Google’s Search Strategy | Strategy for Product Managers

Imagine you want a pair of red sneakers.

You visit an online store, type “red sneakers” into the search bar, and hit enter. Instantly, you’re presented with hundreds of options.

At first, it seems like you’ve so many choices! But as you start scrolling, you realize a problem: most results don’t align with the specific image in your mind.

1. Some sneakers are fire-engine red, while others are burgundy, scarlet, or even bordering on orange.
2. Some have the right shade of red but lack the features you care about, such as a sleek design or a particular material like canvas or leather.
3. Others are simply in the wrong category — perhaps running shoes instead of casual wear.

Despite spending precious time sifting through filters and scanning countless pages, you still can’t find exactly what you’re looking for.

The problem — Words alone are not Enough for Search, as they are limited

Solution → Multimodel Search

Google's Strategy is to make their Search “Multimodel”

What is Multimodal Search?

Multimodal search overcomes these limitations by integrating various types of inputs — text, images, voice, and even contextual data.

This multi-faceted approach allows users to express their intent in diverse ways, creating a more intuitive and efficient search experience.

Here you use an Image search to look for a Dress.

You have a bunch of options that are similar to the dress you do Image Search on.

Some of these options are according to your taste and preference but some of them are not.

What if you want to refine the Search Results?

What you did is, you give another input from the Text on the Image you have searched for.

This is Multi-input ( Where you are giving input from Image + Text ).

See the Results which you get when you combine the Image and the Text.

This is what a Multimodel Search is, when you combine Inputs from various Sources to give the Search Result.

How Does Multimodal Search Work?

At its core, multimodal search combines data from different sources to deliver precise results.

Imagine spotting a handbag that you like on Instagram.

Instead of struggling to describe it in text, you upload its image to a shopping app.

The multimodal system analyzes the image, cross-references it with textual descriptions, and presents options that match the design, color, material, and even your brand preferences.

Voice search is another game-changer.

Imagine cooking and realizing you need a specific kitchen gadget. Instead of typing, you simply say, “Show me egg beaters available online,” and the results are tailored to your request, merging voice inputs with contextual understanding.

Transforming the Shopping Experience

The benefits of multimodal search are:

1. Faster Product Discovery: Users can find what they’re looking for quickly, without the hassle of endless scrolling.
2. Enhanced Personalization: These systems learn from user behavior, preferences, and previous searches to deliver curated recommendations.

Real-Life Applications of Multimodal Search

To understand its potential, let’s explore some practical scenarios:

Fashion

You spot a celebrity wearing a stunning jacket on social media. By uploading the image into a shopping platform, the system identifies the exact jacket or recommends similar styles that match your preferences.

Home Décor

Imagine remodelling your living room and needing a couch that fits your aesthetic. You upload a photo of your space and describe your desired style. The multimodal system considers both the visuals and your voice inputs, presenting options tailored to your needs.

Groceries

You scan the barcode of an ingredient or upload its image to find recipes or check stock availability at nearby stores. Multimodal search simplifies meal planning and shopping.

The Future of Multimodal Search

As AI continues to evolve, the possibilities for multimodal search are limitless. Potential advancements include:

• Augmented Reality (AR): Users can visualize products in their space before purchasing.
• Advanced Natural Language Processing (NLP): Systems could better understand context and nuances in text and voice inputs.
• Integration with Wearable Tech: Imagine shopping directly from your smartwatch or AR glasses using multimodal search.

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