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10 Ways to Create Effective On-Site Search Experience for your eCommerce Store

Few Ways to Create Effective On-Site Search Experience for your eCommerce Store

The conventional physical stores are a classic example of the traditional salesmen modulating the search experience of walk-in customers. This not only saves a customer’s precious time but also encourages him to make the purchase, ensuring higher sales for the store.

On the contrary, an online eCommerce store doesn’t have any salesman involved. A user is on his own to find his way through the e-store to get what he really wants, resulting in easy user drop-outs when he cannot quickly locate the products that he is looking for.

According to a recent research, buyers are 90 percent more likely to use a search service compared to browsing to find a product on an e-store.

That is why offering an enhanced search experience on your eCommerce platform is crucial as it quickly leads the users to the right products, ensuring higher conversions and increased sales for your e-store.

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What does a Good Search Experience Mean?

With a better search experience, we mean that if a visitor enters “sunglasses” in the search bar and the catalog refers to them as “shades,” the search results should be able to detect the meaning and showcase accurate results.

All that is needed to understand the intended meaning behind the search and respond to it well.

If the page goes like “No results found,” you are definitely not working on search experience optimization. This will lead to visitors disappointed and leaving the platform even when you had the product in your inventory.

The experience is similar to a frustrated salesman who tells the customer that they are running out of stock or do not have a particular item even when it is lying right next to them.

Now that you understand the basic concept, you might be wondering as to how to go about creating an effective search experience. So, here are some best practices that will make for a nice search experience, thus better customer experience.

1. Embracing the Fitts’s Law

Embracing the Fitts’s Law

According to Fitts’s Law, the time needed to make the move to the target area is the ratio between the distance to the target and its corresponding width. This law can be applied to your eCommerce store for it helps adjust the search bar (placement and width) for achieving accuracy.

The idea is to make the search bar dominate on the eCommerce UX platform so that the user does not have to fight at their wits’ end to get what they want.

A simple example of an eCommerce store that implements the Fitts’s law is: Amazon. The search bar is not only close to the address bar but is spread across the width of the page and not just a negligibly visible small box at on either ends of the UI.

2. Leveraging the Power of Machine Learning

Leveraging the Power of Machine Learning

An eCommerce on-site search should be imitating the mindset of humans, only then relevant searches can be expected. That is why it becomes indispensable to leverage the power of machine learning or even Artificial Intelligence (AI) on the eCommerce store.

The AI technology has opened the door to better searches by amalgamating data originating from a keyword search, click rates, conversion rates, existing inventory tags, customer ratings, and product popularity.

This eCommerce trend supports four sublevels of machine learning in an on-site product search journey. The following illustration gives you an insight into the same:

Let’s discuss each one of them in detail.

  • Text-matching Search: This is the most basic search method deployed by most of the eCommerce website experts. Here the entered “string” is broken into “words”.
    The search results get preferences in this order: product’s title, features, description. This means that a match found using the product’s title gets preference over the match found using the feature tags.
  • Query Transformation and Expansion: This level deals with natural language processing that handles query prefixes, suffixes, and even misspellings.
    Once the user enters the query, irrespective of the wrong spelling or its distorted meaning, the system will go ahead with presenting results through fuzzy text searching.
  • Basic Machine Learning Search: This level brings personalization to the search results. If you buy a particular product frequently, the search result would automatically rank that product at number one when you enter a search result for it.
    Autocomplete is also a great way that enhances the search experience. Machine learning search predicts a query by analyzing earlier searches and online behavioral analysis.
  • Advanced Machine Learning Search: At this level, machine learning search uses neural networks to map the complex combination of words to offer an advanced and refined search result.
    Advanced machine learning focuses on learning with every interaction so that accurate and personalized experience can be offered. The result? Better conversions, loyalty, and reorders.

3. Reverse Image Search is the New Age Search Tip

Putting the keyword-based search behind, the reverse image-based search is creating a lot of buzz in the online space. With giant players like eBay, Pinterest, and Google already shifting over to reverse image searches, the scope of the technology is getting wider and wider.

In fact, according to Pinterest’s CEO, Ben Silbermann said,

“The future of search will be about pictures rather than keywords.”

Reverse Image Search is the New Age Search Tip

The process of search is simple: Take a picture, upload it, and see how similar results get displayed on the screen. This way a customer does not have to spend hours to look for a product they have in mind.

With fewer players in the market that have an active reverse image search, your e-store will be a digital experience platform that will get recognition and appreciation for its unique approach.

4. Voice-based Search Cannot be Given a Miss

From asking questions like “What is the weather like,” to asking recommendations for products and placing orders verbally; voice search is doing its bit in disrupting the online space.

Broadly termed as voice commerce, searching for products through the “voice” medium is changing the face of eCommerce stores. Moreover, the introduction of voice-based assistants such as Amazon Alexa, Google Assistant, and Siri are becoming the reason behind increased customer expectations.

A lot many brands are already using voice search for engaging customers to create their mark in the e-business world.

According to Brian Dean,

Voice searches are replacing traditional searches.

To add to the reasons for embracing voice-searches, a survey concludes that it is an opportunity to boost sales as it steps up innovation and convenience big time. Here are the findings:

As for the best example in the market, “Amazon’s Choice” tops the charts that revolve around placing orders for Choice-labeled products through voice commands given over Amazon’s Alexa.

5. Visual Search Chatbots for the Wow Effect

Voice-based Search Cannot be Given a Miss

Bots are no longer a surprise for a business as well as a user. These innovative and creative virtual assistants are already creating a buzz on the web. And, why not when bot integration promises to trim down the business expenses by as much as 8% by 2022.

Where it was thought that bots lack the emotional quotient to accurately search for the customers, the visual chatbots have turned the tables.

Fashion brands such as Levi’s and Amazon have already set an example by building virtual shopping assistants for better search experience optimization.

These visual bots can not only help in recommending fashion styles but also help with sizing and managing returns/exchanges.

There are mainly four phases that a visual chatbot conversation comprises of, such as :

  • Text to image
  • Image to Image/Text
  • Image to Smart Image
  • Visual Interaction

Each of the modes exists to enhance the search experience at any level of user interaction.

6. Implementing Faceted Search for Better Results

Implementing Faceted Search for Better Results

When a user visits an eCommerce store, he has a particular product in mind that he is looking for. If the user fails to find that particular product or even its nearly equivalents, the search experience is believed to be a failure.

This is where “Faceted Search” can save the day. The technique revolves around narrowing down the search results based on applying precise product attributes as filters (price, color, customer reviews, brand, etc).

More refined filtering called dynamic faceting further aids in concentrating the already faceted results. The thing is; by applying a particular filter, the new filters show up that are relevant to the narrowed down results.

The benefits of faceted search are manifold. Like it helps in enhancing findability, reducing the possibility of null results, improving search experience optimization, and providing valuable results.

So, the emphasis should be on including relevant and to-the-point filters alongside every search result.

7. Adopting Big Data Analytics Techniques

Big data analytics is the newest technology that is gearing towards offering a refined search experience. The technique takes user queries into consideration to decipher their intent behind the search.

Adopting Big Data Analytics Techniques

Actually, big data analytics, i.e. predictive search capabilities judge users based on the queries they make. Like what are they looking for? What is their purchase history? What did similar user personas buy? What types of products they prefer?

Once the big data goes about analyzing answers to these simple questions, business intelligence can go about offering personalized recommendations to the customers. Thus, a direct improvement to the search experience.

8. Following the Principle of “Searchandising”

When effective merchandising strategies get implemented along with enhancing the search experience, “Searchandising” is the side effect. Basically, it is about giving weight to certain products and their corresponding attributes over other products.

An example of such attributes can be stock availability, deal of the day, arrival date, trending products, click-through rates, etc.

via GIPHY

Technically, searchandising blends faceted search, autocomplete, product recommendations, recent search results, and frequently searched queries- with behavioral analysis data and automation to create a desirable search experience.

Therefore, through searchandising you will be able to deliver improved search results, provide exposure to your best-in-store products, and drive sales.

9. Convenience to Search Similar Products

A human mind is never satisfied as they are always seeking for more. Like if they come across a particular product on the eCommerce store, they would love to see other similarly priced and styled products. This makes for a variety and the flexibility to choose from among options.

Convenience to Search Similar Products

Many eCommerce platforms have started enhancing the search experience by adding a “similar product” feature both on the mobile eCommerce app and the website platform. There are many ways to do that:

  • Hovering: When a visitor hovers the mouse over the particular product, a mini screen with thumbnails appear that list similar products.
  • Long Press: When a mobile user long presses on a product thumbnail, a mini screen listing similar products appears.
  • Scrolling: When a visitor scrolls down a dedicated product page, a section listing similar products appear.

10. Looking Beyond the Search Bar

Thanks to technology, eCommerce websites have been reinventing and innovating from time to time. Where the search bar was thought to be imperative, some eCommerce platforms beg to differ.

The trend is gradually shifting to category thumbnails on the homepage that enlists the corresponding products and facets. It not only makes for a visual delight but also enhances the search experience big time.

Here’s how Artsper is doing its bit to eliminate search bar for good.

Looking Beyond the Search Bar

Another interesting aspect of this categorical distribution narrows down to “recommended categories.” Here common categories based on preferences, purchase history, and behavioral analysis add up to make for great customer experience.

Conclusion

Embedding the above-listed search experience features in the eCommerce platforms will give you the much-needed competitive edge. All you need is a great team that has the willingness to try something out-of-the-box, and some expert help to smoothly transit through the process.

Good Luck!

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Lalit Singla

About the Author

Lalit Singla is working as Project Manager at Net Solutions since the last 5 years with over 10 years of experience in LAMP Stack. His expertise lies in Enterprise eCommerce and custom database driven development. In his free time, he enjoys listening to classical music.

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