Transforming E-commerce with Natural Language-Based Discovery to Drive Decision-Making

E-Commerce searches often have less relevance and take a lot of clicks to filter down to the product of choice. Decision-making ends up in confusion despite spending hours of research online/offline. This causes sellers to lose high intent shoppers and leads to impact on conversion, AOV and results in abandoned carts. The In-Store experience of salesmen helping customers with guided discovery, contextual product recommendations and dynamic real-time promotions/discounts based on buyers' intent is missing from online shopping.

SeekNShop aims to bring this experience to online/mobile for e-commerce businesses which will improve sales metrics. With high engagement and user interaction apps being voice-enabled virtual agents and chatbots/chat applications, the world is moving towards Natural language human-like conversational interfaces.

The company is aimed at addressing the discovery and decision-making gaps in most shopping and enterprise domains (eg. e-commerce, automotive, real estate, finance, pharma, healthcare).

S S V S Sarath founded SeekNShop, an Artificial Intelligence-based company that offers e-commerce and other shopping domains (travel, recruitment, finance, automotive (cars), etc.) with the ability to perform natural language-based discovery to drive decision-making. Its flagship products include:

• DeepSeek which is a natural language-based text/voice search service that is pluggable into any website/app or kiosk for any business domains.
• Chabot which is integrated with search and provides a chat-based search and conversational recommendations.
• Recommendation Engine for Car Discovery.

SeeknShop.IO also provides e-commerce specific services such as inventory insights (which brands to focus on or which categories convert more etc.), search merchandising, ability to customize search, search experience and visual search. The company specializes in Enterprise Search and Domain Language Understanding.

In addition to SeekNShop, Sarath has founded another startup called IntentBI (in Private Beta / Stealth) which offers Natural language and Voice-based Reporting and eliminates the dependency on SQL developers or Report Builders. This product helps any SME/enterprise to just upload a flat file of data (TXT/CSV/XML/JSON) of any number of columns and rows and start asking queries in their natural language (English/Spanish/German etc.,).

Here quoting an example Sarath says, a physical retail store can upload with their Financial data and ask queries like "Find the top 10 stores in Eastern US where GMV greater than 20 million USD and Sales Quantity above 5000 units sorted by GMV as a pie chart" and a chart will be generated in <0.1 seconds.

Also, the complexity of presenting search, contextual recommendations to help users buy coupled with a real-time, buyer aware promotions as well as real-time user understanding for sellers is the need of the hour and the company reckons it is targeting the right segment i.e., e-commerce & online travel.

Sarath believes in bringing the best of the in-store experience to digital shopping while leveraging the power of data from the digital world.

With IntentBI, this eliminates the delay in decision-making caused due to lack of instantaneous actionable insights as there is a dependency on developers and various complex reporting systems to get the required analytics and reports at user's disposal. It would also reduce the cost of hiring report developers and SQL developers for instantaneous reporting by making it Natural Language Driven Data Reporting.

Leveraging AI to Bring the Best In-Store Experience

Shoppers prefer physical store experience due to the human interaction, assistance and support offered by the salesman. The best in-store experience is always the one in which the shopper gets personal attention from the salesman, guided product discovery, context-based recommendations (when there is confusion or suggestions for alternatives), product/customer support and a chance of getting a bargain/discount based on the shopper's affinity to buy.

This is a common customer pattern while shopping but this isn't possible in e-commerce just because of the sheer scale to be catered to (millions of users active), the cost involved in employing humans for providing similar support as above, training them regarding various product options, etc.,

SeekNShop.IO has a goal to re-create this best in-store experience leveraging AI and the power of live conversations of customers with some patent-pending techniques. Based on its current traction, the company sees that it has a product-market fit primarily with medium to enterprise-level e-commerce customers as this is an age-old customer experience pattern being enabled on online/mobile channels leveraging best in class algorithms giving near human experience.

Most of its customers have seen an uptick in conversion by more than 20-50 percent and its accuracy rates for various user queries have been >99 percent with an average response time being 53ms. With SeekNShop's high speed and high accuracy search, businesses have improved their ROI and conversions considerably in addition to achieving great user satisfaction and user experience.

Enabling Conversations Seamlessly

The core of what SeekNShop.IO offers is the Domain Language Understanding algorithm and the overall intent matching and noise elimination. All these algorithms and design approaches are the core of what shaped the product SeekNShop.IO offers and benefits customers. These algorithms help achieve a seamless conversational experience for end-consumers given the mechanism of how Natural Language search product understands heavily unstructured conversations/queries and eliminates noise and identifies the right intent to provide highly accurate results.

The core of what IntentBI offers is the Voice-based Reporting and being able to understand the volumes of flat files of data and still be able to provide accurate results just like a standard scripting language like SQL even though the queries are in Natural language and completely unstructured. The core of this also has an NL to SQL engine and NL to DataMap Engine that understands any kind of data and is able to allow filtering, sorting and querying on top of that data.

Rising Above the Challenges

According to Sarath, challenges with any innovation is to find an appropriate business value, product-market fit and be able to market the same with ample amount of customer reach which the company has been working on through a variety of partner ecosystem across industries. Also, the high accuracy and precision of the Natural Language search needed a lot of customization for customer requirements given some of them wanted a good recall (i.e., some related results to show up and not only the take accurate ones). Also, SeekNShop.IO had to fine-tune and enable a lot of customizations into its product to enable and handle merchandising needs of the customers which eventually helped it in becoming a complete product which is required by the market.

Significant Moves to Thrive in The Market

SeekNShop has partnered with various e-commerce and automotive agencies and platforms along with other strategic marketing and sales partners to help it reach to its target customer base. The company is also venturing out into other segments through similar partnership models to help itself with maximum outreach.

A Seasoned Innovator

Sarath has over 13 years of experience in Machine Learning (ML), Data Science and Product Management and comes with a vast enterprise, consumer experience while working with Microsoft, Amazon, CA Technologies and few startups.

He has a strong vision towards enhancing digital lives and improving discovery and decision-making needed in companies' daily tasks leveraging Data and AI.

Sarath currently holds 4 patents and filed 20+ patents for the company. He primarily takes care of Product Vision, Strategy, Roadmap, Sales and Customer Engagement apart from defining all the cultural and corporate aspects of the company.