If there’s one critical aspect to the success of your digital strategy, it’s knowing your audience. The more you know about them, the better your targeting and engagement with that audience will be.
The most popular information you can get on your audience from most tools is demographic: age, gender, interests, etc. Even if this kind of data lets us know something about your visits, it doesn’t say much about their story and their context.
Are they interested in your solution? If so, why? Do they want to research what they need on their own or do they require help?
Answering these questions and more becomes easier if we know one crucial bit of information about your visitors — their purchase intent (also known as buying intent)
What Is Purchase Intent (Buying Intent)?
In the digital marketing landscape, purchase intent is a relatively new but highly effective metric for lead generation. It refers to what your visitors are thinking in terms of buying your solution and how close they are to doing it.
Purchase intent is a holistic metric — it blends demographic and behavioral information together to not only measure who your audience is, but also which actions they take towards exploring your offering.
Since products and services today are getting more complex, purchase intent can get spread out over a few stages in your marketing funnel.
For example, some visitors might exhibit informational intent where they are interested in your solution, but don’t yet know enough to buy. Alternatively, some visitors might come with a pure transactional intent. They’ve done all the research required and go straight to the pricing page, sign up for a free trial, or put your product in the shopping cart.
It’s important to be able to differentiate the intensity of purchase intent, which could range from reading blog posts to clicking through CTAs on a landing page. So how can you measure it?
How to Measure Purchase Intent?
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 action. 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 webpages. 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 do you reliably score their purchase intent?
How to Score Purchase Intent?
As mentioned above, measuring purchase intent is mostly about determining user behavior: what do they read, which devices do they use, which channels do 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.
Wait, Can’t We Just Use Chat to Ask Users?
One common tactic to figure out what your visitors truly want is to simply ask them; and the most convenient way to do it is through chat.
Chat solutions are often relied on as a key tactic to identify anonymous website visitors, explain your product, and collect invaluable feedback. When you’re connected with a visitor, you don’t need lots of data analysis — everything you want to know you can just ask, right?
Not quite. Not everyone wants to be chatted with, and even if they do, they will only reveal snippets of information that only tell part of the story. You can often still determine more information about a visitor by watching their purchase intent before engaging them in chat.
Additionally, the strategy of attracting conversations through chat pop-ups only works for your sales team when your traffic is manageable. Otherwise, your team gets overwhelmed trying to sort through visitors to get to those with high purchase intent by themselves.
Relying on chatbots to try and process every visitor is also not ideal - they have limited capability in determining the most effective response for a visitor and can often miss out on opportunities where the human touch would have prevailed.
What is required to effectively identify anonymous website visitors, and score their purchase intent at scale, is to implement a solution like Lift AI.
Lift AI is a buyer intent solution which can be added to your website and works alongside the chat platform you already use, such as Drift or LivePerson. Lift AI leverages its machine-scoring model to measure how likely any given visitor is to buy your solution.
On average, Lift AI will identify 9% of your website visitors with the highest purchase intent and match them directly with your sales team for conversion. Lower-scoring visitors could then either be delegated to a self-help or nurturing bot, both of which could still further qualify visitors while providing useful information.