Behavioral analytics is comprised of metrics which determine how the user behaves when using an application, or visiting a website. These metrics go beyond standard ones, such as page views, sessions, monthly active users, etc. They show us the engagement our product has with users, how it affects retention, conversion, revenue and this is why understanding these metrics is so important.
We learn all of this through various events. These events represent any actions that a user can make. For example: opening the app, creating an account, watching a video, and any other activities tied to a specific user, such as making a purchase. However, it should be noted that analyzing just the right amount of data is key - if we are too eager and follow too many variables, we won’t be able to extrapolate the result we want.
It’s like going to an airport. There’s a perfect route that the staff wants you to take - through the main entrance, over to check-in, baggage registration, and so on. The passenger can also go have a cup of coffee, buy some souvenirs - all of these are good for the airport, so they are deliberately placed in enticing locations in order to lure them there. Every such action brings with it a certain score that’s attached to the visitor and triggers an event - this is how businesses project for and plan their user journey.
Before we start to analyze the user’s behavior, we first have to define what our goal is. What we track and why. Because adding various events without a goal only serves to muddle the analytics that we have. If we wanted to track the onboarding conversions or the user’s behavior on our landing pages, we would have to be aware of all the steps the user goes through and mark them all. This means that every time the user triggers one of these events, our analytics will make a note of these interactions. After that, we count these events, and that’s it. It sounds so simple.
And that’s because it’s not at all that complicated. Let’s take a standard SaaS that has a webpage that serves to promote their app. In order to buy the app itself, the user has to go through the following steps: arrive at the site > register for the trial version > purchase the app. Each of these steps has additional branching in the form of different pages on a site that a user can preview before making a trial version.
Another important metric we always take into account is lead scoring. It is a methodology that assigns each user a particular rating depending on the behavior they’ve exhibited. It helps us to understand which users and visitors are closer to the goal we defined. There are two types of lead scoring.
The implicit kind - where, based on the behavior of the user, we assign an estimate for all the steps they’ve undertaken in our ideal user journey.
The explicit kind - where, after we make contact with the user, based on their demographic or firmographic characteristics, we assign a rating that tells us if that person has a smaller or bigger chance to work (meaning - to use our product) with us.
Now that we have a defined goal, the user journey they should (ideally) make becomes clear. We’ve defined all the key points that every user can go through on that journey, all that’s left is to make sure that our tools for marketing automatization and CRM are in line. This means they monitor and supplement each other.
How do we rate this?
We define the ratings for each point that the user triggers along the way. It’s important to point out that we also have negative ratings - when the user does something that’s not beneficial to us.
By adding these ratings up, we get a final calculation which we use to filter users and prioritize them in the sales funnel. Meaning, we don’t use lead scoring just for sales, but we also apply it to various other actions. For example, when we want to see how complicated the ordering process is, if the users stray too far from the imagined journey - then we give these processes a negative rating.
One of the most interesting things about modern marketing is that we can hack just about any methodology if we understand it enough. We can then adapt it to our system and know that we are doing the best possible thing - both for us, and our users. These hacks are sometimes extremely successful, and this gives birth to new methodologies, and sometimes they’re not - which still leaves us with valuable experience and new knowledge into why a particular method or approach doesn’t work.