Search is the answer to all your complex problems in this digital world. From the earliest days of the internet, people have been trying to find relevant content and products by typing the query in the search bar. However, with the continuous rise in the usage of chat and voice-enabled virtual assistants over the past few years, it has become imperative for search solution providers to go beyond the traditional keyword-based search that lacks the understanding of context and delivers irrelevant search results.
Estimates suggest that the number of digital voice assistants in use will rise from 4.2 billion in 2020 to over 8.4 billion in 2024 globally.
Conversational search overcomes the limitations and challenges of traditional search and offers more natural, contextual, and human-like responses to search queries. Let’s try to understand what conversational search is.
What is Conversational Search?
As the name suggests, conversational search refers to searching the query using natural-sounding language or phrases rather than keywords and providing results to search queries in a more humanized and conversational way. Conversational search leverages AI to understand the customer intent from chat or voice utterances and returns contextual and personalized search results accordingly.
Here is an example of a traditional search query:
Credit card options
In contrast, a conversational search query may look like
Can you suggest credit card options?
Conversational search understands the context from current and previous interactions and provides more relevant, meaningful, and comprehensive search results that enhance the search experience for customers and employees.
Why is Conversational Search Important?
Some of the important benefits of conversational search include:
- Reduced search friction: Conversational search provides users with the most relevant and contextual results in the first interaction, reducing search friction.
- Improved engagement: Conversational search allows users to find products or content quickly, which leads to increased engagement.
- More personalized interactions and results: Conversational search changes users’ interactions from transactional to personalized.
- Better product recommendations: Users receive better product recommendations based on their needs and preferences.
With the increased usage of chat and voice assistants, there has been a substantial transformation in how users search for products online. Conversational search is increasingly playing a significant role in elevating the overall experience for users in
- E-commerce – where product search, discovery, and recommendations are hyper-personalized for consumers and shoppers.
- Digital Workplace – where information discovery is contextual, relevant, comprehensive, and unified for knowledge workers.
In the three-part blog series, we will deep dive and explore how conversational search brings unique strengths and significant value to each use case. Let’s first try to understand these use cases in the context of conversational search.
Why Conversational Search Matters for
E-commerce
Search is a critical component of any e-commerce site. It’s an important tool that allows consumers to find the product that they are looking for.
Think about the last time you searched for a product on an e-commerce site. Have you stumbled upon hundreds and thousands of options for your search query with sponsored ads and technical jargon? In short, would you like to spend time and purchase a product from an online portal that will provide irrelevant search results that are too significant in numbers?
Conversational search removes all the obstacles and bottlenecks of traditional search and provides a delightful experience to shoppers by providing relevant, personalized, and contextual search results.
AI-powered chat or voice-enabled search assistants facilitate conversation by intelligently understanding the context and guiding the customers to the right products via text-based dialogue or rich UI elements like information panels or carousels.
All of this helps in shortening the search cycle, which in turn, reduces bounce rates. Moreover, the machine learning capabilities of AI-powered search engines comprehend the needs of customers in a much better manner. An AI-powered search and recommendation engine can empower e-commerce brands to deliver personalized product recommendations and a world-class search experience to consumers, reducing the cart abandonment rate, increasing shopping cart value, and boosting online sales.
Why Conversational Search Matters for Digital Workplace
Though digitization of the workplace brings a lot of advantages, it comes with various challenges like managing growing data, the number of files, documents, emails, etc.
80–90% of all new enterprise data is unstructured and growing 3X faster than structured data.
On top of this, the problem exaggerates when employees are unable to find the relevant information in a quick time. For an employee, it’s easy to gather the information or document they are familiar with or when they are aware of the file location — the time in searching or discovering the information increases for unfamiliar files or records residing in different applications, systems, or portals across the enterprise.
The knowledge worker spends about 2.5 hours per day, or roughly 30 percent of the workday, searching for information.
Kore.ai’s intelligent knowledge search assistant with self-service options and conversational capabilities can address these problems. It understands the user’s intent based on the natural language search query and assists employees to resolve their queries by smartly matching their questions to digitally stored information on HR, IT, Legal, Marketing, Finance, or Travel articles.
However, there are scenarios where employees and customer support executives would like to utilize the pertinent information available in multiple places, like technical documents, videos, blogs, etc. In such cases, employees can discover the relevant documents by typing a query in the traditional search bar. The cognitive search engine with artificial intelligence and machine learning capabilities accurately understands the context of current and previous search queries. It provides a unified search experience by retrieving the most relevant search results in a single query from disparate knowledge sources across the enterprise, including collaboration tools and platforms, intranet portals, emails, forums, and shared repositories, to name a few.
While enterprise search tools enhance employees’ experience by quickly providing search results, it’s not sufficient to discover and show the same results to every user. An AI-powered enterprise search and recommendation engine leverages ML/NLP capabilities to understand the query’s intent and context, offer personalized search results, and document recommendations based on the employees’ roles, locations, interests, and preferences.
Reach a Wider Audience with Conversational Search Strategy
By now, you should have a brief idea of why conversational search matters for businesses worldwide. We live in an era where a large population, especially millennials, are using chat and voice assistants while shopping or searching for product information, and this will continue to rise. For consumer or employee-focused brands, it’s time to develop and adopt a conversational search strategy for allowing their products, information, and content to be discoverable through chat and voice assistants. Moreover, a successful conversational search experience helps brands increase customer engagement, improve employee productivity, and increase sales and revenue.
Are you planning to implement the conversational search strategy to elevate digital commerce and workplace experience? Kore.ai, a leader in Gartner Magic Quadrant for Enterprise Conversational AI Platforms 2022, is happy to help you with any of your conversational search needs. To learn more about Kore.ai SearchAssist, an AI-powered cognitive and conversational search assistant, please visit our SearchAssist product page or request a demo.
