Measuring purchase intent is not easy as it involves identifying and evaluating the proper correlation between multiple inputs. You can’t simply pick a few visitors and extrapolate their situation to the whole market.
For example, you can’t only rely on demographics — you also need to monitor how a certain cohort interacts with your website. Google Analytics and other similar analytics services can help you uncover some behavioral insights.
Looking at where your visitors are coming from shows their level of activity. Are they actively seeking your solution or did they just click a banner advertisement?
Visitors accessing your website through newsletter links can generally be identified as having high purchase intent — not only are they engaging with your content, they have already given you their email address.
Similarly, you can analyze visitors to different web pages. People looking at pricing and demo pages may have a higher purchase intent than those who exclusively read about your product’s features.
The problem is that most analytics solutions report after the fact, or they don’t take into account the hundreds (if not, thousands) of inputs required to accurately predict a visitor’s purchase intent. Even if data is shown to you in real-time, it takes too long to compare the results and take action. Besides, up to 98% of visitors to your website remain completely anonymous.
If you only rely on visitors who sign up for a newsletter and demo, you’ll miss out on the majority of those who are interested in your offering but are not yet convinced enough to participate.
Location, for example, could say a lot about any given visitor — that’s why IP address lookup is a popular technique in data intelligence software, especially for determining if your visitors are from a key account. That said, in the modern work-from-home market, an IP address lookup is less valuable. So, in order to measure purchase intent, you need to be able to account for many inputs, process them in real-time, and then assign a score of that purchase intent to each visitor.
How to Score Purchase Intent?
As mentioned above, measuring purchase intent is mostly about determining user behavior: what do they read, which devices they use, which channels they come from, how long are they spending on the site, what pages are they looking at and in what order, and so on.
On a marketing level, this all folds into a complex path to purchase. For example, most of the time, signing up for a demo is not a one-step process. People visit, read, leave, and often come back later again after consulting with coworkers and decision-makers, etc. This is especially true for more expensive products or services with high commitments.
So, each of these components in the user journey should add (or subtract) from the visitor’s purchase intent score.
Where does this leave you? Well as you can probably guess, this scoring is not something a human can do in real-time. There are simply too many inputs and variables to take into account. Purchase Intent Scoring is something that must be done by a machine-learning model. More on this shortly.
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