Conversational Analytics: The Future of First-Party Marketing Data

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Conversational Analytics: The Future of First-Party Marketing Data

As new regulations like GDPR and CCPA put a stranglehold on the use of third-party consumer data sources, making sure you have access to actionable first-party data is more important than ever. While you probably have access to data like customer purchase history, website and other digital journey information like paid search interactions, there are limits to how much insight marketers can gather from these sources.

Conversational analytics represents one of the last bastions of precise first-party insights into how your customers interact with your brand, how they think of your product or service, and perhaps most importantly, exactly how they talk about it.

What is Conversational Analytics?

Conversational analytics is the process of extracting usable data from human speech and conversation using natural language processing (NLP) to allow computers to “understand” speech and artificial intelligence (AI) to extract and organize data from it. I know, that’s a lot of alphabet soup to deal with, but what conversational AI boils down to is giving machines the ability to process speech and allowing people to gain insights from massive numbers of conversations at scale — both of which were daunting if not impossible tasks just a few years ago.

Conversational analytic tools are used to extract and process data from both spoken speech (e.g. phone calls and voice assistants) and typed speech (e.g. customer service chatbots). The applications are myriad, so we will stick with our area of expertise and show you how it’s used in call tracking software that allows marketers to understand call context, predict outcomes, and apply the data to optimize marketing campaigns and improve customer experience.

What Are the Benefits of Conversation Analytics?

Benefit 1: Meaningful Customer Insights 

Conversational analytics provides deep insights into customer behavior, preferences, and sentiment by analyzing conversations across various channels. By analyzing the tone, language, and context of interactions, businesses can gain a comprehensive understanding of their customers' needs, pain points, and expectations. This knowledge enables companies to tailor their products, services, and marketing strategies to better meet customer demands, ultimately leading to improved customer satisfaction and loyalty. To quote the classic 80s cartoon, G.I. Joe, “Now you know. And knowing is half the battle!”   

Benefit 2: Real Time Feedback & Resolutions 

Conversational analytics allows businesses to monitor and analyze customer conversations in real time. This real-time feedback enables companies to swiftly identify emerging issues, customer complaints, or trends, allowing them to take proactive measures to address these concerns promptly. By capturing and analyzing conversations across multiple channels, such as chatbots, social media, or customer support calls, organizations can identify patterns, spot potential problems, and resolve issues before they escalate, thereby enhancing customer service and reducing churn.

Keep in mind that the information gathered won’t be all negative feedback either. There could be key words, phrases, or strategies being used by members of your team that weren’t considered but are creating positive experiences for your customers. They just need to be recognized and relayed to the rest of your team or department so they can reap the benefits as well. 

Benefit 3: Personalized Customer Experience

With conversational analytics, businesses can deliver highly personalized experiences to their customers. By analyzing past conversations and customer profiles, companies can gain insights into individual preferences, behaviors, and past interactions. This information enables organizations to personalize their interactions, recommendations, and marketing efforts, resulting in more engaging and relevant customer experiences. Whether it's tailoring product recommendations, providing targeted offers, or providing personalized support, conversational analytics empowers businesses to create unique experiences that strengthen customer relationships.

Benefit 4: Improved Efficiency 

Conversational analytics helps optimize business processes and improve operational efficiency. Automating the conversation analysis saves time and resources previously spent manually reviewing customer interactions. Automated sentiment analysis, topic categorization, and customer intent recognition enable businesses to extract meaningful insights quickly and accurately. This allows companies to focus on strategic decision-making, prioritize areas for improvement, and allocate resources more effectively. By streamlining operations and gaining actionable insights from conversational data, businesses can achieve greater productivity and maximize their operational efficiency.

Benefit 5: Enhanced Sales & Revenue Generation 

We saved the best for last! Conversational analytics can significantly impact sales and revenue generation. By analyzing customer conversations, businesses can identify upsell or cross-sell opportunities, understand purchase patterns, and tailor their sales strategies accordingly. This data-driven approach allows companies to offer personalized recommendations, targeted promotions, and timely follow-ups, increasing the likelihood of conversion and driving revenue growth. Conversational analytics also helps identify potential sales bottlenecks, allowing businesses to streamline their sales processes and optimize conversion rates.

You can learn more about Invoca's conversation analytics capabilities here.

Quick Stats About Conversation Analytics

We have an entire post available here with some pretty staggering numbers, but below are some highlights we discovered about conversation analytics: 

  • 49% of organizations say using speech analytics has helped them support customer satisfaction (source: Opus Research). Remember, it’s easier and more cost effective to keep existing customers than to find new ones! 
  • Caller retention rate is 28% higher than web lead retention rate (source: Forrester). And this retention comes from INTENTION (yes, we know they rhyme!) Web users can be easily distracted and navigate elsewhere, but callers are reaching out to make a genuine human connection with your brand.
  • 52% of companies have accelerated their AI adoption plans (source: PwC). Leading companies have seen the benefits AI-powered conversation analytics can provide, and they're making big investments to onboard this exciting new technology. If you don't follow suit, you might get left behind!

How Does Speech Analytics Work?

Speech analytics, or speech recognition, is used everywhere. When you talk to Alexa, chat with Siri and, of course, when you use conversation intelligence software, speech analytics is at the core of what makes it all work.

The key challenge in making speech recognition work is that words are not the fundamental element of speech. Words are made up of smaller components called phonemes, which are basically the building blocks of speech. In the English language, for example, we have millions of words, but they're made up of only 42 phonemes. So, the first step in speech recognition is to break up the audio stream into these phonemes. The first step that you need to know in order to do that is some linguistic knowledge of how long a phoneme actually lasts.

If you're going to divide up an audio stream into phonemes, you need to be working on the right time scale. And so, as the audio stream comes in, you chunk it out into segments that are overlapping. And then you want each segment to be short enough that it has just one phoneme in it, but long enough that you can actually capture the acoustic signature of a phoneme.

For example, if you were to do this on a piece of music, it would tell you what notes made up that music. Then that gives you an acoustic signature for that small time window, and then that is a signature that you try to match against a library of phonemes you have.

Once you can identify the phenomes, you can match them against a database of known phenomes that exist in English.

To begin distinguishing words, there are a few more steps. So, you take the audio stream and break it up into these signatures of frequencies then match those against phonemes. And then from the phonemes, you need to get to a word, and you use a pronunciation dictionary for that. The same set of phonemes could make multiple different words, so, on top of that, you have a language model. So, for example, “eight,” the number, or “ate” the verb sound the same. But the words that come before or after can give you an indication of which one was the intention of the speaker.

And so, you have a statistical mapping from the audio to the phoneme. And that's never going to be certain because different people speak differently. They have different accents, might have a bad audio connection, and so on. So that’s a statistical connection. And then the mapping of the pronunciation to words is also statistical. There might be different ways to pronounce a word. And then which word choice is statistical because some words sound the same, so you have to determine from context what the most likely outcome is. That means you have these three probabilistic connections. And at the end of the day, you multiply those probabilities together, and you hope that the probability peaks at a particular word.

Want to dig deeper into how speech analytics works? Listen to this Software Engineering Radio podcast with Invoca data scientist Mike McCourt.

The Types of Data Marketers Use Today

  • First-Party Data: First-party data is data that your organization has collected directly from your audience. This could include data from CRMs, website visitors, social media followers, email subscribers, transaction records, and phone calls.
  • Second-Party Data: Second-party data is another company’s first-party data put up for sale. The seller collects this data directly from their audience and sells it to your organization, without a middleman.
  • Third-Party Data: Third-party data is purchased from an outside broker that did not play a role in collecting the data.
  • Zero-Party Data: Zero-party data is information that a consumer willingly provides to a company. This could be from a survey, form fill, social media poll, quiz, or another similar format.

Want to learn more about marketing data sources? Check out our blog,What Marketers Need to Know About 1st, 2nd, and 3rd-Party Data.

Why First-Party Data is Important

  • Unlike third-party data, first-party data complies with the latest privacy regulations.
  • Since first-party data is your own raw data, you can ensure its accuracy and integrity.
  • First-party data gives you a competitive advantage since your company maintains exclusive ownership of it.
  • First-party data is more relevant than third-party data, since it’s data that your existing prospects and customers have willingly given directly to you.

The Disconnect in Confidence and Strategy for First-Party Data in Marketing

Our research found that marketers today find value and are confident in the first-party data they gather from both online and offline channels. Ninety percent of respondents said they are very confident or confident in purchase history data and 86% stand behind data from their company website. However, only 28% of respondents said they have a fully unified marketing strategy, likely because there are still gaps in the primary channels marketers rely on. In fact, over one-fourth of respondents said they don’t have data on customer interactions from their company website.

Marketers Are Underutilizing First-Party Offline Data

While marketers are also confident in offline data, such as human-to-human conversations, it is an under-utilized source. Eighty-one percent of marketers are very confident or confident in data from in-store interactions, followed by 73% with data from phone calls. While 56% have access to phone call data today, only 8% say it is the top source of data they use to inform personalization.

However, our research shows marketers plan to invest more time, resources or monetary budget on offline data sources, with 58% planning to invest more in in-store interactions and 47% planning to spend more toward campaigns that drive phone calls.

Why Conversational AI is the Future of First-Party Data

One of the reasons that conversational AI is seen as a great first-party data source is that today’s consumers expect more than the purely digital “point-click-buy” transactions and are demanding blended experiences that bring conversations into the mix. In fact, 70% of consumers feel frustrated or angry when they don’t have the choice of contacting a human representative.

An Invoca study found that over 70% of consumer are frustrated or angry when they are not given the opportunity to connect with a human representative.

Whether it’s a phone call with a human representative, chatting up chatbot, or getting help on the go with text messaging, consumers want to talk. This means that businesses need better ways to listen. In order to hear and understand what your customers are saying at any sort of scale, you need conversational AI to make sense of and take action on the data.

Companies that frequently have conversations with their customers on the phone are sitting on a goldmine of customer data. They may have thousands of hours of customer phone calls every year and have tens of thousands of call recordings banked — just imagine the kind of customer data that is in all of those calls. You’ll learn why they are calling, what makes a purchase happen, whether they are calling more often for service or sales, whether they are happy or mad — the possibilities are nearly endless. Then imagine manually wading through all those call recordings to gain insights on those calls. It’s just not possible. And as it turns out, it was pretty challenging to get computers to do it, too.

The Challenges of Using AI to Understand Human Speech

On the computer side, much of the difficulty in analyzing conversations lies in the many nuances of human speech. Unlike the formulaic equations and coded strings of commands computers usually deal with, human speech follows only a loose pattern and logic. Even if we are only talking about analyzing the English language, there are hundreds of different accents, inflections, phrase patterns, varying word usage, slang, and colloquialisms that even other people have a hard time understanding. New research shows that some elements of speech are hardwired in the human brain, but what really makes people different from machines is our ability to instantaneously process all of this variance of language. Creating a machine learning algorithm that can “learn” how to process human language is a whole different ball of wax.

When it comes to processing conversations in phone calls, which is what Invoca Signal AI conversational analytics does, things get even hairier. “Phone calls are idiosyncratic in the world of natural language processing,” said Invoca data scientist Mike McCourt. “They can be repetitive, can contain both recorded messages and human speech, and often suffer from bad connections.” On top of that, phone calls also contain both full conversations and sequences of simple yes/no answers, hold music, keypresses, silence and many other variables that you don’t see in textual communications. This makes it difficult to design AI software that can juggle so many competing needs.

Most well-known AI models for language are designed for either long, carefully edited texts like news articles, or for short, spontaneous speech like a Tweet or chatbot conversation. In our experience, none of these well-known models work on phone calls, which are both long and spontaneous. Since analyzing phone calls differs so starkly from the rest of the research community, we did our own research and development to develop our conversational AI algorithms.

How Does Invoca’s AI-Powered Conversational Analytics Software Work?

Invoca’s conversational analytics tool is designed to help marketers get a new view into conversation data from high-intent consumers — such as purchases made or promotion inquiries. Marketers can quickly gain new insights and get attribution from phone calls and take action on them in real time. It is used to drive more revenue-generating calls, boost conversion rates, and optimize the buying experience. Signal AI conversational analytics is most frequently used to:

  • Optimize Ad Spend: Automatically adjust keyword bidding strategies and suppress ads in systems like Google Ads and Search Ads 360 for callers who convert over the phone
  • Seed Audiences: Create new audiences using offline conversion data to expand your reach of potential customers through native integrations with Facebook and Adobe Experience Cloud
  • Personalize Content: Update content management tools like Adobe Target to personalize content for each subsequent consumer visit based on call conversations

Compared to other conversational analytics software, Signal AI offers marketers deeper insight into the unique conversations happening between a businesses’ buyers and agents — often uncovering conversation patterns and behaviors that marketers didn’t know existed — with consumer-level data that can be made actionable across marketing platforms.

Here’s how conversational AI works in Invoca’s call tracking platform:

How Invoca Signal AI conversational analytics works.

Step 1: Call data flows into the Invoca platform during each conversation.

Step 2: The spoken data is transcribed into text so it can be analyzed by the algorithm.

Step 3: The predictive model analyzes the conversation and identifies key patterns, phrases, and actions, then identifies call outcomes such as ‘application submitted’ or ‘quote received’.

Step 4: Those outcomes and insights are pushed into your marketing stack so you can use this valuable conversation data to optimize marketing spend and personalize the customer’s next interaction — all in real time.

Easily Train a Custom AI Model with Invoca Signal AI Studio

Training an AI model may sound like a daunting task to take on, but not with Invoca! Our Signal AI Studio offers no-code UI that speeds you through the process of training a custom AI model. You simply tell it the insight you want to measure, and Signal AI Studio shows you transcribed examples from your calls that either fit or don’t fit that insight. It learns with every response, creating a new AI model in no time.

Custom AI models from Signal AI Studio can accurately detect virtually any insight or topic from conversations, including:

  • Caller Intent: Detect if the caller is a sales lead, current customer, new or existing patient, or job seeker
  • Caller Interest: Determine the specific product or service the caller is interested in, if they are looking for help with an online order, or need support with a product issue
  • Conversation Outcome: Detect if the caller made a purchase, booked an appointment, received a quote, or canceled a service
  • Call Events: Discover important events like if the caller asked to be called back or to speak to a supervisor
  • Voice of the Customer (VoC) Insights: Detect if the caller asked about pricing or a specific product feature, discussed a competitor, or lodged a complaint

Learn more about how Signal AI Studio works here

Identify Phone Conversation Themes with Topic Explorer

It can be difficult to sift through your mountains of voice-of-the-customer data to identify trends. That's where Invoca Topic Explorer comes in. This tool allows you to visualize the themes and related topics being discussed across thousands of your calls at once to surface unexpected insights. You can specify the topics or categories you want Topic Explorer to visualize, view GPT-powered descriptions of topic summaries, and review transcribed examples from actual calls. Topic Explorer is a unique and powerful way to surface new and actionable insights on customers, call experiences, agent performance, and digital marketing campaigns you weren’t using Signal AI Studio to actively analyze.

These insights can give you a deeper understanding of what your customers are interested in and how to target them.

How Are Marketers Using Conversational Analytics Tools to Drive Better Results?

Ok, so now you know what conversational analytics is and how it works. But how are businesses using this technology in the real world? Below are two examples of how cutting-edge companies are using Invoca’s conversational analytics software to drive better marketing results.

How Rogers uses Invoca conversational analytics to boost revenue from paid search campaigns by 18%

Rogers is Canada’s largest telecom company, serving over 10.8 million subscribers. Rogers’ mission is to provide the very best in wireless, residential, and media to Canadians and Canadian businesses. 

The majority of Rogers’ marketing budget goes to Google paid search. However, before using Invoca, Rogers didn’t have visibility into the phone call conversions each campaign, ad, and keyword was driving. Since many of their customers buy over the phone, this was making it difficult for them to understand which campaigns were truly giving them the best ROI. 

Rogers used Invoca’s AI to understand not just which customers converted over the phone, but the average value of the conversion for each customer type. With this data, they were able to understand the revenue that each paid search campaign, ad, and keyword was driving — both online and over the phone. They could then feed this revenue data into Google Ads to inform Smart Bidding. Smart Bidding weighs their bids in proportion to their returns, decreasing their cost per acquisition by 82% in a two-year period. They also achieved an 18% lift in net revenue from paid search. 

Invoca also helped Rogers identify keywords that they previously thought were promising, but were driving the wrong types of calls. Before using Invoca, Rogers could see the Google data showing that an ad had driven a call — but they were left in the dark about the outcome. This led them to prioritize certain ads that were driving billing questions and customer support calls, rather than sales calls. With Invoca, Rogers was able to cut spend on ads that were driving non- sales-related calls and instead allocate that budget to higher-performing campaigns. 

“The results with Invoca have been phenomenal, to say the least,” said Charlie Farrell, Senior Manager of Search Engine Marketing at Rogers. “The benefits are constantly compounding with such minimal lift for the returns. I’ve never had a product where I spend more time selling people on the results than doing the work to get it going.”

Get the full case study here.

How Miracle-Ear uses Invoca conversational analytics to drive a 16% increase in media efficiency

Miracle-Ear has over 1,500 locations nationwide, the vast majority of which are locally owned and operated, and many of the franchisees have been with the organization for decades.

As part of its digital transformation, Miracle-Ear wanted granular data about the phone calls its marketing programs were driving and the revenue those calls generated. With Invoca’s Signal AI, Miracle-Ear is able to integrate conversational analytics data into its digital marketing tools. This enables them to send user-level call outcome Signals directly into Adobe, Google, and Facebook algorithms to drive campaign efficiency and effectiveness. 

“Invoca is a very powerful tool that can be the linchpin for your entire data structure and digital strategy,” said Huff. “The Invoca motto is, ‘integrated data drives automated results’ and that is exactly what happened to us.” 

Miracle-Ear built a structure that allows them to automatically pull Invoca data into their BI tools, automate reports, and show marketing campaign performance down to net revenue. They also set up integrations with Google Campaign Manager, Adobe Experience Cloud, and Facebook, which enable them to automate processes and better use the algorithms of the BI tools to their advantage. 

As a result, Miracle-Ear drove a 16% improvement in media efficiency and a 15% increase in call center efficiency.

Get the full case study here.

Additional Reading

Want to learn more about how Invoca’s AI can help you drive more revenue? Check out these resources:

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