Artificial intelligence-powered platforms are helping marketers make more precise data-driven decisions faster than ever. Many martech companies are launching AI applications, and it’s exciting to see the technology become pervasive across so many industries so quickly. Invoca lives and breathes to help marketers get more visibility into phone calls, so of course our AI solution, Signal AI, is designed to allow you to better understand and predict consumer behaviors by mining data from phone conversations with your customers.

Signal AI has been used to analyze over 2 million conversations and the award-winning technology is used by companies like Frontier Communications to uncover new opportunities to boost marketing ROI. That’s all great, but you’re probably interested in how it actually works. Let’s get beyond the AI buzzwords and dig into the 1s and 0s.

What is Machine Learning and Artificial Intelligence?

“Artificial intelligence” (AI) and “machine learning” (ML) are frequently used interchangeably, but they aren’t the same thing. Just to clear the air:

AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.

ML is a current application of AI based around the idea that we should be able to give machines access to data and let them learn for themselves.

However, ML and AI are not magic wands. As nice as it would be, marketers can’t just flip an AI switch and double marketing ROI. Like all marketing tools, they are most valuable when they are powered by a clear strategy and clean data.

Using AI to Extract Value from First-Party Call Data

Phone conversations are the ultimate first-party data source and they’re a holy grail for marketers who work in industries that rely on phone calls to make sales. But marketers aren’t usually equipped to handle the sheer volume of data created by phone calls. Just imagine listening to thousands of phone calls a month to figure out when conversions happen, when they don’t, and trying to apply what you discovered to take the next appropriate digital marketing step for each individual caller. Some organizations have actually tried this, and it’s costly, error-prone, and inefficient to perform at scale.

So when it comes to calls, you enter the modern conundrum of having too much data — in this case, call recordings — and no way to efficiently analyze or use it. Enter AI. It can take in vast data sets and draw conclusions by detecting intent and word/phrase patterns, then give marketers access to valuable, actionable insights that would be otherwise unattainable. On top of that, Signal AI is able to detect subtleties that humans might miss, as machines can identify patterns that people could easily gloss over.

How does Invoca Signal AI work?

Ok, so Signal AI sounds cool, but how does it all work? We’ll break it down into a four basic steps:

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.

*For marketers concerned about HIPAA and PCI compliance, sensitive information is never stored anywhere and Invoca automatically detects and redacts it from all data and analytics — even in cases where calls are recorded.

There are many ways that marketers can put this data to work, here are a few typical applications:

  • Optimize Ad Spend: Automatically adjust keyword bidding strategies and suppress ads in systems like Google Ads (formerly AdWords) and Search Ads 360 (formerly DoubleClick search) 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

How Signal AI is Implemented

While Signal AI is quite advanced functionally, it does not require a heavy IT lift to implement, and “training” it to identify outcomes for your business can be accomplished fairly quickly. We offer two different versions of Signal AI that can be selected depending resource availability and complexity of the use case.

Pre-trained AI

We have built “out-of-the-box”, industry-based predictive models that have been pre-trained using tens of thousands of hours of industry call data. Our Industry AI models are applicable to considered purchase-focused businesses in industries like insurance, automotive, financial services, telecommunications, home services, and healthcare. We understand that not all companies want to identify the same insights from their conversations, so we’ve created over 25 distinct pre-trained Signals for marketers to choose from. This package is also ideal for marketers that may have an insufficient volume of call recordings to train a custom algorithm.

Custom AI

Business that have unique business outcomes, high volumes of existing quality call data, a more sophisticated data science or analytics function at their organization are more likely to use the Custom AI. To implement Custom AI, you first identify the outcomes you care most about and then compile a set of calls where that outcome is met, and another set of calls where that condition is not met. For example, if you want to identify calls where a caller submits an application, you’ll compile sets of calls where an application was actually submitted and where an application was not submitted, so that the AI model can learn how to distinguish between when specific events happen vs. when they do not. The data is then uploaded to Invoca so the algorithm can learn the patterns inherent across customer conversations, in order to effectively make predictions and classifications on new inbound phone calls.

Is AI-powered Call Analytics for Me?

The long and short of it is, if your business uses digital marketing to drive a high volume of customers to the phone, then you need a call analytics solution that uses AI to make the most of conversational data. Any marketer knows the value of first party data, and the data you can glean from people who are calling your business is more accurate than correlating clicks to behavioral intent ever can be. Utilizing a tool like Invoca Signal AI makes it possible to use this data to optimize digital advertising, reduce marketing costs, and reach more potential customers than ever.

Knowing the outcome of each and every phone call and being able to automate marketing actions after the call is invaluable to marketers of many different stripes, but with one common thread: they all value phone calls.

If this sounds like you, call 855-727-9492 or click here to schedule a demo.

 

Maria Bruno

Posted by Maria Bruno

Maria Bruno is a Senior Product Marketing Manger at Invoca and is responsible for positioning, sales enablement, and go-to-market strategy. Prior to joining Invoca, she held account management and field marketing roles at Oracle and Responsys. Maria earned a B.S. in Economics & International Studies from the University of Wisconsin-Madison, where she gained a love of cheese curds and an unfortunate accent.

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