Picking up where old-school call tracking leaves off, Invoca’s AI-powered call tracking platform can unearth rich insights from phone conversations and attribute call conversions to the entire digital journey. With this complete set of call data you can optimize your digital marketing tactics to drive high-quality calls and also automate subsequent marketing actions like retargeting or suppression in real time. Invoca provides call intelligence that is much like the marketing intelligence data that you have come to expect from online analytics and marketing automation tools.

Creating call intelligence to optimize marketing campaigns requires several distinct tools and technologies in your martech stack, but it can be simplified into 4 steps:

  • Step 1: Track calls and attribute them to previous consumer touchpoints
  • Step 2: Unify caller data across multiple sources
  • Step 3: Analyze phone conversations to classify call drivers, behaviors, and outcomes
  • Step 4: Push call intelligence out to the rest of the marketing stack

Let’s dive into each step to make sense of the process.

Step 1: Track calls and attribute them to previous consumer touchpoints

Tracking campaign effectiveness has been around since the early days of advertising. Those early marketers used tactics like “mention this ad and get 15 percent off” to attribute the effectiveness of mass channels like newspapers. Of course, you’re probably more familiar with the digital world of attributing clicks to conversions. The challenge with calls is how to create 1:1 attribution with prior digital touchpoints. After all, “mention this ad when you call” doesn’t really get us where we need to be. Enter dynamic number insertion, the key technology that makes 1:1 online-to-offline attribution possible.

Dynamic number insertion, or DNI, uses JavaScript code to replace static phone numbers on your website with a phone number that’s unique to each customer. To see it in action, go to invoca.com on your computer and check out the phone number in the upper right. Now go look at it on a coworker’s computer and you’ll see a different phone number. If you dial each number, they still go to the same place. But using that dynamic number, we can now associate that specific call to each of your previous online activity, for example, if you clicked on an Invoca search ad. Clever, right?

But how is this possible? Wouldn’t you run out of phone numbers pretty quickly if thousands of people visit your website every day? Well, you would if you didn’t recycle them. The reality is that if a consumer doesn’t call within an hour of visiting your site or seeing your ad, there’s a very small chance that they’ll end up calling that number. Therefore, we can then safely reuse that number, exposing to a new consumer after a given expiration period. This makes 1:1 call tracking feasible and keeps costs down.

Step 2: Unify caller data across multiple sources

In order to create that sweet, sweet closed-loop attribution, we need to capture data for each unique consumer. This information is contained in a caller profile, where a marketer can store a veritable cornucopia of relevant data. These data come in various forms:

  1. Customer journey data like ad exposure and website visitation (you might think of this as cookie or campaign data)
  2. First-party data, like customer records, that can be pulled in from a marketer’s CRM
  3. Third-party demographic data
  4. Call data that includes standard metrics like length of the call, time of day, caller area code, and more
  5. Conversational data derived from speech analytics and other AI-based tools (more on this in the Step 3)

By unifying these datasets into a rich caller profile and associating it to the phone call, you now have the basis to understand which marketing programs are driving the most calls, and begin putting this information to good use. Great work! Pack it up, your job is done, right? Not quite.

You also have to know that you’re driving the right calls. After all, not every website visitor is of the same quality. Some bounce right away, while others stick around and complete a purchase. The same goes for callers. Some are looking to buy, while some are looking to get product information or just complain about the weather. To understand the unique value of each call, we need to dig deeper.

Step 3: Analyze spoken conversations to classify call drivers, behaviors, and outcomes

using call tracking to identify call drivers behaviors and outcomes

Let’s say you get 1000 calls into your call center each day, and you know you get 100 phone conversions like a purchase. You also know that half of people that do convert have called in a few times before finally pulling the trigger. How can you determine the 10 percent of the calls that you really want? And perhaps even more challenging, how can you identify the high-quality prospects that may convert on a follow-up call?

It’s easy to forget that your customers are actually telling you where they are in the buying process. They’re telling your call agents things like “let me call you back tomorrow when my paycheck clears,” or “I’m almost ready to buy, let me check with my wife and call you back.” This conversational data is is critical to understand, and most marketers are leaving it on the table.

To feasibly classify these conversations into useful digital datasets, you need an automated system that can understand what’s being said and accurately derive meaning from it. Enter Signal AI — our machine learning-powered predictive analytics technology that analyzes your callers’ conversations and turns them into actionable marketing intelligence. With Signal AI, not only can you predict whether a conversion happened on each call, you can predict things like caller type (e.g. service call vs. sales call), as well as milestones on the path to conversion. And when you understand the nature of a call, you can optimize your media for higher ROI, improve marketing effectiveness, and personalize the customer journey.

Step 4: Push call data to your marketing stack to optimize campaigns

Learning something new about your campaign is good. Optimizing your campaigns based on that information is much better. For instance, if campaign A is driving more profitable calls than campaign B, you would want to see this attribution information reported in the same place that you manage your campaigns.

If calls are important to your business, then you want call intelligence pushed into all the marketing platforms that matter to you, whether it’s search marketing, programmatic advertising, analytics tools, email marketing, or any one of the hundreds of martech solutions out there. We call this data accessibility, and it’s why data formats and integrations are so important.

Data Formats

In order to maximize the usefulness of Signal AI-measured outcomes, we report them as binary signals: essentially a true/false answer to specific questions like “did a conversion happen?” or “was it a sales call?” Since this is the most common data format within digital marketing, it makes it easy to integrate Invoca with many types of systems.  

Invoca Martech Integrations

Inovca also offers a flexible integration framework, supporting integrations across wide range of marketing tools, including the most important digital marketing platforms like Google, Facebook, Adobe, and Salesforce. By pushing call intelligence in commonly-understood and reportable formats, marketers can democratize access to this, making it easier to extract value across their entire marketing team.

Check out this case study to see how Frontier Communications used Invoca to optimize campaigns and add over $4 million in revenue.

 

Ian Dailey

Posted by Ian Dailey

Ian Dailey leads product marketing at Invoca. In this role, Ian brings to market new call intelligence technologies that help modern marketers understand who's calling, what's happening in the conversation, and how they can best leverage these new data to improve ROI. Prior to this role, Ian led product marketing for the Rocket Fuel DSP — a pioneering buy-side programmatic media platform — where he launched a number of solutions including cross-device optimization, hyperlocal mobile targeting, and online-to-offline attribution. Ian holds an MBA from the University of Michigan and a bachelor’s degree from the University of Southern California.

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