Part 2 Typeless Search: Refine & Filter
This is the second part of a series of 4. You can read the 1st part in this link.
The future of search is typeless. Search can no longer be conceived as an input/output retrieval system, but rather as a platform for communication, one that provokes and nurtures a rich contextual dialogue between a brand and its customers.
This second post in the series will focus on search formulation, filtering and refinement use cases, which happen both in the search box as-you-type and in the search results page (a.k.a. SERP).
How could shoppers be guided and assisted to quickly find what they’re looking for without having to scroll and/or refine the initial query (ie. typing again in the search box)? Let’s explore how brands can use the search experience as their best ambassador and sales-assistant to inspire and guide with trends and style recommendations.
Type-ahead & search refine suggestions
The typeless experience begins with the search bar.
Say that a shopper wants to buy some new jeans. What is trending now?
By typing “jea” in the search box, you instantly know that “regular” “ripped blue” and “slim fit” are the trending styles for men today, and “mom”, “high waist”, “white”, and “wide leg” the favourite ones for women.
She selects “jeans” and a list of trends in the form of “tags” are displayed to further signal the initial intention. “High waist” style is selected, new items are displayed. So what’s trending in “high waist jeans”? Well, lets ask the search bar again: “high waist mom” “high waist with yoke”. What is yoke? Lets try that. Voila! She finally finds a pair of jeans she loves without knowing how to search for them.
The shopper needs to buy some beauty products as well. She always buys Clinique from her favourite store, particularly, she loves Clinique’s Moisture cream. As a brand enthusiast, she wonders what other great products Clinique is well known for. She decides to ask the search “shop-assistant”.
The shopper has found what she wanted on the front page, intuitively, with almost no typing and no hit-or-miss requests. The interaction feels natural, like real shopping because those suggestions feel relevant and speak their own language. Search refinement suggestions are generated every single day from the local store. Local customers help local customers to find what they’re looking for with very little effort.
Visual filters that inspire but also guide
But natural is not necessarily human. To feel more human, a search experience must yet do more. Besides dialoguing without text, the search interface must also inspire. It must remind us visually of the human impact of our purchase while also guiding us through what it contextually knows to be our shopping priorities.
Visual filters are of great help for shoppers to skinny down a list of results through the use of “traditional” filters but displaying them in a different way. The use of icons to represent colour, shapes, age, size, category and so on could be a great complement to the refinement tags presented above.
Here are some other visual filter examples designed to help customers find the right dress for a special event or the best toy depending on the age of the kid:
The combination of search type-ahead and refine suggestions, together with visual filters are great ways to inspire and help customers explore a store’s catalogue and latest trends, always in a contextual, visual and effortless way.
Optimizing the search journey with your customer’s dialogues
A flesh-and-blood shopping assistant would entertain the shopper as they walk through the store. The search journey must do the same. To that end, we emblazon navigational aids such as product suggestions, category buttons and trend tags with evocative visual cues, and express them in the same language as the customer would. Guiding shoppers joyfully through the merchant conversation, these cues take shoppers on an emotional journey, not a mechanical ecommerce event sequence. Even better, the placement of these cues prioritises product trends and best selling choices according to real-world shopping experiences, not necessarily the cold logic of a product category page order.
In the next post we’ll explore new ways for shoppers to engage in meaningful search-powered conversations beyond the initial intent, recommending new products that they were not looking for in the first place.
Stay tuned!
Credits: Thanks Michael Airhart for co-authoring the content.
