The 'spray and pray' approach to B2B client acquisition is DEAD.
Demand Insight will transform the way you prospect.
The days of your prospect's buyer's journey being anonymous or over. It's transforming and it's happening faster than you think. The B2B digital buyer's journey is now transparent. No more anonymity.
Your company can now track and monitor your marketplace's digital behavior using identity resolution. With identity resolution, you have the ability to identify by name, who is actively researching a purchase of what your company sells, including those prospects on your website.
To do identity resolution, you need what's called an identity graph, sometimes called an ID graph. An identity graph is a database that stores all identifiers that correlate with that particular person.
Things such as a cookie, a device ID, a persistent identifier for a particular ad channel, their user agent, their browser are all pieces that help connect their identity. By having an identity graph, you can recognize a person regardless of their device and monitor all of their digital behavior.
Our system collects and processes the individual identity on over 40 billion pieces of digital behavior. Things like URL-level navigation, keyword search, content consumption, and ad campaign response.
The first thing we do is when we onboard your marketplace to our identity graph. This is how we start to collect all of that behavior that are relevant to the buying behaviors of what you sell. Because of the identity graph, we're able to track and monitor their digital behaviors across 40 billion pieces of digital behavior per day.
As we process that information, we're looking for the common behaviors between your prospects. When a prospect clicks on one of your ads and is engaged on your website, then all of the chronological behaviors leading up to that purchase are analyzed (it’s called a training sample).
See, machines are dumb. They are systems that can learn from data, but they must be taught to do so. The specific data that is the teacher in this example is that chronological behavior. Behavior that:
This training sample for any one individual prospect is now compared to hundreds of other prospects who committed the same or similar behavior. We then build out a behavior pattern of prospects that are InMarket.
Next, our system goes back and looks at all of the individual behavior for that particular prospect, and all of the other prospects. We use this to create a behavioral profile. As the behavior comes into our system, we're resolving the identity on those 40 billion pieces of digital behavior looking for the same behaviors in the training sample profile.
And we're just asking the simple question, "Is this person in our client's marketplace? And if so, is this behavior exactly like previous conversions?" If yes, now we start collecting more of that person's behavior and we build out the behavior pattern.
The machine is taught to predict, and if that prediction continues to have behavior, we'll push it as a lead (an InMarket lead). If we make a prediction and you run ads to it (we'll give you the ability to do that), you'll get a click and we'll be able to know if they visit your website. If they navigate to your website outside of anything you might be doing directly with them, we'll also know that as well.
For a deeper dive on identity resolution and machine learning, watch this video.