“If you don’t understand your customer, someone else will.” – Jeff Bezos
Business, esp in retail, has always been about knowing what the customer wants—sometimes even before they do.
Whether it’s that store assistant who remembers your favorite fabric or an ecommerce brand that suggests just the right configuration of smartphone before you even search for them, the game is the same: anticipate, personalize, and delight. . .
But …customers today are impatient.
60% of shoppers abandon a site if they can’t find what they’re looking for within 30 seconds.
And with over 2.64 billion people shopping online, brands don’t just need to “know” their customers they need to predict, personalize, and guide them effortlessly through their shopping journey.
Is your Ecommerce Search Strategy – Seamless or Sluggish?
Modern customers are accustomed to seamless search and getting instant results – typing into a search box/search engine or conversational search on Amazon/Youtube/Whatsapp apps and finding what they want without much efforts or the need to type or say accurate search phrases.
Now if such a customer is presented with an outdated search feature where he/she is expected to put extra effort into searching with the exact product name, and search term, you are bound to lose that customer.
According to research from Baymard, 60% of sites “require their users to search by the same product type jargon the site uses.
Now look at this Forrester research that says 68% of consumers who have a bad experience with a website’s on-site search are likely never to return to that website again.
Now connect the dots… you got that right!
Its time to make your website/storefront’s search more intuitive and smarter so that it can help customers with precise product/information discoverability.
This is where advanced tech such as AI and Natural Language Processing (NLP) is changing the game.
Natural Language Processing (NLP) is at the core of all the conversion-driven search, hyper-personalized solutions that we get to experience today .
So, how exactly is NLP making ecommerce more customer-centric you ask? Let’s dive in.
Tracing the Evolution of Natural Language Processing through Time Travel
Before proceeding to understand how Natural Language Processing (NLP) is redefining the rules of CX in ecommerce and retail, let’s do a time travel to understand its evolution:
Joseph Weizenbaum with his experimental computer program ELIZA, considered as first-of-it-kind chatbot( Source: Gluekit)
1954 |
One of the earliest demonstrations of NLP/ machine translation – The Georgetown-IBM Experiment in 1954, translated Russian to English using a predefined set of rules. While limited, it showcased the potential of automated language processing. |
1966 |
Then in 1966, Joseph Weizenbaum, designed a computer program called ELIZA , first one of its kind, to understand the man-machine interaction in natural language! A rule-based chatbot designed to simulate conversations with a therapist, ELIZA could respond to user inputs using scripted dialogue patterns. While it lacked true understanding, it laid the foundation for conversational AI. |
Mid-2000s: |
The Introduction of Word Embeddings around 2000s enabled NLP models to grasp relationships between words, enhancing search relevance, and sentiment analysis. |
2010 |
2010 marked a significant leap in the world of NLP with the rise of Recurrent Neural Network Language Models (RNNLMs) since this set the tone for remembering previous information, and contextual understanding by the models. |
The evolution of Natural Language Processing (NLP), as we saw, from simple rule-based systems to sophisticated models capable of understanding context, sentiment, and intent was no less than magical!
NLP: Making Customer-Centric Retail Experience Possible
Today, Natural Language Processing (NLP) is a key foundation of customer-centric retail, enabling ecommerce platforms to analyze, interpret, and respond to consumer needs in real-time.
Here is a look at how NLP is transforming retail and ecommerce experience for users:
Making Search more impactful: One of the best use cases of Natural Language Processing is to make ecommerce search experience more impactful and accurate for the users. How does NLP ensure this? Let’s understand different phases through which modern NLP models make search so impactful:
- Interpret user intent: NLP uses semantic search to identify real meaning and intent behind his/her query.
NLP goes beyond mere keyword tagging to analyze and recognize synonyms, related terms, and varied phrasing, and fetch results based on sematic similarity.
Even if the search term entered by the user doesn’t comply with the industry standard or is complete.
If a grocery storefront’s product listings use the term “organic Fuji apples” or “organic red apples”, with NLP you can ensure that these products still show up, even if the user didn’t type the exact name.
- Dynamic Faceting & Re-Ranking: NLP-driven AI search systems can adjust filters and search rankings in realtime based on a user’s browsing behavior and search intent, ensuring they see the most relevant products first.
If the shopper frequently buys “vegan soy-based milk”, the search can prioritize showing those first in their results, making the experience more tailored.
- Proactive Auto-Suggestions: NLP can refine and predict search terms based on trending searches, user preferences, and common phrases, reducing search effort.
NLP can also be used to personalize results by showing customer-focused smart product recommendations based on past searches and browsing history.If the customer just types “apple”, NLP-powered search can predict intent by considering past behavior and common queries, offering filters like “organic,” “bulk packs,” or “freshly stocked” before they even finish typing.
- Smart Product Tagging & Categorization: In retail and ecommerce, accurate product tagging determines the efficacy of how accurately the products are searched for and ultimately discovered by the users.
NLP extracts key features and attributes from product descriptions, automating product tagging and improving discoverability.
How Adobe Commerce (Magento) Uses NLP to Boost Ecommerce Success
Leading ecommerce platforms like Adobe Commerce (Magento Commerce) are integrating NLP to deliver a more conversational search experience.
Adobe Commerce’s Live Search feature, powered by Adobe Sensei, utilizes NLP to understand the context and intent behind search queries, delivering more accurate and relevant results.
NLP Drives Inclusion & Expanded Reach, Really?
One of NLP’s most transformative capabilities is making digital solutions more inclusive.
Breaking Language Barriers: A study by CSA Research found that 76% of consumers prefer purchasing products with information in their own language. NLP-driven translations and localized search capabilities help businesses cater to these audiences, significantly improving accessibility.
Enhanced Reach into the untapped market: Merchants and brands leveraging NLP for multilingual and regional-language support can tap into previously underserved markets, expanding their reach and revenue potential.
Ecommerce giant Flipkart acquired 20% new customers after they introduced support for around 12 vernacular Indian languages, making them accessible and shoppable beyond the metros.
Roadmap to Implement a NLP-Powered Search for Your Storefront :
Implementing advanced NLP capabilities like smart search and product discoverability requires strategic planning and expertise.
Are you too looking evaluate and leverage AI-enabled search and product discovery for a more customer-centric ecommerce experience? Then Embitel’s ecommerce consultant, with 18 years of industry experience, can definitely offer assistance. Drop us a mail for a 1-on-1 consultation.
