{"id":35574,"date":"2018-11-13T19:24:44","date_gmt":"2018-11-14T00:24:44","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/using-deep-learning-to-predict-customer-lifetime-value-in-online-fashion-retail\/"},"modified":"2019-01-12T09:35:17","modified_gmt":"2019-01-12T14:35:17","slug":"fashion-forward-using-deep-learning-at-asos-to-predict-customer-lifetime-value-in-online-fashion-retail","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/fashion-forward-using-deep-learning-at-asos-to-predict-customer-lifetime-value-in-online-fashion-retail\/","title":{"rendered":"Fashion Forward \u2013 using deep learning at ASOS to predict customer lifetime value in online fashion retail"},"content":{"rendered":"

I explore the impact that deep learning neural networks have had on the process of forecasting customer lifetime value (CLV) in online fashion retail (retail) using current applications at the online fashion retailer ASOS[1]<\/a>. CLV is a key marketing metric that assigns future profitability to a customer based on three inputs: marginal value, churn rate, and acquisition cost[2]<\/a>.<\/p>\n

In OFR, correctly predicting CLVs is a competitive advantage because of the significant negative impact that unprofitable customers have on a business\u2019 bottom line. There are two ways in which deep learning is improving profitability at OFRs. First, deep learning reduces the quantities of orders from negative value customers by reducing marketing dollars targeted at this customer segment, which increases marginal value. Second, deep learning reduces the churn rate for customers, which increases their average lifetime with the retailer.<\/p>\n

The business of online fashion retail is predicated on customers being able to try on clothes at home and return the items that they do not want[3]<\/a>. For example, in an interview with one ASOS customer, the customer indicated that he had purchased eleven pairs of jeans so that he could find a perfectly fitting pair. However, out of the eleven items, this customer returned eight and kept only three. While this might seem shocking, this is not an atypical purchasing pattern at ASOS. This problem can be exacerbated by free shipping thresholds, which encourage some customers to purchase up to that spend level. Then subsequently return the filler items having received the free shipment discount.<\/p>\n

\"\"<\/a>
The cost of returns<\/figcaption><\/figure>\n

These kinds of customers tend to be negative value customers for ASOS. Customers with negative values are the bane of OFRs because the costs free shipping and returns are borne by the retailer. According to CBInsights, \u201cbeyond lost revenue, returns are expensive to process. Each time an item is returned, retailers often pay for its return-shipping, re-sorting, and re-shelving\u201d[4]<\/a>,[5]<\/a><\/sup>.<\/p>\n

At ASOS, deep learning neural networks can identify the behaviors of high value customers, who tend to shop for undiscounted items, in-season items, and more unique pieces. In addition to the items that they look at high-value customers tend to look at different products at different times in a different sequence than low-value customers. Deep learning is then able to assign an intention to purchase and the value of that intention to a given customer given their behavior on the website[6]<\/a>. This means that ASOS can offer unique promotions to a high-value customer to encourage a purchase.<\/p>\n

\"\"<\/a>
High-level overview of the CLTV system<\/figcaption><\/figure>\n

In addition to predicting intention to purchase and value of the purchase for a given customer deep learning neural networks are also used to predict the likelihood of a customer churning. Prior to application of machine learning, it was challenging to identify customers at-risk of ending their relationship with the OFR prior to them ending the relationship. Now with the implementation of deep learning, a retailer like ASOS can implement retention strategies[7]<\/a> for high value customers who might be considering taking their business elsewhere. Anecdotally, this is incredibly powerfully because it is generally accepted that it is harder to win back an old customer than to maintain a current customer.<\/p>\n

\"\"<\/a>
High value versus low value customer behavior<\/figcaption><\/figure>\n

However, going forward, ASOS should consider actively divesting unprofitable customers similar to Net-a-Porter, which \u201cretained the right to refuse returns or close accounts if the level of returns was deemed excessive\u201d [8]<\/a>. This is important because unprofitable customers redirect valuable resources that should be dedicated towards profitable customers. Should ASOS fire its unprofitable customers and if it does \u2013 how should it execute this strategy? More importantly, how should ASOS integrate customer style and size recommendations? Could there be privacy and security concerns and how should the business think about mitigating these concerns?<\/p>\n

\"\"<\/a>
How to politely fire your customer<\/figcaption><\/figure>\n

[Word count = 787]<\/em><\/p>\n

Endnotes<\/span><\/p>\n

[1]<\/a> ASOS Plc, together with its subsidiaries, operates as an online fashion retailer in the United Kingdom, the United States, Australia, France, Germany, Spain, Italy, Sweden, the Netherlands, and Russia. The company offers womenswear, menswear, and sportswear products. It sells approximately 85,000 branded and ASOS brand products primarily through its website, asos.com, as well as through social media platforms and magazines. The company is also involved in marketing staff employment and payment processing businesses. ASOS Plc was founded in 2000 and is headquartered in London, the United Kingdom (CapitalIQ).<\/p>\n

[2]<\/a> (Steenburgh & Avery, 2017)<\/p>\n

[3]<\/a> Returns cost retailers $260 billion in 2015, according to the National Retail Federation. And about 30 percent of items bought online end up being returned, versus 9 percent of items bought in stores\u00a0(Quenqua, 2017).<\/p>\n

[4]<\/a> (CBInsights, 2018)<\/p>\n

[5]<\/a> (Dennis, 2018)<\/p>\n

[6]<\/a> (Chamberlain, Cardoso, Liu, Pagliari, & Deisenroth, 2017)<\/p>\n

[7]<\/a> (Fridrich, 2017)<\/p>\n

[8]<\/a> (Wells & Ellsworth, 2018)<\/p>\n

References<\/span><\/p>\n

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Andersson, T. (2017, July 21). Deep Learning & Fashion Imagery<\/em>. Retrieved from LinkedIn: https:\/\/www.linkedin.com\/pulse\/deep-learning-fashion-imagery-thomas-andersson\/<\/p>\n

Andrew, N. (2017, July 18). A simple way to understand machine learning vs deep learning<\/em>. Retrieved from Zendesk: https:\/\/www.zendesk.com\/blog\/machine-learning-and-deep-learning\/<\/p>\n

ASOS Plc. (2018, October 17). 2018 Earnings Call.<\/em> Retrieved from CapitalIQ: https:\/\/www-capitaliq-com.ezp-prod1.hul.harvard.edu\/CIQDotNet\/Transcripts\/Summary.aspx?CompanyId=9237218<\/p>\n

ASOS Plc. (2018). Annual Report.<\/em><\/p>\n

Berzgal, G. (n.d.). Returns in online retail: How to use it as a differentiator in your offering<\/em>. Retrieved from IMRG (UK’s online retail association): https:\/\/www.imrg.org\/blog\/returns-in-online-retail-differentiator-in-offering\/<\/p>\n

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CBInsights. (2018, November 8). 5 Startups Solving One Of Retail\u2019s Major Pain Points: Returns<\/em>. Retrieved from CBInsights: https:\/\/www.cbinsights.com\/research\/startup-retailer-returns-expert-intelligence\/<\/p>\n

CBInsights. (2018, April 3). Retail\u2019s Adapt-Or-Die Moment: How Artificial Intelligence Is Reshaping Commerce<\/em>. Retrieved from CBInsights: https:\/\/www.cbinsights.com\/research\/artificial-intelligence-reshaping-commerce\/<\/p>\n

Chamberlain, B. (2017). Deep Learning in Retail & Advertising Summit, London. (A. Cuthbertson, Interviewer)<\/p>\n

Chamberlain, B. P., Cardoso, A., Liu, B. C., Pagliari, R., & Deisenroth, M. P. (2017). Customer Lifetime Value Prediction Using Embeddings. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining<\/em>, 1753-1762.<\/p>\n

Charmberlain, B. (2017, November 3). Ben Chamberlain, #ASOS- Using deep learning to estimate CLTV in e-commerce #reworkretail.<\/em> Retrieved from YouTube: https:\/\/www.youtube.com\/watch?v=UIuiUec6rNI<\/p>\n

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Dennis, S. (2018, February 14). The Ticking Time Bomb Of E-commerce Returns<\/em>. Retrieved from Forbes: https:\/\/www.forbes.com\/sites\/stevendennis\/2018\/02\/14\/the-ticking-time-bomb-of-e-commerce-returns\/#3b33f7194c7f<\/p>\n

Fedyk, A. (2016). How to tell if machine learning can solve your problem.<\/em> Boston: Harvard Business Review.<\/p>\n

Fridrich, M. (2017). Hyperparameter optimization of artificial neural network in customer churn prediction using genetic algorithm. Faculty of Business Administration, Brno University of Technology<\/em>, 9-21.<\/p>\n

Grossfeld, B. (2017, July 18). A simple way to understand machine learning vs deep learning<\/em>. Retrieved from Zendesk: https:\/\/www.zendesk.com\/blog\/machine-learning-and-deep-learning\/<\/p>\n

Kozlowska, H. (2018, August 13). Shoppers are buying clothes just for the Instagram pic, and then returning them<\/em>. Retrieved from Quartzy: https:\/\/qz.com\/quartzy\/1354651\/shoppers-are-buying-clothes-just-for-the-instagram-pic-and-then-return-them\/<\/p>\n

Mitta, V., Sarkees, M., & Murshed, F. (2008, April). The right way to manage unprofitable customers.<\/em> Retrieved from Harvard Business Review: https:\/\/hbr.org\/2008\/04\/the-right-way-to-manage-unprofitable-customers<\/p>\n

Quenqua, D. (2017, December 26). Many Unhappy Returns? Online Holiday Shopping\u2019s Big Hangover<\/em>. Retrieved from New York Times: https:\/\/www.nytimes.com\/2017\/12\/26\/style\/online-holiday-shopping-returns.html<\/p>\n

Ram, A. (2016, January 27). UK retailers count the cost of returns<\/em>. Retrieved from Financial Times: https:\/\/www.ft.com\/content\/52d26de8-c0e6-11e5-846f-79b0e3d20eaf<\/p>\n

Segura, A. (2018, April 30). Artificial Intelligence in Fashion Retail<\/em>. Retrieved from The Fashion Retailer: https:\/\/fashionretail.blog\/2018\/04\/30\/artificial-intelligence-in-fashion\/<\/p>\n

Siff, L. (2012, May 21). Your Unprofitable Customers Are Killing You<\/em>. Retrieved from Forbes: https:\/\/www.forbes.com\/sites\/lawrencesiff\/2012\/05\/21\/your-unprofitable-customers-are-killing-you\/#6d3a447b388d<\/p>\n

Skinner, R. (2018, February 8). ASOS Uses Machine Learning To Understand Customer Value<\/em>. Retrieved from Forrester: https:\/\/go.forrester.com\/blogs\/machine-learning-asos-customer-value\/<\/p>\n

Softweb Solutions. (2018, January 8). How deep learning and AI radically change the way retailers interact with customers<\/em>. Retrieved from Softweb Solutions: https:\/\/www.softwebsolutions.com\/resources\/deep-learning-and-AI-for-retailers.html<\/p>\n

Steenburgh, T., & Avery, J. (2017). Marketing Analysis Toolkit: Customer Lifetime Value Analysis.<\/em> Boston: Harvard Business School Publishing.<\/p>\n

Wells, J. R., & Ellsworth, G. (2018). ASOS Plc.<\/em> Boston: Harvard Business Review.<\/p>\n","protected":false},"excerpt":{"rendered":"

ASOS is using deep learning neural networks to predict customer lifetime value based on online shopping behavior.<\/p>\n","protected":false},"author":11349,"featured_media":35886,"comment_status":"open","ping_status":"closed","template":"","categories":[462,5036,5038,5037,5039,2033],"class_list":["post-35574","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-asos","category-customer-lifetime-value","category-deep-learning-neural-networks","category-online-fashion-retail","category-online-returns","category-online-shopping","hck-taxonomy-organization-asos","hck-taxonomy-industry-fashion","hck-taxonomy-country-united-kingdom"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nFashion Forward \u2013 using deep learning at ASOS to predict customer lifetime value in online fashion retail - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/fashion-forward-using-deep-learning-at-asos-to-predict-customer-lifetime-value-in-online-fashion-retail\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fashion Forward \u2013 using deep learning at ASOS to predict customer lifetime value in online fashion retail - 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