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Product2Vec

通過(guò)機(jī)器學(xué)習(xí)理解產(chǎn)品競(jìng)爭(zhēng)

主講人

陳芳琳

講座時(shí)間

2023年4月21日（周五）10:00

講座方式

Zoom：968 1693 1357

主講人介紹

陳芳琳于紐約大學(xué)獲得市場(chǎng)營(yíng)銷學(xué)位博士和碩士學(xué)位，于清華大學(xué)獲得經(jīng)濟(jì)學(xué)和數(shù)學(xué)學(xué)士學(xué)位，現(xiàn)為邁阿密大學(xué)市場(chǎng)營(yíng)銷系助理教授。她的研究聚焦于市場(chǎng)營(yíng)銷和機(jī)器學(xué)習(xí)的交叉領(lǐng)域，例如產(chǎn)品競(jìng)爭(zhēng)和消費(fèi)者定位。在她的一組研究中，陳芳琳博士專注于從產(chǎn)品共同購(gòu)買模式中發(fā)掘信息，以理解產(chǎn)品關(guān)系和市場(chǎng)結(jié)構(gòu)。她的另一組研究關(guān)注的是如何通過(guò)機(jī)器學(xué)習(xí)優(yōu)化鎖定目標(biāo)顧客的順序，從而提高客戶留存率。此外，她還對(duì)媒體消費(fèi)感興趣，包括傳統(tǒng)媒體和數(shù)字媒體。

Fanglin Chen is an Assistant Professor of Marketing at the University of Miami. Her main research interests center around the intersection of marketing and machine learning, specifically in the areas of product competition and consumer targeting. In one research stream, she focuses on extracting information from product co-purchase patterns to understand product relationships and market structure. The other research stream focuses on developing sequential targeting strategies to boost customer retention via machine learning. She is also interested in studying media consumption, including both traditional media and digital media. She received her PhD and MS in Marketing from New York University and BS in Economics and Mathematics from Tsinghua University.

講座內(nèi)容

從產(chǎn)品層面而非品牌層面出發(fā)去研究競(jìng)爭(zhēng)和市場(chǎng)結(jié)構(gòu)可以幫助企業(yè)更好的理解同類相食和產(chǎn)品線優(yōu)化等問(wèn)題。本研究引入的基于表征學(xué)習(xí)的Product2Vec方法適用于研究產(chǎn)品數(shù)量較大時(shí)的產(chǎn)品級(jí)競(jìng)爭(zhēng)。該模型以購(gòu)物籃為輸入，為每個(gè)產(chǎn)品生成一個(gè)保留了重要產(chǎn)品信息的低維向量?；趯?duì)這些產(chǎn)品向量的分析，本研究得到了如下發(fā)現(xiàn)。首先，本研究證明這些向量可以反映任意一對(duì)產(chǎn)品之間的類比關(guān)系。其次，本研究創(chuàng)建的兩個(gè)指標(biāo)，互補(bǔ)性和互換性，能夠準(zhǔn)確的衡量任意一對(duì)產(chǎn)品之間的互補(bǔ)/替代關(guān)系。第三，本研究還通過(guò)將價(jià)格因素排除在產(chǎn)品向量之外調(diào)整了表征學(xué)習(xí)算法，從而進(jìn)行價(jià)格彈性的估計(jì)并研究產(chǎn)品層面的競(jìng)爭(zhēng)。研究表明，與普通的選擇模型相比，本方法可以更快更準(zhǔn)確的生成需求預(yù)測(cè)和價(jià)格彈性。第四，本研究提出了Product2Vec在營(yíng)銷實(shí)踐上的兩個(gè)應(yīng)用：1）分析品牌內(nèi)部和品牌間競(jìng)爭(zhēng)；2）分析市場(chǎng)結(jié)構(gòu)。總的來(lái)說(shuō)，本研究展示了機(jī)器學(xué)習(xí)算法（例如：表征學(xué)習(xí)）在增強(qiáng)和改進(jìn)傳統(tǒng)營(yíng)銷方法方面的價(jià)值。

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. We introduce Product2Vec, a method based on representation learning, to study product-level competition when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional vector that preserves important product information. Using these product vectors, we present several findings. First, we show that these vectors can recover analogies between product pairs. Second, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Third, we combine these vectors with traditional choice models to study product-level competition. To accurately estimate price elasticities, we modify the representation learning algorithm to remove the influence of price from the product vectors. We show that, compared with state-of-the-art choice models, our approach is faster and can produce more accurate demand forecasts and price elasticities. Fourth, we present two applications of Product2Vec to marketing problems: 1) analyzing intra- and inter-brand competition and 2) analyzing market structure. Overall, our results demonstrate that machine learning algorithms, such as representation learning, can be useful tools to augment and improve traditional marketing methods.