how conversational insights take the guesswork out of marketing

For more than two decades, Google and other search engines have attempted to crack the consumer intent code. The entry point for a search marketing campaign is the keyword list. Yet keywords—whether spoken or typed—represent the tip of the iceberg when it comes to understanding what a user wants. There’s no way to clearly measure (or identify) user intent, but 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. 

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.

Asking a search engine or virtual assistant a question is the beginning of a conversational journey that carries the searcher across channels until they ultimately find what they want (or not). 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—from the search engine’s perspective—is over. 

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, using conversational analysis to gain an understanding of customer intent that goes far beyond keywords. 

Content created in partnership with Spectrm.

The customer conversation is online

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 each day—40 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 chat an average of 20 minutes per day in 2020, with Business Insider predicting that the average would grow to 24 minutes by 2021. Interacting with chatbots is a natural extension of consumers’ comfort with messaging in social media apps like Facebook and Instagram.

Increasingly, messaging is how we connect with each other. Facebook and Instagram are at the center of this trend. Businesses have the potential to reach and engage with over two billion people on Facebook and Instagram using their respective 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. This is next-level chatbot technology that 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.

Spectrm 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. Generic conversational AI uses general NLP that can be used for simple tasks like autosuggestions and basic keyword matching. Domain-specific NLP is trained for the individual business. Spectrm’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 quickly generate their own data sets to train the algorithm.

“Marketing chatbots that use domain-specific NLP learn how your individual customers speak. The customer intent data specific to your business, customers, and goals are used to continuously improve your chatbot. 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 Spectrm

Chatbots are only part of what makes conversational marketing platforms work. Platforms like Spectrm 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 much more in-depth intent data than a simple keyword search. Consider the follow 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 the context of how they’re interacting with the chatbot.

Conversational data also enables businesses to create customer profiles using the answers people provide in chat. Personalization and segmentation become easier based on the granularity and specificity of conversational data. This information can be used to personalize marketing messages at a one-to-one level directly in chat. 

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 the 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 makes it possible for companies to build chatbots that engage and convert customers on the websites, apps, and social platforms where people spend their time—no engineering resources 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 datapoint 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.”

To view Spectrm’s offerings, click here.