While shopping for a shirt online, I had a surprisingly poor experience with an apparel brand that I’m particularly fond of. Rather than naming names, let’s call them Sean’s Shirts. Sean’s website makes some rookie mistakes in data management and user experience (UX) that undoubtedly cost their eCommerce channel several thousands of dollars in business every day.
Many companies are looking into cutting-edge data science and machine learning techniques as a way to address their data management challenges, but without first squaring away UX data management, these powerful tools will only yield a fraction of the ROI they are capable of producing.
This article points out common eCommerce mistakes you should avoid, and suggests some basic issues to resolve to get it right. In the examples below, the underlying problems can be resolved, not by implementing advanced data science techniques, but by making improvements to UX and data management fundamentals.
Link your PDF catalogs
Since I was on their mailing list, I recently received the Sean’s Shirts PDF catalog via email. While on the surface it appeared to be a well-designed catalog, it wasn’t long before I encountered a serious limitation: the catalog’s product pages weren’t hyperlinked! To actually buy a product, I had to copy the name, go to their website, search for it, and then wade through the search results to find what I was looking for.
Not all of your customers will be as determined to buy your products as I was. Make it as easy as possible for your users to purchase catalog items. Minimize the number of clicks it takes to find and buy your products. Hyperlinks in PDF catalogs are one important way to accomplish this.
Only list what’s relevant
While searching for formal shirts on the Sean’s Shirts website, I was bombarded with 239 products, most of which were accessories like cufflinks, ties, and scarfs. The first page of search results displayed 20 products, of which only 3 were actual shirts.
Show your customers exactly what they’re looking for. 79% of visitors who have trouble accessing the right information on a website say they won’t return. To prevent this situation from arising with your shoppers, analyze their virtual footprints and remove distractions like unwanted, low-relevance search results.
To offer your customers highly relevant products, you must study visitor behavior and measure your UX effectiveness. While machine learning can further hone your search results and provide personalized recommendations, effective product and customer data management remains a prerequisite. If done properly, even simple tag-based categorization can vastly improve the accuracy of your online store’s search capabilities.
Add effective search filters
I ran into another issue while scrolling through the search results for “formal shirt”. Although some basic sort and filter controls were available, they didn’t include useful options like “Best Match” or “Popular”.
If you make customers toil to find the product they want, chances are they’ll give up in under 10 seconds. Adding common controls like “Sort by Price” and product category filters are essential, but you should also add more advanced search capabilities.
To create a “Popular” filter, you need visitor data that’s indexed and ranked, which underscores the importance of data-centricity. Similarly, offering product-specific filters like “Collar Style” for formal shirts requires sophisticated data management. These UX features offer a powerful way to make your online shopping experience stand out.
Remove any extra steps
I eventually found a shirt I liked and began the checkout process. Because I like receiving my purchases as soon as possible, I decided to choose the “pick up from store” option. I was expecting the site to automatically detect my location and suggest the nearest outlet, but instead I was left to make that choice on my own. That wasn’t all: even though I had already set up a user profile on the Sean’s Shirts site, I had to enter my address again – not the best checkout experience.
Never make your shoppers repeat a task. As mentioned earlier, you should strive to minimize customer effort. My shirt-shopping story is a prime example of poor master data management. Sean’s Shirts failed to centralize customer data so it could be used as needed throughout their buyer’s journey.
Shoppers are highly goal-oriented – they want to get the job done as quickly as possible. Any friction in your user experience equates to customer attrition. Effective master data management can help you avoid needlessly inconveniencing your shoppers and reduce churn.
Conclusion: Take your time building effective eCommerce
“Nothing great comes easy, and nothing easy can ever equate to greatness.”
– Edmond Mbiaka
Truly great eCommerce offers users a hassle-free experience, and that requires a data-centric approach and efficient, intuitive UX. Reducing clicks, prioritizing relevant content, and simplifying the buying process all rely on adherence to proven usability practices and a data-centric culture across your enterprise. While your long-term goal might be to implement AI, machine learning, and other advanced methods for improving your online experience, achieving data-centricity and enhancing usability take precedence.
Read more: Why is eCommerce integration important?
With a data-centric culture in your organization, you can personalize your upselling and cross-selling strategies, research and develop data-driven product enhancements, and develop customer-focused sales and marketing strategies with real-time access to customer behavior. A well-crafted user experience empowers your customers by giving them quick and convenient access to your products and services.
For additional information on how your business can implement a data-centric culture and offer a better user experience, contact Visionet today for a complimentary session with our experts.