Big Data, the mining and processing of petabytes worth of information for the purposes of gaining deep insights into consumer behaviour, supply chain efficiency, and so on, is big business. It is widely hyped as the next big thing for enterprise, allowing advanced analytics that will drive in-depth understanding, and optimise efficiency, in a wealth of business processes.
There is scarcely an industry that will not be touched by the power of big data. But is it really worth the investment? A recent study conducted by Bain & Company makes the answer crystal clear. The study notes that early adopters have gained a significant lead over competition, with advanced analytic capabilities driving outperformance of competition by wide margins:
Twice as likely to be in the top quartile of financial performance within their industries
Twice as likely to use data very frequently when making decisions
Three times as likely to execute decisions as intended
Five times as likely to make decisions significantly faster than industry peers
Simply put, companies who are lagging behind in their adoption of big data will miss out on an increasingly essential competitive tool.
By enabling customisation options for consumers, there is the opportunity for a raft of data collection. At a superficial level, of course, website orders generate demographic and geographical data. However, a study by Deloitte recently found that consumers are generally happier to part with a bit more personal data in exchange for personalised products, thus deepening the amount of data collectable via their customised orders.
Most importantly, however, big data on a mass customisation project enables companies to better understand the specific desires of consumers. Whilst, of course, purchasing habits can already be analysed in order to inform future collections, the ability to identify specific elements of consumer desires provides the opportunity to develop future products, whether mass produced or customised.
Where a manufacturer divides its production line between mass production and mass customisation, both aspects can benefit from big data. Essentially, deep analytics allow a brand to offer more of what the customer wants.
Beyond the order process itself, a brand that brings additional elements to their website (and bricks-and-mortar store, in fact) can further make use of big data. User experience (UX) is the cornerstone of brand positioning; an engaging, interactive website is central to driving traffic and conversions. Gamified website or app content, which could - for example - allow visitors to design digital versions of preferred outfits or interior spaces, is an engaging method of gathering data on consumer trends and desires. Over and above industry-proposed trends (i.e. from Fashion Week and Vogue), a brand who engages with the mass customisation model has the opportunity to approach customer desires head-onCovet: The
A great example of an app that has big potential for driving big data direct to fashion brands is smartphone and tablet app, Covet Fashion (by Crowdstar). Covet is presented as a game, in which players create outfits from current collections of real-world brands, such as French Connection, Rachel Zoe, Chromat, and Nicole Miller. Over 50% of users cite Covet Fashion as their number one source for style inspiration, over friends, family, magazines, and social media.
Though there is no explicit mention of the data gathering opportunities offered by Covet, it goes without saying that these will be significant.The app does not simply give users the ability to create looks. The point is that looks are designed for pre-set daily contests with a theme, which may be something like ‘Design a look for a cocktail party using one French Connection footwear item and a Rachel Zoe skirt’. The user ‘buys’ items into their virtual wardrobe, and uses them as part of their outfit for the contest. Users enter their looks into the contest, and those looks are then judged by other users to establish those which are most popular within the user base. Those with the largest number of votes are awarded with virtual money to be spent on further clothing items for future contests. As a player’s wardrobe grows, they are awarded unlocked privileges (such as new hair and makeup styles) at ‘closet value milestones’.Thus, the brand is able to gather data on what kinds of looks are trending with users. Furthermore, users can click through from the app to buy those clothing items in real life. Covet is, therefore, an absolute treasure trove of data.
Driven by the success of Covet Fashion, Crowdstar has released Design Home, an interior design game that works off a similar premise to Covet. Fellow players rate each other’s interior designs, and can click through to buy the real life furniture and homeware items featured on the app.
Design Home represents a massive big data gathering opportunity that is getting excellent traction on the iOS and Android app store, with a growing user base that is set to rival that of Covet.
Both Covet and Design Home have proved phenomenally successful apps, so much so that Glu Mobile announced recently that it had acquired a controlling interest in Crowdstar.
The success of Covet Fashion and Design Home are proof positive that the desire to create bespoke spaces is strong in consumers. So, what if a brand offering mass customisation can take this a step further?
Whilst purchasing products via mass customisation has strong appeal with consumers, as evidenced by the success of Nike iD, and Adidas’s recent foray into the customisation model, purchasing is the final action in the conversion model.
In order to start consumers along the path to purchase, a gamified version of customised design, using the 3D rendering software on the brand website, could prove a useful method of both driving conversions and gathering big data.
Rather than simply enabling mass customisation of products for purchase, what if brands were able to offer users the opportunity to design customised products using simple-to-use 3D rendering software? The wealth of data generated from allowing users to create a profile and portfolio to be shared with other website users would be a useful tool in gathering more data on what customers want.
Of course, it also improves user experience, brand perception, and overall traffic to the site. Along the way, whilst creating designs, it can be logically assumed that many will be inspired enough by the designs they have created to convert to sale.
We already know that consumers are increasingly focused on developing their unique identity through the products that they purchase. This, as we have stated before, is partially driven by social media, and the rise of an identity-centric culture which has been further enabled by the digital age. It has therefore become increasingly difficult for brands to gauge precisely what customers are looking for in the products they buy.
The rise of big data offers an in-road to facilitating this process. Combined with more tools available than ever to allow such large-scale data collection, the barriers that have stood in the way of true understanding of consumer desire will crumble.
Brands are now becoming increasingly empowered to provide products and services specifically designed to meet customer desires. Similarly, consumers are being empowered by the opportunities to customise the products they purchase, simply by giving up a little more information than they are used to about their purchasing habits.
Combining big data with mass customisation is a key step towards optimising this empowerment for both brands and consumers, building a future of purchasing where all parties are getting what they need and what they want.