Dressipi Whitepaper Beginner's Guide To Fashion Personalisation
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Beginner’s Guide to Fashion Personalisation Beginner’s Guide to Fashion Personalisation dressipi.com Contents 1. Introduction 2. Context 3. Personalisation 4. Fashion (Retail) Personalisation 5. How it Works? 6. Key Benefits 7. Getting Started 8. Summary 9. About Dressipi 2 Beginner’s Guide to Fashion Personalisation dressipi.com Introduction The Paradox of Choice by Barry Schwartz (2005)1 is a well-known book that details how the everyday decisions we make have become increasingly complex due to the overwhelming abundance of choice with which we are presented. Schwartz details the cost of having a choice overload and how curation can help. Although the book was published over 10 years ago when choice was a lot more manageable than it is now, the premise still stands – choice can often make us worse off. One major consequence of the growth of the Internet relates to ease of access when it comes to consumption. We are all drowning in a sea of options, leading to ineffective decision making. Of course, bad decisions come at a cost. When it comes to fashion, bad decisions can lead to a shared burden: retailers suffer when customers return garments or become frustrated with their shopping experiences, and the customer suffers as their needs are not adequately met. Companies are beginning to realise the impact on their bottom line (including hidden costs) from returns. Research carried out by Mintel2 suggests that 45% of online customers returned at least one item in 2017, representing a significant cost. This short paper seeks to outline how retail personalisation can help address some of the challenges arising from the paradox of choice we all face, leading to more satisfactory outcomes for both parties. Finally, we use the terms ‘retail personalisation’ and ‘fashion personalisation’ interchangeably throughout the text. Context Shopping online continues to grow (although the rate of growth is now slowing) with the likes of Amazon winning on convenience and price (two key elements of customer’s purchase behaviour). Where once the majority of browsing for clothes happened in stores, the growth of online shopping and increased time pressures mean that an increasing percentage of clothes are purchased online. Having said that, consumers still like to shop in stores – recent research by Vista Retail Support3 stated that 81% of UK consumers see the physical store as vital to the shopping experience. All this considered, the ability to shop using any device, whether you are in store, at home or at work is compelling. Shopping for fashion, however, is not without its challenges: • The choice can be overwhelming – where to start? • You want to be sure an item will look good on you and suit your preferences • When you’ve found the item/s you like, you want to know how you should wear it and how it will work with other items in your wardrobe and, if it is a new brand for you, what size to buy The net effect of these issues is that conversion rates are significantly lower and return rates significantly higher within the fashion vertical (compared to other categories, electronics, books, etc). 3 Beginner’s Guide to Fashion Personalisation dressipi.com Retailers still typically merchandise the same products in the same order to all of us so unless the dress you want to buy exists within the first 4 pages, you’ll give up and quit – resulting in a much lower conversion rate. This results in the following issues: • 20% of products driving 80% of the revenue (and this becomes increasingly self-fulfilling) • Low sell-through rates • Too much focus on optimising the PDP (Product Description Page) whereas most of the traffic is lost and the funnel narrows at the PLP (Product Listing Page) Bedrooms become the new changing rooms and free returns are commonplace. Of course, in store returns are handled more simply by putting garments straight back onto the shop floor, whereas posted returns are much more complex, so this development is clearly a worrying one for fashion retailers. In terms of the costs to retailers the following issues arise: • Lost revenue as P&P needs to be taken into account • What if the item is no longer in season when it is returned? • What backend processes need to be put in place to get a garment from the receiving warehouse back to the store (if that is the internal process)? • Restocking and cleaning costs i.e. the cost of getting the product back into circulation • There are also opportunity costs associated with not having stock available for others There are obviously other metrics that are also important (aside from conversion and return rates). Increasing engagement, frequency of purchase and average order value (AOV), as well as driving footfall in stores are KPIs that retailers are under increasing pressure to deliver on. Putting service back into stores and creating seamless experiences that truly serve the customer on an individual level can help them achieve this. There are winners and losers across all segments of retail (traditional bricks and mortar retailers, online pureplays, department stores etc). The customer has so much choice and there are so many other places to buy clothes that unless the value proposition is really clear (great product, great service/ experience at the right price point) then they will just go elsewhere. Personalisation is all about increasing the relevancy of all three components to ensure continued revenue and margin growth for the retailer. 4 Beginner’s Guide to Fashion Personalisation dressipi.com Personalisation Free face-to-face styling consultations are now widely available in many high street stores. They are popular and deliver good returns for retailers. The benefit of face-to-face support is that there is real-time assistance which takes your unique circumstances into account. However, this approach is not scalable and only appeals to around 26% of customers (many people lack the time or the confidence to see a personal stylist) and thus most consumers are left to navigate an endless array of options leading to a frustrating buying process. Fashion personalisation tries to replicate the in store personal stylist service, by using data to help optimise your purchase decisions. By collecting data from customers (who create profiles) and understanding how this relates to the garment data, it is possible to curate the range so that the shopper is offered a selection that better meets their needs, be this in style preferences, sizing or options that are based on previous purchase behaviours. Shoppers are in effect offered a reduced set of options that are more likely to meet their needs. Online personalisation has its roots in Amazon’s recommendation engine4 with Fortune5 declaring in 2012 that: ‘Judging by Amazon’s success, the recommendation system works. The company reported a 29% sales increase to $12.83 billion during its second fiscal quarter, up from $9.9 billion during the same time last year. A lot of that growth arguably has to do with the way Amazon has integrated recommendations into nearly every part of the purchasing process from product discovery to checkout.’ More recently, Spotify and Netflix have been trailblazers in the recommendation systems world for music and TV/film. More than 80% of the TV shows people watch on Netflix are discovered through the platform’s recommendation system6 and Discover Weekly playlists boasted 40 million unique users just a year after it launched in July 20157 . In short, retail personalisation aims to replicate personalisation engines in the context of fashion, however, what is less well known is how much more challenging the context is when compared to non-fashion personalisation. Fashion (Retail) Personalisation While personalisation has been readily available via the likes of Amazon, Spotify and Netflix as mentioned above, fashion personalisation is much more complex. In part, it is because fashion purchases are much more emotional rather than rational decisions. When it comes to fashion the following factors impact customer purchase decisions: 1. Seasonality – When is the garment being purchased? 2. Wardrobe – How will the garment fit in the context of existing clothing the consumer already owns? 3. Sizing – Perhaps my body has changed shape/size since last year. 5 Beginner’s Guide to Fashion Personalisation dressipi.com 4. Trends – Certain items come into fashion/go out of fashion. 5. Suitability – Does the item look good on and is it appropriate for the occasion it is being purchased for (work, everyday wear, a wedding)? Challenges also exist on the retailer’s side when it comes to personalisation including: 1. Garments only exist to be sold for a very short period of time – This is different to other verticals such as films or music which can always be purchased fairly easily for a very long time. The result is that in fashion each individual item only has a short period in which to collect data about it, meaning the domain ends up very sparse. 2. High Volume of Items – Large retailers release new garments daily, meaning there is a constant high turnover of products. Not only does this create problems around understanding product availability, but also ensuring that the recommendation system is sophisticated enough to capture data on the garments on an ongoing basis. 3. Data Sparsity – How well do retailers really know their customers or their garments at the necessary granular level? People shop across multiple retailers and channels. A customer might buy their jeans from one retailer and then go to another to buy a top. Each retailer has a very limited view of their customer’s full preference profile and overall spend on clothing. In short, standard recommender systems (such as click-based or cohort based) struggle to deal with the nuances around shopping for clothes. They tend to put a lot of weight on past purchases, but in reality, past purchases are not always indicative of future purchases. This impacts the predictive capability of these tools, as more intelligent data points can be used for better results. How it Works Retail personalisation refers to the process whereby consumers benefit from the retailer knowing about them – often through the creation of a personal profile, coupled with behavioural and garment data to help make better purchasing decisions. There are a number of technologies available on the market which tackle personalisation in slightly different ways, so this section is based on our experience at Dressipi. At Dressipi, it all starts with data. Our solution is typically dual-branded so the user usually starts by creating a Dressipi profile within a retailer’s own site. This information, alongside our garment tagging technology (each garment is tagged with a series of data points), thus allows the recommendation system to give high-quality personalised features such as: • Style advice • Outfits (including items the customer already owns) and similar items • Product recommendations throughout the journey • Email marketing (weekly themed and post-purchase) • Push notifications (to alert a customer when an item is in store) 6 Beginner’s Guide to Fashion Personalisation dressipi.com • Wishlists • Sizing (recommending the correct size) This is all provided on a truly one-to-one level8 , enhancing the customer’s shopping experience (online and in store) based on their intent and preferences. Some of the data points we capture on the customer include: • Body Shape • Age • Colours & Pattern Preferences • Lifestyle • Attitudes to Fashion To ensure personalisation functions correctly the retailer needs to provide transaction data (to measure value impact and further improve product recommendations), a product feed (to feed the algorithms and attach garment data to each item) and engage with partners like Dressipi on a consultative basis to ensure the most value is gained from the application. Customers will enjoy a seamless experience as the look and feel is controlled by the retailer, so everything from the personalised emails through to the individual’s customer profile will be completely on brand. Dressipi provides: • All garment tagging and metadata • API or hosted service (with all required support) • Extensive reporting and dashboards • Continual recommendations/algorithm updates • Weekly content for personalised emails and communication • Service emails • Access to Dressipi customer and garment data if required A typical deployment takes 4–8 weeks depending on the back-end infrastructure of the retailer. Key Benefits With regards to face-to-face personal shopping, the benefits are likely to include: • A less stressful shopping experience • A more efficient search (curation) • Access to professional input (3rd party validation of choice) • All of which leads to a better outcome for both parties (the store will likely benefit as the satisfied consumer will spend more and return less) When it comes to fashion personalisation at scale, the benefit set is broadly similar as both parties benefit from the engagement. According to Shep Hyken9 , the following represent four key benefits: 1. Personalization drives impulse purchases: Forty-nine percent of customers bought items they did not intend to buy due to a personalized recommendation from the brand they were doing business with. 7 Beginner’s Guide to Fashion Personalisation dressipi.com 2. Personalization leads to increased revenue: This is the big win for the company willing to make an effort to personalize the customer’s experience. Forty percent of U.S. consumers say they have purchased something more expensive than they planned to because of personalized service. 3. Personalization leads to fewer returns: Only 5% of impulse purchases (mentioned above) were returned, and 85 percent of impulse buyers were happy with what they bought. 4. Personalization leads to loyalty: This is the “Holy Grail” of personalization. Forty-four percent of consumers say they will likely repeat after a personalized shopping experience. Indeed these benefits mirror some of the findings from Dressipi, where they noted key benefits of a fashion personalisation deployment for the retailer included: 1. Increase in net incremental revenue per visitor by a minimum of 8% 2. Increase in conversion and frequency of purchase by up to 30% 3. Reduction in returns by 15% 4. Reduction in mark-downs Furthermore, fashion personalisation allows retailers to move from predictive retailing to reactive retailing. Data insight on the customer and individual products helps retailers optimise the product range for the upcoming season, as well as more accurately predicting the correct quantity and size for each individual location. In short, fashion personalisation offers value to both parties; the consumer, and the retailer leading to the elimination of some of the friction inherent in non-assisted transactions. Getting Started If you are responsible for any of the following it is likely that a retail personalisation solution can meet your requirements: • Growing revenue • Reducing returns • Digital transformation While there are a myriad of approaches (and indeed technologies) that can help with these, retail personalisation is designed to meet a number of these needs in one solution. The following represent some of the key steps: 1. Learn more about fashion personalisation and what it entails 2. Undertake an internal audit to assess capability to deliver 3. Assess which stakeholders need to be involved in the project 4. Consider option set via some market research 5. Engage a commercial partner like Dressipi to understand the solution 8 Beginner’s Guide to Fashion Personalisation dressipi.com 6. Decide and implement a Proof of Concept 7. Full roll-out. Build vs Buy? Some retailers will naturally assess whether or not an in-house build is an option. However, from our experience, we have yet to see a retailer successfully build their own solution and note they usually have to go back ‘to the drawing board’ 18 months down the line (after making a decision to build in-house) at which point they are falling behind competitors. Summary In summary, in an increasingly time-pressed world, the last thing customers want is to have retailers pushing thousands of options at them as soon as they enter the online or physical store. While filters can help narrow the choice, they fall well short of offering a truly personalised set of options. The good news is that technology can now deliver exceptional results that help both the customer and the retailer to better meet their respective needs for mutual benefit. Removing some of the friction from the shopping process ensures an enhanced experience for all, and retail personalisation solutions lead to better customer decisions be it in sizing or compatibility with existing garments. The commercial benefits are also compelling; ranging from an increase in average order value through to a reduction in returns, as well as enhanced buying and merchandising decisions. Footnotes and links 1 The Paradox of Choice by Barry Schwartz (2005) 2 Financial Times: Online retail sales continue to soar, 11 January 3 Vista Retail Support 4 Rejoiner: The Amazon Recommendations Secret to Selling More Online 5 Fortune: Amazon’s recommendation secret, 2012 6 Wired: This is how Netflix’s top-secret recommendation system works, 22 August 2017 7 TechCrunch: Spotify seeks more personalized playlists after Discover Weekly finds 40M users, 25 May 2015 8 Dressipi case study: One to One Personalisation 9 Forbes: Personalized Customer Experience Increases Revenue And Loyalty, October 29 2017 9 About Dressipi Dressipi is the world’s only Fashion Prediction Platform, enabling retailers to predict what their customers will buy and not return, optimising profitability and giving customers the best possible experience. Our data-driven approach helps drive significant new revenues for retailers (a minimum of 8% increase in net incremental revenue per visitor), decrease returns (by 15%) and increase conversion and frequency of purchase (by up to 30%). Leading retailers use Dressipi’s Fashion Prediction Platform for its best in class recommendations and prediction scores, enabling radically improved customer experiences and more informed decisions on demand to supply matching, merchandising and acquisition. Our unique database of over 5 million connectable fashion customers combined with fashion specific AI, expert knowledge and proprietary structured product data means retailers can be more profitable, more customer centric and more efficient. #bepredictive Contact Dressipi To learn more about how Dressipi’s data-driven approach accelerates leading retailers to be truly predictive, get in touch today. info@dressipi.com www.dressipi.com @dressipi Beginner’s Guide to Fashion Personalisation Copyright © Dressipi 2018 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form by any means without the prior permission of the copyright owner. This publication may not be sold or resold for any fee, price or charge without the written permission of the copyright owner. Every effort has been made to ensure that this publication is free from error or omissions. However, the author shall not accept any responsibility for the accuracy of the information contained within, or liability for the consequences of anyone relying on, or acting upon the information contained here within. This guide is for reference only and does not constitute professional advice. Where companies are named it is for illustrative reasons and does not indicate an endorsement.
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