How Search Relevance has Empowered the E-commerce Sector

Search relevance is a known phenomenon. It has powered user search on some of the major search engines and ecommerce web portals of the world. Enabled by powerful technologies to accumulate and offer best product recommendations and product experiences, AI powered search relevance has unfolded a whole new realm for making purchase easier and efficient.

Artificial Intelligence, E-commerce and Search Relevance

Incepted majorly to offer products and services at the doorstep of customers through smartphone technology, the ecomm sector offerings are dissected to provide best online purchase experience to customers. The ecommerce retail purchases have grown in 2020 (slowed down by pandemic) yet show a steady growth of $4.89 tn in 2021 and further.

In the context of the e-commerce sector and customer satisfaction, it would be evident to relate the quote - “Your most unhappy customers are your greatest source of learning.” which aptly explains how tapping onto customers who abandon carts or abruptly leave the website without making a purchase are the greatest source of inspiration for creating cutting-edge product development and ecommerce experiences.

To begin with, every ecom portal is backed by a recommendation engine that provides customized results to online visitors when they visit the portal. Along with this, the engine supported by AI backed applications for making customer searches more relevant and related to what they are looking for. For example, when a customer is searching grocery then search can help them with suggesting brand and the prices as per the recommendation engine algorithm, powered by machine learning algorithms. Advanced search relevance techniques are able to interpret best suitable search keywords and make appropriate suggestions to visitors.

Types of Recommendations

Making use of word embeddings, tokenization, and essential vectorization - ecommerce search helps users reach their desired products in as few steps as possible. These practices work from within the deployed recommendation system. In the case of a retail store available online, such experiences have led to increasing business growth, reduced churn and improved retention rate on ecommerce web portals.

We can see three primary types of engines in search relevance:

1. Collaborative: This works as per user-item logic. Users with shared interests will get similar kinds of recommended suggestions. This type is also referred to as neighborhood relevance.

2. Content based: Content based type works as per the chosen products and history of the user. It is purely content based and driven by data classification techniques.

3. Clustered: Similar to collaborative, the recommendation system works as per cluster approach. Users are divided into clusters and the engine makes use of data to provide appropriate suggestive items.

Search Relevance

The adroitness of the existing recommendation system based on the results it produces with relation to traffic generation, and rise in engagement ratio. Hence, on the fundamental basis of the platform, adopting an apt recommendation system for search holds the key.

The NLP and Machine Learning Part in User Searches

Essentially, it is a natural language processing task. NLP techniques such as information retrieval and named entity help in displaying the suggested keywords based on which the user can select and land on the related product page for purchase. This helps in reducing exits without purchase and greater engagement by retargeting other relevant products on the same page, itself.

Also Read: NLP or Natural Language Processing As Machine Learning Approach

Similarly, machine learning algorithms such as decision trees, naive bayes and support vector machines, which are widely utilized in textual data intensive tasks are also applied in building search relevance models for ecommerce stores. These algorithms empower the e-commerce recommendation system with capability to lift the most relevant, search and historically significant search words for every visitor. To add, from other ML approaches, Adaptive Boost or Adaboost uses an iterative process for arriving at the most accurate classifier; indicating highly accurate keywords for visitors to select from, leading to better conversion rate for purchase.

For Ecommerce: What to expect next

With Artificial Intelligence joining in to make basic search more interesting by recommendation system having machine learning models with training datasets, search relevance has diversified from its entity. Rapid changes can be seen in the sphere of natural language processing techniques with new updates being introduced for increased adaptability on user platforms.

In terms of the E Commerce sector, expect more new approaches in the coming, importantly around the capability to provide more control to users on what they want to see, select and buy. Advanced user profiling will become crucial amid this, at the backend. The algorithms will become more data-intense and help the NLP framework to figure out behavioral patterns based on semantic web behavior of users.