各個部分如何對最終結果（模型推理）做出貢獻，但與特斯拉相比，可能會失去預測準確性。最後，所有的汽車都需要某種燃料（乾淨的數據才能運作良好）。如果沒有清潔的燃料供應，您將無法完成任務。
Your choice of car for your trip can radically change what supplies you can take, how far you’ll get on your fuel, how fast you’ll go, and other factors. Hence, your choice of AI/ML implementation is intrinsic to your predictive modeling problem, assumptions, and business use cases.
您為旅行選擇的汽車可以從根本上改變您可以攜帶的補給品、您的燃料可以行駛多遠、您可以行駛多快以及其他因素。因此，您對 AI/ML 實施的選擇取決於您的預測建模問題、假設和業務用例。
Forecasting is a very specific type of predictive modeling. Predictive modeling is conceptually challenging, since there are one-to-many inputs, and one or more outputs. With forecasting, you’ve added an additional layer of complexity, in the dimension of time, which complicates data collection and adds model complexities like autocorrelation – data is correlated to itself through time.
預測是一種非常特殊的預測建模類型。預測建模在概念上具有挑戰性，因為存在一對多輸入和一個或多個輸出。透過預測，您在時間維度上增加了額外的複雜性，這使得資料收集變得複雜，並增加了模型的複雜性，例如自相關性——資料隨時間與自身相關。
Frequently, you want to predict numerous timepoints into the future, which means estimation of uncertainty around the components of your time series, such as seasonality (morning activity vs. night activity, peak season vs. non-peak season, weekdays vs. weekends) and trends (price changes and worldwide pandemics).
通常，您希望預測未來的多個時間點，這意味著估計時間序列組成部分的不確定性，例如季節性（早晨活動與夜間活動、旺季與非旺季、工作日與週末）和趨勢（價格變化和全球流行病）。
Responsible predictive modeling and forecasting
負責任的預測建模和預測
Even as we help our customers – and ourselves – with fiscal decisions, we are fully aware that forecasting is a complex balance of artistry, science, and intuition. It’s important to understand how to generate a prediction and determine what inputs are needed, and it is equally important to sufficiently assess the uncertainty around the prediction and evaluate the results within that context. In essence, viewing predictions as a range of numbers as opposed to a single number.
即使我們幫助客戶和我們自己做出財務決策，我們也充分意識到預測是藝術、科學和直覺的複雜平衡。了解如何產生預測並確定需要哪些輸入非常重要，充分評估預測的不確定性並在該背景下評估結果也同樣重要。本質上，將預測視為一系列數字而不是單一數字。
If a business is wrong about their
forecast and subsequent fiscal year planning efforts, it can significantly
impact their return on investment in marketing and operational costs, which
presents challenges and difficulties for shareholders and employees. If
forecasting work is oversimplified or the uncertainty is insufficiently
understood, then the data can be incorrectly actioned upon.
如果企業的預測和隨後的財政年度規劃工作有誤，可能會嚴重影響其行銷和營運成本的投資回報，這給股東和員工帶來挑戰和困難。如果預測工作過於簡單化或對不確定性的理解不夠充分，則可能會錯誤地根據資料採取行動。
The data scientist needs to understand the power and limitations of their models, and effectively communicate the good and bad of the outputs to leaders to drive the best decisions for the company. Responsible use of models means reporting what went wrong as much as what went right.
資料科學家需要了解其模型的威力和局限性，並有效地將輸出的好壞傳達給領導者，以推動公司做出最佳決策。負責任地使用模型意味著報告錯誤的地方和正確的地方一樣多。
The benefits of predictive modeling and forecasting for marketing analytics
預測建模和預測對行銷分析的好處
Here at Intuit, the application of our
predictive modeling and forecasting through AI/ML to delight our customers is
shaped by our company values and philosophy. We have always been a
customer-obsessed company. That is, we steep ourselves in deep customer
empathy.
在 Intuit，我們透過 AI/ML 進行預測建模和預測來取悅我們的客戶，這是由我們公司的價值觀和理念所塑造的。我們一直是一家以客戶為中心的公司。也就是說，我們沉浸在深刻的客戶同理心中。
Our efforts are reflected in the connecting of customers to the right products through ML-driven personalization. It’s also reflected in the delivering of algorithmic-assisted insights for their unique business or personal financial situations, highlighting changes in their financial health, and enabling them to easily make sound fiscal decisions.
我們的努力體現在透過機器學習驅動的個人化將客戶與正確的產品聯繫起來。它也體現在為他們獨特的業務或個人財務狀況提供演算法輔助的見解，突顯他們的財務健康狀況的變化，並使他們能夠輕鬆做出合理的財務決策。
Personally, I’ve used Intuit’s Mint personal finance management service to maximize my financial health and well being. Mint suggested a new credit card based on my shopping habits. Although it had an annual fee, I enjoyed a far better return on my spending. This is exactly the type of experience I want from algorithms extrapolating on my data: Help me make better decisions based on my unique situation.
就我個人而言，我使用 Intuit 的 Mint 個人財務管理服務來最大限度地提高我的財務健康和福祉。 Mint 根據我的購物習慣推薦了一張新信用卡。雖然它有年費，但我的支出得到了更好的回報。這正是我想要從演算法對我的數據進行推斷的體驗類型：幫助我根據我的獨特情況做出更好的決策。
Choosing an ideal credit card is a
perfect example of a classification, machine learning problem.
選擇理想的信用卡是分類機器學習問題的完美範例。
How Intuit envisions the future with AI and ML
Intuit 如何展望人工智慧和機器學習的未來
The future is always unknown, even as we
work hard to gather data and extrapolate outcomes through predictions,
forecasting, and marketing analytics via AI and ML technology. However, there
are those who have concerns about AI and ML taking jobs. If that’s you, rest
easy. Why? Because computers are just not that smart.
未來總是未知的，即使我們努力透過人工智慧和機器學習技術進行預測、預測和行銷分析來收集數據並推斷結果。然而，也有人擔心人工智慧和機器學習會搶走工作。如果那是你，請放心。為什麼？因為計算機沒那麼聰明。
For example, I think we can all agree
that smart bulbs and smart TVs aren’t doing much “thinking.”
例如，我想我們都同意智慧燈泡和智慧電視沒有做太多“思考”。
Computers (or algorithms driven by AI/ML)
are better than humans at solving very specific types of problems, but they’re
terrible at solving other more complex problems. These algorithms will
increasingly be used as tools to solve components of a complex problem,
handling the parts of a problem that we simply aren’t as good at solving. They
will help us classify and predict in meaningful ways. But, they won’t replace
our human intuition and implicit decisiveness (at least, not for a while).
電腦（或人工智慧/機器學習驅動的演算法）在解決非常特定類型的問題方面比人類更好，但在解決其他更複雜的問題方面卻很糟糕。這些演算法將越來越多地被用作解決複雜問題的工具，處理我們根本不擅長解決的問題的部分。它們將幫助我們以有意義的方式進行分類和預測。但是，它們不會取代我們人類的直覺和隱含的決斷力（至少暫時不會）。
This is why Intuit envisions a future that melds the best capabilities of machines and humans to deliver personalized customer experiences, all on one secure platform. And that future is AI-powered and expert-driven.
這就是為什麼 Intuit 設想未來融合機器和人類的最佳能力，在安全的平台上提供個人化的客戶體驗。這個未來是人工智慧驅動和專家驅動的。
To learn more about AI and the future, check out how artificial intelligence is redefining apps, and Intuit’s Shir Meir Lador’s lessons learned leading AI teams.
要了解有關人工智慧和未來的更多信息，請了解人工智慧如何重新定義應用程序，以及 Intuit 的 Shir Meir Lador 領導人工智慧團隊的經驗教訓。