We discussed changes going on with fashion in a previous post. The industry is becoming more global than ever. International brands have always been part of the fashion game, but nowadays shoppers, retailers and designers form a vivid ecosystem that has turned fashion online into a smooth, universal and fun experience.
While our blogger researched for this post, she was enticed – courtesy of a Facebook ad – to purchase a pair of gorgeous shoes. These shoes originated from a designer shop located in Seattle – quite a long distance from Tel Aviv, where she’s located. She paid in shekels and a couple of days later, a box was placed at her doorstep. These gorgeous shoes had to travel across the seas and then some more.
Fashion eCommerce is not just about being global. It is about offering the right item to the right person at the right time. This is where analytics comes into the picture.
The usage of Big Data enables an engagement’s first step – reaching out to the customer. Would our blogger have heard of that online shoe store in Seattle, were it not for the specific terms she was researching? Such relevant and valuable data can be extracted from SEO, but not from SEO alone. Analyzing valuable data such as reviews, likes and ratings is equally important. A ‘share’ from a friend or someone whose opinion we value, like a fashionista we look up to, is known to be a major channel for attracting new customers. Data is manipulated so that it can bring the right customer to the site or app. It provides insights about where the customers came from: Are they organic? Did an existing customer bring them to the store? Knowing where new traffic comes from and how much was paid for it is key to allocating future customer acquisition funds accordingly and boosting future traffic.
Bringing new traffic is awesome, but it is not the end goal. In other words, pageviews are great, but purchases are the real thing. Analyzing data helps increase conversion rates through the purchasing path. Why don’t customers check out once finding a desired item? What is the conversion funnel’s soft spot? Do they reach the shopping cart and then go back to browsing because they are looking for alternatives? Or worse, do they leave the site and look for alternatives elsewhere? Is there something about the checkout screen that prevents them from completing a purchase? Is the process too long…?
Asking the right questions is crucial. The existence of endless amounts of data does not guarantee an ability to extract appropriate insights. The right query is the gate through which one must pass in order to harness the power of Big Data. CoolaSQL is a flexible and agile tool that enables answers to the most relevant business questions.
Another important tool is path analysis. Tracking the actions of shoppers provides granular information about where the site, or app’s strengths and weaknesses are. There are many ways to optimize a site and business plan with these insights. Some have to do with improving the shopping experience, for example displaying items differently. Others have to do with bigger changes, like targeting different audiences.
Cohort analysis groups shoppers according to characteristics that you decide upon (i.e. the date a shopper registered, or made a first purchase, revenue parameters, location, gender, etc.). This means we can go deeper than just what action they performed to understand why they performed it. Cohort analysis allows segmenting the audience more efficiently, thus sending targeted emails, discounts, promotions or push notifications to a mobile app.
Then again, leading a customer to the end of a conversion funnel is great, but we want to keep those customers coming back for more. Our blogger bought those shoes – because, well, being the thorough researcher she is, she had no other choice really… But how will the shop retain her for future purchases? Retention is the one metric every consumer-facing online platform cannot ignore. Retaining customers means keeping them engaged. Encourage them to like, share and rate services. This not only brings in new customers through viral activity, it also increases engagement and loyalty. Use data to find out more about user behaviour and preferences. What items do they prefer? Which categories do the items belong to? Focus on data already collected: personalize messages according to segmented audiences. Track. Measure and keep iterating for best results.
Request a demo of CoolaData in action to see how this is done.