The Importance of Predictive Text In Personalized Search

If your website was a physical department store, then your site search bar would be the virtual sliding door letting a constant stream of customers inside. According to global payment service provider Klarna, 44% of online shoppers start their buying journey at the search bar of a search engine, with 41% starting from a retailer’s website. 

Shoppers coming from Google expect a Google-like experience when they search on your website – that requires a site search tool that understands context and  anticipates their needs. Predictive text can help retailers meet this expectation. It’s a personalized search technology that mixes AI and data to come up with a list of (meaningful) suggestions as customers type. This makes it much easier and faster to narrow down a large list of potential products to exactly what the searcher needs.  

What is Predictive Text?

Predictive text helps users search faster by suggesting words and phrases connected to their search query before they finish typing it. These suggestions appear as a list of clickable terms beneath or beside the search bar. It may be annoying when someone finishes your sentence for you at the holiday dinner table, but it’s incredibly helpful in the context of site search. Here’s an example from premium coffee maker Nespresso’s website: 

Predictive text suggestions on Nespresso’s website

A predictive text system processes each keystroke in real-time and offers suggestions based on what you’re currently typing and your past search patterns. This may be a list of similar keywords (e.g., hiking boots, hiking clothes, hiking gear) or categories (e.g., adult hiking boots, women’s hiking pants) or a mix of the two.

How Does Predictive Text Work?

Predictive text is uses natural language processing (NLP) and machine learning to understand queries and provide suggestions. NLP helps the system understand what shoppers mean when they type even in the case of mispelled words. Machine learning analyzes patterns in user behavior to determine the best suggestions. The system gets better at predicting relevant suggestions as more data accrues over time. 

The first step to integrating predictive search into your website is to have the search system index your entire product catalog. No content stone is left unindexed including product descriptions, brand names, category information, reviews, and technical specs. When a shopper starts typing their search query, the algorithm matches their input against this indexed data. It uses keyword matching to surface relevant results, but it doesn’t stop there. 

Predictive search systems also factor in:

• Previous searches and clicks during the current shopping session
• Historical purchase data for logged in users
• Popular searches from other customers
• Current inventory levels
• Seasonal trends

Suggested queries and categories are presented as a list of keyword and phrase options close to the search field. Shoppers can select any of the queries in the list with a click.

Predictive Text Examples

Here’s how predictive text guides different types of shoppers to relevant products even if they’re not 100% sure what they’re looking for: 

1. B2B Cleaning Supplies

A purchasing manager at a busy hospital types “industrial grade c” and a list of terms appear beneath the search bar to help him pinpoint some options – including industrial grade cleaning supplies, industrial grade cleaning equipment, and commercial cleaning solutions. The system uses the context of the searcher and the search itself to provide these recommendations (it’s coming from a business, they’ve purchased bulk items in the past, they need a specific type of medical-grade cleaner, etc. 

2. Gluten Free Groceries

A busy dad is ordering groceries from the park while his children play. He punches “gluten f” onto his grocery store’s mobile app and gets some relevant suggestions, again, based on the context of the search and the searcher. In this dad’s case, suggestions include gluten free pasta, gluten free bread, and gluten free cookies. Drawing from the customer’s past purchases, in-session browsing behavior, and current inventory, the system may recommend other foods suitable for people with diet restrictions (e.g., almond butter vs. peanut butter.)

3. Buying an Elusive Water filter

Maggie, a homeowner versus a contractor, types “samsung fridge wat” into Amazon’s search bar. The system might suggest samsung fridge water filter, samsung fridge filter, samsung compatible water filter, etc. Even without knowing their exact model number, the searcher can narrow down options through smart filtering. The system recognizes this is a maintenance purchase and might also suggest “how to install refrigerator water filter” or display filters that match previously purchased appliances.

Autocomplete vs. Predictive Text

When you type a query into a search bar, you might notice two different types of text assistance – autocomplete and predictive text. Though similar, these features serve different purposes and use different technologies.

Autocomplete = Word Completion

Autocomplete finishes the word you’re typing based on common word patterns and dictionary matching. Its suggestion is based on the current word and focuses on spelling accuracy. When you type “toas” it suggests “toast” or “toaster” to complete that single word.

Predictive Text = Context-Aware Suggestions

Predictive text looks at the bigger picture. It analyzes search context, user behavior, and historical data to suggest complete search phrases and relevant products. When you type “toas” it might suggest “toaster ovens under $100” or “2-slice toasters with bagel setting” based on your browsing patterns and data from other shoppers.

The technologies work together in modern search systems, with autocomplete handling basic word completion while predictive text provides deeper, more personalized suggestions that guide shoppers to specific products.

Benefits of Predictive Text

Predictive text changes how shoppers interact with your site. Fundamentally, it’s a tool that makes searching easier because it removes some of the barriers to product search, particularly on mobile devices. Let’s unpack the benefits of how predictive text works to improve and personalized search. Here are some benefits:

1. Reduces Frustration for Mobile Shoppers

It’s not easy to type on mobile devices. Even when you manage a successful search, sifting through huge lists of products is a slog. Predictive text simplifies mobile search by making suggestions as the shopper types and providing a list of relevant, clickable results. It’s also not stumped by typos, but helps reduce them via the autocomplete feature. 

2. Guides Customers to Relevant Products

Predictive text is a great sales tool. By suggesting products and categories related to a shopper’s search (even when there’s not an exact match), the searcher is exposed to a host of inspirational ideas and products. It also connects shoppers to products more quickly since each of the items in a predictive list are clickable. 

3. Eliminates the Need for Perfection

Unlike lexical (keyword) based search, predictive search doesn’t need an exact keyword match to produce relevant results. It uses NLP, so it can handle misspellings and vague queries (e.g., “gifts for mom”). It also looks at context, so it knows that “great gifts for candle lovers” could also include aromatherapy lamps or incense holders. 

4. Uses Data to Get Better 

The system uses data from all shoppers to improve the predictive suggestions over time. When multiple customers successfully find products through specific search patterns, those patterns inform future suggestions for similar searches.