Shift from Search Engine to Intent Engine
In the current times where the customer’s digital activity has taken a significant leap, the traditional businesses have also moved over to the virtual channels. In the Insurance Industry, the entire customer journey can be reimagined into a more efficient model leveraging advanced technical assets. This has created an opportunity for the Insurance companies to expand their market, streamline their processes, enhance their productivity and even reimagine their strategy.
Adding another Parameter to SEO Process: Intent
The first stop for most of a consumer’s queries today is the Web Search Engine, which serves as a hub for information flow and from the perspective of businesses, a channel for online discovery. The process is as simple as typing your query into a search engine and getting hundreds of links of relevance by recognizing the keywords. Since the consumer query is in natural language and often plain English, another dimension to this query resolution process is understanding the intention behind it. This is where Intent Engines come into picture.
Simply put, Intent Engines identify the user’s intention and optimise the search process. The intent can be categorised into three broad categories:
· Navigational: The reason behind this query is to go to a specific website or source
Ex: “Customer Login for XYZ Bank”
· Informational: Where the consumer is seeking some information about something
Ex: “Best health insurance plans”, “Agent near me”
· Transactional: When the purpose is to buy something
Ex: “Buy a life Insurance”
Since the customer’s reason for each of the above queries is different, the outcome would also be different, best suited to the objective they are trying to achieve.
Data to Intelligence to Business
The insurance industry is driven by risk assessment. To efficiently perform their tasks of lead generation, customer meetings, risk analysis and making a sale, the agents obtain data on customers by observing them to decide how to proceed further. In the absence of physical meetings, the agents need an alternate set of data points to strategize their approach. In today’s digital scenario there are numerous avenues of a customer interaction: searches, websites, social media, maps etc, which create a digital demographic, that can serve as an input to the insurance agent.
In addition to observing patterns of customer interaction, recognizing different elements of incoming request serves as input to the agents. Some examples of queries are:
“Find me an insurance agent who speaks Mandarin”
“Agents near me that cover life insurance at best interest rates”
Each of these queries have multiple attributes like:
· Primary Focus: “Insurance agent”
· Product category: “life insurance”
· Language: “Mandarin”
· Ratings: “best”
· Geography: “near me”
Customers are inclined to choose the agents that answer their queries best. Hence it is essential to structure all the information with detailed attributes into a single place. These details help not just connect to the customer but also establish the credibility of the business vis a vis your peers, which can translate to higher conversion rates.
AI Backed Knowledge Graphs
Search engines have started leveraging AI to answer multidimensional user queries like:
“Find an insurance agent near me that covers life and health and who speaks French”
To be able to answer this query, data from multiple sources is needed. While search engines leverage knowledge graphs to best answer this query, businesses need to build their own knowledge graphs that can respond to such queries.
A knowledge graph is a structured database that stores data in a variety of formats from multiple sources. Each organisation needs to create its own knowledge graph and store all its data in a single source, with all the attributes and facts about the business mapped to each other. This allows the business to maintain, update, scale and respond to customer in an efficient and timely manner.
Leveraging AI Backed Intent Classification in Insurance Industry
Insurance Industry has a large number of datasets: Insurance Agents details, Product Portfolio & Services, offices and branches, customer data, market trends and regulations etc. Each of these data bucket can be a potential knowledge source that the company can leverage.
First, structuring and detailing business facts result in an increase in the website views, hence increasing the Online Discoverability of the business.
Second, using data tagging and AI powered classifier, streamlines the query allocation and resolution process by improving the delegation process and eliminating manual sorting. This results in two things:
· Precise allocation of resources in an automated way, allowing the agents to focus on the customer
· An improved customer analysis, positively impacting the Lead to Conversion ratio
Third, data engines are being used to create a 360-degree view of each customer encompassing: customer behavior, preferred channels, financial history, behavior and risk profile.
AI powered systems are not just a case of innovation but are becoming an integral part of the digital ecosystem of businesses and the companies stand to gain not just market advantage, but also tangible business results.
