how conversational insights take the guesswork out of marketing

Google and other search engines have attempted to crack the consumer intent code for over two decades. The entry point for a search marketing campaign is the keyword list. Yet keywords—whether spoken or typed—represent the tip of the iceberg for understanding what a user wants. There’s no way to measure (or identify) user intent. Still, Google is getting better at figuring out what a user wants with technologies like Google Hummingbird, an algorithm update they rolled out in 2013. Google introduced Hummingbird in response to the increasingly conversational nature of search queries.

Asking a search engine or virtual assistant a question begins a conversational journey that carries the searcher across channels until they ultimately find what they want (or not). Per a 2013 article in Wired, “Google is now examining the searcher’s query as a whole and processing the meaning behind it.” In January 2020, Statista reported roughly 40 percent of US search queries contained four or more terms. Keywords pull the curtain of intent back, but they only provide a glimpse of the customer journey, labeling the searcher’s thoughts without revealing the “why” of what they’re searching for.

Once a user clicks on a search result, the conversation is over from the search engine’s perspective. But thanks to advances in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI), businesses have access to a much deeper understanding of what consumers want across the entire buying journey. AI-powered chatbots that “speak” to consumers can collect customer intent data and take the conversation beyond an initial keyword query. They enable businesses to leverage that customer intent data instantly to scale one-to-one personalization in direct chat. Below, we discuss how conversational marketing platforms employ NLP and AI in chatbots to guide customers through the buying funnel. We useg informal analysis to understand customer intent that goes far beyond keywords.

The customer conversation is online.

Interacting with chatbots is a natural extension of consumers’ comfort with messaging in social media apps like Facebook and Instagram. According to Hootsuite’s Digital In 2020 report, 60 percent of the world’s population is online. The report found that, globally, users spend an average of 6 hours and 43 minutes online edaily40 percent of their waking life using the internet. A large chunk of that time, more than two hours, is spent using social media. Consumers were using mobile messaging and chatted an average of 20 minutes per day in 2020, with Business Insider predicting that the standard would grow to 24 minutes by 2021.’

Increasingly, messaging is how we connect. Facebook and Instagram are at the center of this trend. Businesses can reach and engage with over two billion people on Facebook and Instagram using their messengers. This level of engagement gets to the root of consumer intent, diving beneath surface keywords to the conversational data that can help companies understand what’s motivating the consumer to conduct their search in the first place.

Leveraging Conversations to drive results

Conversational marketing platforms use messaging apps to engage with consumers and determine intent. Next-level chatbot technology uses AI to create a two-way exchange with every customer, asking them questions throughout the buying process and capable of operating on multiple messaging channels. Spectrum is an example of a conversational marketing platform that goes beyond simple, generic approaches to conversational AI by using domain-specific NLP to guide consumers through the customer journey.

Spectrum’s approach to conversational AI combines domain-specific NLP with the use of generative adversarial networks, a type of machine learning that enables enterprises with little or no customer intent data to generate their data quickly sets to train the algorithm. Generic conversational AI uses general NLP that can be used for simple tasks like autosuggestions and essential keyword matching. Domain-specific NLP is prepared for the individual business.

“Marketing chatbots that use domain-specific NLP learn how your customers speak. The customer intent data are specific to your business, customers, and goals and are used to improve your chatbot continuously. It’s about understanding how your customers engage naturally with your brand and training your bot to respond to that to drive outcomes valuable to your business. Even if you don’t have a lot of conversational data to train your bot.” – Writes Spectrum.

Chatbots are only part of what makes conversational marketing platforms work. Platforms like Spectrum operate across multiple messaging channels where consumers spend all their time, including Facebook Messenger, Instagram Messaging, Google Business Messages, and even at the display level via conversational display ads using AdLingo and Google DV360. Consumers like chatting with businesses. They’re already moving through the buying cycle using one-on-one conversations that provide more in-depth intent data than a simple keyword search. Consider the following statistics:

• 75 percent of consumers prefer to engage with brands in private messaging channels versus traditional channels
• 65 percent of people are more likely to shop with a business they can reach via chat

Conversational data = More targeted campaigns

Conversational data can be used to create marketing campaigns that are more targeted than traditional search and display campaigns. They enable businesses to design targeted messaging around the customer journey, learning what customers want/need in how they interact with the chatbot. Conversational data also enables businesses to create customer profiles using people’s answers in chat. This information can be used to personalize marketing messages at a one-to-one level directly in chat. Personalization and segmentation become easier based on the granularity and specificity of conversational data. None of this is possible without the right platform. Some factors to strongly consider while evaluating an enterprise-level conversational marketing platform would be:

• An easy-to-implement no-coding setup
• Customizations for your specific company and customer needs
• Easy integrations with your tech stack
• Enforcement of the highest privacy standards (GDPR, CCPA, and others
• Connection to your product feed (for ecommerce websites) and ability to serve product recommendations/content in real time based on user input
• Flexible role management with the ability to set user access roles

Tools like Spectrm are at the heart of marketing automation, enabling companies to acquire new customers at scale. A robust conversational marketing platform allows companies to build chatbots that engage and convert customers on the websites, apps, and social media where people spend their time—no engineering resources are needed. Just like search engines, conversational intelligence tools effectively use language to get to the heart of consumer intent.

They go beyond keywords to make every data point actionable, using chatbot analytics to optimize funnels and segment customers. In Spectrm’s words, “Reaching the right audience is getting harder every day. Consumers are more curious, demanding, and impatient than ever. They expect their digital experiences to be personalized, instant, and effortless. Chatbots enable brands to connect with their audience personally and offer seamless customer experiences from the start.”