“The Way it Was” Isn’t a Realistic Expectation
Getting“back to normal” won’t look the same in the retail world post-COVID-19. The so-called retail apocalypse was already underway before the pandemic started, and the virus has simply reduced the time you have at your disposal to respond.
A paper published in the Harvard Business Review reviewing the aftermath of the 1980, 1990 and 2000 recessions found the companies that thrived in the post-recession environment did so through timely, well-chosen investments in their core capabilities. A 2019 follow-up in that publication — which added the lessons of 2008–2009 —reached similar conclusions.
Retailers need to focus their efforts and investments on what’s immediately actionable and delivers an immediate payback. Improving the online shopping experience for your customers can deliver on both counts.
On-Site Search Is a Critical Pain Point
Consulting firm McKinsey & Company followed its annual report on the state of fashion in 2020 with a COVID-19 specific update analyzing how the pandemic has impacted the sector. The report highlighted “improving the customer journey and the broader customer experience” as a critical priority for fashion e-commerce.So, where do you start?
One pain point for customers is the sheer difficulty of simply finding what they’re looking for. In a physical store, customers can ask an associate for help, but online they’re forced to negotiate with your site’s search functions instead.
Yet, in a 2020 annual report one-commerce search, researchers at the Baymard Institute scrutinized search capabilities across 60 top e-commerce sites. The results were underwhelming, with 61% of sites delivering unsatisfactory results and 15% outright failing to show appropriate products. Those are sobering statistics. If customers can’t find what they’re looking for, they’ll simply go elsewhere.
If Not Search, Then What?
The Baymard Institute offers practical, actionable advice on improving your site’s search capability through things like “smart scopes” and improved autocomplete suggestions in your search bar. By all means, you should implement the ones that make sense for your site. However, this begs the larger question:what if someone doesn’t know what they want?
The issue is discoverability — how will your customers know what they want until they see it? Search works best for shoppers who know exactly what they want already, and Baymard’s research showed that it’s weakest at helping people find things they can’t exactly describe. It lets down those customers who would ordinarily have browsed your store searching for inspiration or asked an associate’s advice. Search simply isn’t made for queries like “dress to minimize my hips.” However,that’s exactly the type of experience e-commerce brands now need to deliver throughout the digital experience.
Heightened Expectations for Fashion E-commerce
Outside the fashion industry, streaming platforms like Netflix and Spotify track what customers search for, the things they watch or listen to,and make recommendations based on those.
That kind of personalization is sharply limited in the fashion e-commerce world. Knowing what kind of dress or handbag to recommend for a specific shopper is a lot more complex than knowing their purchase history or their gender and age. And your website may be constrained in how well it can deliver true personalization because your product catalog simply may not be providing enough data points (“dress,” “red,” “cotton,”“style") to make it work.
Baymard’s researchers argue that “All significant product attributes should be searchable, even if they don’t exist for all products sold on the site,” but that’s usually not the reality. Manually overhauling your product data to add more attributes would be costly and time-consuming. So, how can you move forward to greater personalization?
Machine Learning Can Accelerate Your Transformation
The answer is not to do it manually but to lean on sophisticated AI algorithms to do it for you. Lily AI’s proprietary machine-learning system has been trained to analyze the products within your existing site and apply tags for every meaningful product attribute, and combine them intelligently.
This deep tagging provides your site with an improved ability to handle the specific search queries your competitors struggle with, but that’s secondary to its real importance. By tracking those additional data points, Lily AI’s algorithms can take you past basic personalization (“Customers who bought this also bought that”) and into the realm of hyper-personalization.
Going Beyond Basic Personalization With Lily AI
Standard approaches to personalization draw on purchase history or broad segment-level data about preferences and buying patterns, but these provide little insight into why consumers make the choices they do.That’s because individual choices — especially in fashion — are driven by emotional and psychological factors which are deeply personal.
Lily AI’s deep tagging and the thousands of additional data points it analyzes make it possible to know your customers at that deeply granular level. The more a given customer interacts with your site, the more the technology can infer about them personally — their body type, the things they like and dislike about their appearance, their preferred color combinations and their overall style and fashion sense. In short, it creates a psychographic profile of the customer as an individual. You’ll understand not just their purchasing history, but the emotional needs driving it.
The Future of E-Commerce
Inits COVID-19 update, McKinsey advised that retailers need to “...become not just more digitally adept but to become digital frontrunners” if they’re to flourish in the post-COVID-19 marketplace. Lily AI can help make you a digital frontrunner by creating a shopping experience built around your customers’preferences.
It takes just four weeks to “go live”with Lily AI and begin to give your customers the online shopping experience that will keep them coming back. Ready to learn more?
Fred Decker is a freelance writer based in Atlantic Canada. His work has been featured on numerous sites including MSN, Livestrong, The Nest, the San Francisco Chronicle’s sfgate.com, the Houston Chronicle’s chron.com and many others. He was educated at Memorial University of Newfoundland,Nova Scotia Community College and the Northern Alberta Institute of Technology.