How to Predict Customer Needs with Automated Intent Extraction
How to Predict Customer Needs with Automated Intent Extraction
Intent drives sales. This is something that has been widely known in the B2C community for some time. It shows. From monitoring click behaviors to items added to carts, intent has been central to B2C sales and marketing. This intent data is their bread and butter. B2B sellers and marketers have only recently begun extracting intent data. After all, the realities are quite different, the sourcing of intent data is not as straightforward.
New Directions in B2B Sales and Marketing — Automation and Augmentation
With advancements in and increased access to AI tools, however, intent extraction for B2B sales and marketing has become as available as it is for B2C companies. Intent extraction tools can be used to measure anonymous searches to generate leads. Consider the following example. Someone searches for a marketing platform subscription. This search can be tied to the IP address of the searcher, and now you have the company name. Although the searcher is anonymous, you can impute the role that they are most likely to hold: let’s say marketing manager. You can now reach out to the lead — “I noticed that you are interested in marketing platforms” — to schedule a call and a product demo.
This intent information can be derived from page visits, searches and social media posts. With the rise of AI automation, this intent data can automatically be captured and updated so that marketing campaigns and leads can be driven by real-time insights into consumer intents. With automation, your sales and marketing teams can focus on what they do best rather than chasing down general market data and qualifying leads.
Automated Intent Extraction: A Primer
Automated Intent Extraction systems automate analysis of customer conversations based on the back-and-forth interactions with interactive machine learning. Human language learning occurs through complex iterations of back-and-forth exchange of language. AIE algorithms follow the same logic. The end result is an automated method of language analysis that follows the complexity and nuance of understanding in human language. By the year 2020, Gartner predicts that approximately 85% of customer support inquiries can be managed automatically. Intent analysis doesn’t simply afford positive customer support experiences; however, it serves as a powerful tool to understand customer needs, desires and sentiments. With well-developed AIE you can improve marketing and branding decisions, precisely personalize customer experience, effectively retarget customers and identify inauthentic communications (spam).
Let’s back up for a moment to make sure we are on the same page. Just what do we mean by intent? Simply put, intent is what the customer wants out of the contact — their aims in calling, emailing, posting on social media or interacting with chat interfaces. Intents fall into two categories: casual and business. Some examples of casual intents include greetings, thanks, affirmation and negation. Simply put, casual intents are important contextual queues that do not directly pertain to the bot’s business. Business intents, on the other hand, are intents that fall within the bot’s purpose.
Let’s look at an example. In the context of a chatbot, the intent is assigned to a user’s utterance. It is a prediction of what the customer’s aim is. If a user visits a CRM site and provides a chatbot with the utterance “I would like a quote,” AIE would assign the best corresponding intent: RequestQuote.
Entities qualify the intent with additional information. Using the above example, let’s suppose the user says “I want a quote for an annual subscription for a midsized business” The natural language processing algorithm would be able to provide total and component entity tags to specify subscription duration and business type.
AIE tools require training with real utterances relevant to the business of the bot. Relevancy is obvious: train to the intents the bot is designed to respond to. As insightful as we may be, capturing various phrasings can be difficult. Real customers will differ in terms of language proficiency, dialect and style. It is important that training incorporates the ways in which customers actually speak.
Uses of Automated Intent Extraction
Automated Intent Extraction is a robust tool that analyzes both language and interaction behaviors such as question responses and page visits. A prime example of AIE is the intelligent assistant. Intelligent assistants are like chatbots in terms of interface but use more sophisticated algorithms to respond to customer language and behavior so that customers feel that they are interacting with a person on the other end of the chat window. Using a combination of Natural Language Processing (NLP) and automated analytics, AIE interacts with customers in a natural way while providing useful insights into measures of sentiment, intent and behavior. Let’s explore a couple of examples.
Intelligent AI Assistants
Suppose a customer has questions about how to operate a new product, and they find the online forums to be difficult to navigate. Intelligent AI assistants can provide customers with instructional content in response to specific intents or provide abbreviated instructional guides or video content. If a customer simply doesn’t know how to change their KPIs, a one-page guide might suffice. If, on the other hand, the customer is completely new to your CRM, an introductory how-to is most appropriate. AIE is oriented toward personalization, or the right solution for the problem at hand, not a scattershot approach. You can be sure that the customers who feel like you know and understand them will be delighted and loyal brand advocates.
Inquiry Prioritization
It isn’t simply the customer-representative relationship that benefits from AIE. Every aspect of the customer experience is improved through intent analysis. Prioritization of resolutions can also be automated, so that customers with the most urgent needs can reach resolution as quickly as possible. Imagine that you have a customer who intends to make a complaint about the build quality of a product and a customer who intends to make a complaint about a damaged shipment. While both intents are categorically identical, the needs for resolution differ significantly. If the customer with the damage complaint is forced to wait a minute more than needed, the relationship and the brand perception is tarnished. If the customer with a build quality complaint receives a follow-up in a few days, it is unlikely to affect their perception of the brand or product. Targeting the most urgent inquiries automatically ensures that resources are directed in the most efficient ways.
Intent Prediction
Intent analysis isn’t just rooted in present customer intents. By understanding the intents and behaviors of existing and past support inquiries, future customer intent can be predicted based on their characteristics and behaviors. Knowing what customers will want, has great potential to increase sales, accelerate individual purchase decisions and improve customer satisfaction. Consider the following, a customer has visited your site a few times and explored some product content. You may not know the propensity of conversion from just this information. Automated intent extraction can match behaviors such as which pages are visited and how long a customer interacted with content compared to other past customer behaviors and characteristics to then determine the right content, the right ads and ad placement and the right product are surfaced. This curated experience can improve customer support by offering personalized support interventions such as additional content specific to user behavior, or even scheduling a call with a representative with a personalized prompt.
Automated Intent Extraction can help you retain customers by showing them that you understand what they are asking for and that their most pressing concerns matter. Through automation, we move away from a conception of the buyer’s journey as homogeneous. There is no one size fits all story. Automation ensures that the details are captured in real time so that you can understand your customers as individuals.
Impressive Advantages of Automated Intent Extraction
Integration
Automated Intent Extraction helps consumer businesses satisfy their customers by offering excellent client support. It makes the entire support system more efficient and operate flawlessly without the need for employees to do repetitive and uninteresting tasks. AIE technology easily integrates with your present customer support platforms, eliminating the pains of large-scale roll-outs.
Automation
AIE significantly reduces customer support costs by offloading repetitive tasks to computers while improving the smoothness and efficiency of support interactions. In addition to improved allocation of labor, AIE improves customer satisfaction through nuanced, personalized customer knowledge. Automated insights improve business decisions by locating strengths and weaknesses in product and brand reception.
Maintenance
AIE keeps on learning from the data that is currently being running on the system. In a nutshell, all you require is historical data to get AIE up and running, after which it continues learning using existing data sources. That way, once it is implemented, it simply gets the job done.
Learn More About Intelligent Automation Solutions
Automated Intent Extraction is one of many effective applications of intelligent AI. To learn more about intelligent automation applications for marketing, sales and other use cases, read our recent blog posts. Whether you’re looking for a no-code intelligent automation solution yourself or the right information to evangelize and train your workforce for this fourth industrial revolution, taking the time to understand intelligent transformation is an important first step.
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