The fast fashion industry, which includes brands such as H&M, Zara, Forever21, Asos and TopShop, serves up new products at historically low prices and at an ever growing pace: within weeks of New York Fashion Week or being worn by the latest It girl (Kindred, 2015). Using data driven marketing, rapid product development and agile supply chain management, annual product lines have increased tenfold, product life cycles have decreased from months to weeks and even days (Sull and Turconi, 2008) and customers can consume clothing in on demand, disposable manner (Pal 2016).
As it feeds insatiable consumer demand, fast fashion is considered by some to epitomise materialistic consumption (Kim et al 2013). The rapid growth comes at social and environmental cost: unethical labour practices with poor health and safety such as child labour, sweatshops, excessive waste from disused clothing, and production methods that pollute the land, water and air (Kim et al 2013).
This represents a new opportunity for the sustainable fashion industry, as anti-consumerism is turning some consumers away from fast fashion (Kim et al 2013). The industry, dubbed “slow fashion” akin to the slow food movement, flies in the face of the fast fashion trend, as it pursues the “triple bottom line” objectives of economic prosperity, social justice and environmental quality (Elkington 1994).
This paper will explore how slow fashion can follow the lessons of fast fashion to transform itself using data in order to achieve their sustainability objectives.
This paper will discuss how, in order to achieve their objectives, the Slow Fashion industry must overcome 3 key challenges: (1) increase customer lifetime value, (2) reduce waste, and (3) prove a sustainable supply chain.
The business model of Slow Fashion, is characterised by higher unit costs and lower sales volumes. Handmade, artisanal items take time and skill to create, so they cost more per item, but tend to promote more timeless and more durable and therefore last longer (Clark, 2008).
As a result, Slow Fashion must find innovative ways to engage consumers over a long period of time to increase customer lifetime value (CLV), such as gathering and synthesizing data about what customers want (Zarley Watson and Yan 2013).
Slow Fashion can mimic its nemesis the fast fashion industry and obtain consumer demand data throughout the sales cycle as shown in Figure 1 (Kindred 2015). This enables a better knowledge of customers and supports agile product development (Sull and Turconi, 2008).
Consumer demand data is obtained through surveys, quizzes, competitions, social media mining, A/B testing of campaigns, using cookie tracking, wish lists, browsing behaviour, shopping cart and purchasing history, and participation in loyalty programmes (Kindred 2015).
Once the data is gathered, unstructured machine learning algorithms such as “K-Means Clustering” (Hartigan and Wong 1979) is used to make meaningful consumer segments to identify and target the styles and preferences of existing (and new) consumers. These segments, combined with identity management API services are used to recognise and know customers across devices (mobile, desktop, in store), tailor marketing campaigns, help them discover new product ranges, and make recommendations to suit their style and stage of the customer life cycle (Kindred 2015). This is turn drives customer loyalty and hence CLV.
Product Discovery and Recommendations
Recommendations algorithms are being built for product discovery, which, when accurate, encourages more frequent purchases from a customer increasing their Total Average Revenue Per User (ARPU) and total average products per user (APPU). These can be suggested to consumers when online, via eDM, or returned as search results, and may utilise natural language processing and visual search (although these are both nascent technologies) (Kindred 2015).
By building artificial intelligence based learning mechanisms (such as a feedback loop on click through rates among other indicators), the accuracy of recommendations algorithms can improve, in turn driving better customer retention and repeat visits (in store and online) in a cost efficient way.
Figure 1 Fashion Data Cycle (Kindred 2015)
However, categorising and classifying customer preferences and inventory into a metadata taxonomy and structure to enable natural language processing and visual search can be challenging. There is no universal taxonomy for fashion styles, colours and preferences, from a product or a customer point of view (Kindred 2015). For example, the slightest change in hue of colour or the length of an item can make all the difference in what is “on trend”. In addition, the visual images of collections and related metadata are intellectual property and brands are unwilling to release this information, which limits the data available for these services (Kindred 2015).
For an organisation to become more data intensive, a significant change in mind set and skill set is required (in order to change company’s culture towards data driven decisions. The data science skills required to achieve this customer segmentation are short in supply and might not be as accessible to small manufacturers, which Slow Fashion entrepreneurs usually are.
By delivering the right product, in the right quantity to the right location to the right customer, the fashion industry has an opportunity to reduce waste (Sull and Turconi 2008). This is even more crucial for the Slow Fashion industry, as the lead times are naturally longer due to sustainable production practices, the supply chain is less agile and conservation of natural resources is also an objective. Utilising data to improve sales forecasts and optimise systems can reduce waste for slow fashion brands.
Accurate Sales Forecasts
Sales forecasts must be accurate at an individual SKU level in order to avoid stock outs and discounting, however this is a challenge as demand is highly uncertain and seasonal (Guo et al, 2011).
Through the data gathered on consumer preferences throughout the product data cycle (Figure 1), and through tracking market signals i.e. key word mentions in media, influencers and brands online channels, and visual image search and recognition, Slow Fashion can build more accurate demand forecasts as fast fashion players. Statistical techniques and machine learning presents an opportunity to take hundreds of signals in real time and translate them into a product forecast (Guo et al, 2011).
However, analysing and making sense of this data cannot be performed by machines alone as fashion is characterised by subjectivity, extreme fluctuations in demand and contextual relevance (Kindred 2015). For example, the hands-on role of Zara’s store managers has been critical to the success of Zara’s agile supply chain (Sull and Turconi 2008).
Human understanding is needed to interpret the qualitative and quantitative data, in order to differentiate in real time, between an anomaly and an emerging trend, and adjust forecasts as necessary (Kindred 2015).
Optimisation of Systems
Optimising production processes and the distribution value chain is also crucial for ensuring efficient use of resources and reducing waste.
In recent years, the price of microprocessors and cloud storage is becoming so low that it is possible to connect almost all devices to the internet, for example putting a micro-chip into each garment like UnderArmor – Fitbit for clothes (Kindred 2015). Through this “Internet Of Things”, performance data for key elements of Slow Fashion production, distribution network, online and offline, can be tracked in real time and stored in the cloud.
Data analytics and structured machine learning algorithms can be used to analyse and visualise this data, in order to provide solutions that optimise processes and production to reduce waste and use resources sustainably i.e. factor in enough workers so hours are reasonable, allow enough time for fields to recover between plantings etc (Guo et al 2011).
The challenge with obtaining this data is the internet connectivity and power supply of local suppliers who are often in developing nations may not be reliable, and therefore there may be missing data. In addition, despite the reduction in tracking costs, rolling out a tracking system to a large number of small independent producers may not be feasible for the smaller scale slow fashion brand.
Slow Fashion has the desire to prove to stakeholders that their supply chain is managed sustainably, and the provision of reporting can provide transparency and traceability to illustrate this (Morgan 2015).
By tracking raw materials from their source (using the Internet Of Things as described above), reporting on key equity measures such as working hours, and the production of, and payments to artisanal suppliers, and the use of data visualisations on Slow Fashion brands websites, consumers can see the origin of their purchases, and also see the impact their patronage is having on local communities over time (Carter and Rogers 2008).
In addition, in order to make the Slow Fashion supply chain as agile and responsive as possible, it can use supplier identity credentials, electronic data interchange and open book accounting to enable trust between suppliers, brands and consumers (Park et al 2013).
However, slow fashion brands are not generally of the scale to demand compliance from their suppliers, and gathering consent might be difficult. Suppliers may be primary producers without IT systems, so obtaining consistent, accurate and regular data could be a challenge.
Lastly, this kind of open data sharing could be a privacy issue for many small suppliers as it basically reveals their household income. In areas of civil unrest, data could be used for unintended purposes and compromise the safety of some suppliers.
This paper has shown how Slow Fashion participants can become more data driven to address the opportunities and challenges facing their industry.
They can use data mining to identify potential customers as consumers pursue fast fashion avoidance. Concurrently, they can use product and consumer data to know their customers better, and through algorithms and machine learning, match their product line and processes with consumer demand more accurately increasing ARPU and APPU. These strategies reduce waste, grow revenue and improve the triple bottom line (Elkington 1994).
Furthermore, using data to report on the supply chain can also prove to stakeholders that a Slow Fashion brand is authentic and sharing value with its suppliers, and over time illustrate that it is delivering long term value to the communities that work with it.
This data intensity will require a significant mind shift amongst suppliers and brands in order to make data central to decision making, as well as making the supply chain mobile internet connected.
In this way, as Slow Fashion becomes more
data intensive, they can innovate in a way to achieve the triple bottom line
benefits of economic prosperity, social justice and environmental quality.
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