Republished from Direct Marketing News by Elyse Dupre, Special Features Editor A look at how the…read more
Feb 23, 2017
How Data Science is Driving Customer Satisfaction
By Sujay Kar, Head of Data Science for TechStyle Fashion Group
(republished from MultiChannel Merchant)
Feb 22, 2017 2:15 PMWho hasn’t heard of buzzwords like ‘big data‘, ‘customer personas’ and ‘1-to-1 engagement’? Every now and then, someone makes a pitch that claims to be ‘disruptive’ or ‘paradigm-shifting’ with aims to dethrone market leaders and transform the industry, but often we’ll see startups and companies that threw around such jargons go belly up. But my experience with real world data science models has made me believe in personalization. It is at the heart of our strategy for hedging business risk and increasing customer satisfaction in fashion.
The ecommerce space is going through a major transformation like the Fintech industry, where one-to-one marketing and personalization are driving innovations through technologies like machine learning.
Companies are now able to leverage this fascinating convergence of technology and consumer demand. Attaining personalized engagement with your consumer has to be on top of the list for every marketer in today’s crowded marketplace. In the past, marketers didn’t really focus on strengthening the relationship with their customers before the iPhone came to life. All they wanted to do was work on that great Super Bowl ad and wait for the money, in true Mad Men style. However, now our customers have access to a smartphone, which every brand can use to reach them, creating more challenges in getting their attention.
The Smartphone Changes Everything
The fundamental truth here is that the smartphone is really a ‘micromarketplace’. For customers, it is a means to accessing
this incredible bazaar where brands are delivering a wide range of experiences. Traditional advertising can’t persuade customers to respond positively anymore. Consumers really want personalization when making a purchase, especially in industries like fashion where 38% of consumers want personalized products.
Today’s smartphones have created a new breed of ‘on the go’ customers, who use the improved computing power of their devices to get personalized content at their fingertips. According to Adobe’s ecommerce tracking, this Thanksgiving saw 58.6% growth on mobile transactions over 2015.
Trusted Friends are the Most influential
Unsurprisingly, people are opting to believe word-of-mouth and community-based recommendations when compared to company advertisement and promotions. A recent IBM survey s
howed us that only about 10% of consumers are paying attention to recommendations from a manufacturer or retailer. However, 90% of consumers trust a recommendation from friends and family, and 68% believe other consumers. Another study found that 74% of online consumers get frustrated with websites when they receive content that has nothing to do with their interests.
Brands have tried to solve this problem with the help of marketing automation that provides content to consumers based on their personas as informed by their clicks, page views, and behavioral data. But this wasn’t delivering true, personalized experiences as large swaths of customers were being sold the same products.
Companies can deliver on their promise of personalization with the help of big data analytics, virtual design and a flexible approach to global production. Though it may come as a surprise, the best potential here lies in fashion – an industry that arguably relies on personalization more than any other.
Fashion’s Rendezvous with Personalization
Fashion has always been all about expressing your personality. There is no other industry that better helps its customers in embracing their desires – whether it is to fit in or stand out. On one side you have couture Valentino gowns and custom Armani suits, and on the other – thousands and thousands of ‘value’ fashion garments available on discount racks. Designers, manufacturers and retailers undertake big gambles daily in trying to anticipate what their audience will like.
Tony Hsieh built Zappos after figuring out how ecommerce could allow fashion brands to develop a richer engagement platform and successfully link marketers with their core customers. Using the mantra of ‘delivering happiness’, Zappos is using the modern tools and technology to create a better online experience, improve customer service and deliver personalized products at an individual consumer-level. The last point is the tricky part. How can marketers really deliver on that promise?
NIKEiD has a solution, by charging a premium for personalization — on its products from high heels to oxford shirts – often referred to as the co-creation model. But since a brand can only offer options based on baseline products that have already been embraced by a large critical mass of consumers, this alternative has its limitations. Then there are companies like Unmade that deploy a digital production platform, allowing customers to design and order highly customized knitwear. This is a highly personalized, zero-stock, on-demand model – and this model might face challenges while scaling up.
Looking at this tactically, we use membership subscription to offer true personalization, by allowing brands to collect meaningful data, and produce and sell products that are designed, merchandised and created – keeping in mind a customer’s desires and preferences. With a monthly membership, customers think deeply about their picks and make selections that will help a company gain the necessary data, which can be used to customize the entire experience journey for consumers – from brand advertising and dynamic content to customer support and loyalty incentives. Outside of a company’s questionnaire that a consumer may fill up during signup and tracking site behaviors (which is used by most serious brands nowadays), data points can also be gathered from social media and third-party sources.
Looking to the future
Moving forward, brands and companies can sharpen their personalization strategy by ensuring that data, personalization and analytics teams work hand-in-hand with the media and creative teams in producing ads and content, and implementing a smart media buying strategy. And an effective online personalization strategy doesn’t have to apply only to digital efforts, these can be replicated across physical, brick-and-mortar retail stores too. Companies with both physical and online stores can determine what a customer likes based on his online purchasing habits, clicks and other data to make recommendations in-store when they visit in person. This can help to deliver a better experience across both physical and virtual channels.
Companies can also win the personalization conundrum with a platform integration that delivers custom communications directly from the brand to the customer, and helps to offer customers with highly relevant and valuable content that leads to an increase in transactions and sales. By combining good customer support with your marketing and branding efforts, marketers can craft a winning strategy that helps increase revenues.
Stories of innovation are told in the world of technology marketing every day, but most of them don’t make a strong enough stand to survive the test of time (and smarter competitors). While innovators of tomorrow might be dreaming of VR-enabled meetings, fully autonomous cars and big data robots that could perfect the personalization pitch, it’s time for those in the fashion world to wake up – as personalization is here and its promise is real. With the arrival of data and personalization, the fashion industry has a way to continue delivering custom products to its customers, and take better and calculated risks. Ignoring personalization, would equal ignoring customer happiness – and that is just fashion suicide.
Sujay Kar oversees Data Science for TechStyle Fashion Group – His role is in building a data driven organizational strategy that drives growth across all techStyle brands and business areas, including web analytics, media analytics, retail analytics, product analytics and finance Analytics.