Using Artificial Intelligence to Analyze All Fashion Customer Data
The apparel market is one of the largest in existence, accounting for 2 percent of the world’s GDP, and valued at roughly $3 trillion. Every year, American households spend close to $2,000 on apparel alone, and over 211 million of these shoppers make their purchases digitally. In an industry this large, and this competitive, it’s hard to keep customers engaged and active with your lines.
With the increase of digitalization, retail markets are growing at an unprecedented pace. 15 of the 20 cities with the largest growing apparel sales lie outside of the Western marketplace. Newer markets are already beginning to dominate sales. Markets in Asia and South America, for example, already account for one third of global revenue in female apparel, and this number is only expected to grow.
With this trend towards globalization comes the question: “Can a global brand really live up to the needs of diverse cultures from Latin America to Eastern Europe all the way to Asia without putting its identity on the line?” (McKinsey) The differences in expectations and desires from country to country are massive. It is a big challenge for global retailers to properly segment and market to their increasingly diverse international buyers.
To become a staple in someone’s wardrobe, you have to understand them and their style—now and in the future. Consumers want the brands they purchase from to provide them with promotions and messages that are tailored to their specific look. 86% of customers say that personalization has an impact on what they buy/who they buy from and 1/3 of customers feel that there isn’t enough personalization in their current shopping experience. Converting transactional sale shoppers into repeat buyers who wear multiple items from different lines is a big challenge.
In order to achieve this, you need to be flexible and dynamic in segmenting and marketing to your wearers. Trends are constantly shifting, media consumption is ever changing, channels of engagement are different from one month to the next. Today, roughly 35 percent of consumers rely on recommendations from social networks, a metric that would have been non-existent 10 years ago. Being able to get ahead of someone’s style and understand their tastes, and what SKUs suit their look, can be the difference between someone buying “those shirts on sale that one time” and a repeat customer who bases their wardrobe on your seasonal pieces over the next three years.
The amount of customer data currently available is huge. Browsing history, purchase data, social mentions, and spending trends are all sitting in data lakes and warehouses at your company waiting to be unlocked. When used correctly, this data can show what customers are buying, when they’re buying it, and what they’re likely to buy next. In this way big data has the power to revolutionize how the apparel industry interacts with its customers—by creating forward thinking styles, offers, and recommendations at the individual customer level.
How AI Can Help
Although most big fashion brands already have tools and data scientists that help them make sense of their data, there’s an opportunity to implement an AI + Machine Learning based solution that can double or even triple the speed and accuracy with which they act on their shoppers data and preferences.
