Why Visitor Journey Analytics Gets You More Customers
As marketers, we now have data at our fingertips. The industry has given us a broad set of data warehousing and analytics tools. In many cases, these tools work well at providing us aggregate data about what visitor acquisition channels are working for our top-of-funnel spending. We see reports on paid, organic and referral channels, as well as our cost and conversions there-to. What about visitor engagement data between acquisition and purchase — is this at our fingertips and actionable?
The answer lies not in an analytics feature checklist. It’s in the fundamental approach of web analytics tools — they don’t focus on the visitor journey. They do focus on web sessions, prioritizing it over a detailed clickstream journey of the individual over time.
Eventually, a lack of visibility into visitors’ footprints leads to a set of hypotheses around how these visitors should be marketed to, backed by gut-feel instead of dropoffs understood by visitor analytics.
In this post, we’ll cover how marketers can harness the power of visitor analytics to tackle the complexity of a variety of consumer interactions and increase conversion rates.
What’s the Visitor Journey opportunity?
If you aim at making visitor-backed decisions that eliminate dropoffs and drive conversion, you need something that gives you a detailed view of individuals as opposed to sessions. To influence a visitor to engage and buy, you need visitor analytics. Here’s why:
Why Visitor Journey Analytics?
Visitors buy over multiple visits
Depending on the nature of the product (commodity vs unique) and the competitive pricing dynamic, the number of visits it takes to convert varies. However, most marketers admit they rarely see conversions happening on the first visit. Harvesting data from all visits, as opposed to one session, allows marketers to see visitor journeys holistically and understand when to intervene. Is it always smart to intervene on the first visit?
Visitors aren’t always identified
Visitors may sign up on the website, come back later as a logged-out user, and complete a purchase. Knowing this clickstream is important for a complete customer profile.
Visitors create complicated funnels
Despite marketer’s anticipation/plans, visitors often have their own way towards the conversion.
Visitors move across domains. Tracking doesn’t
Visitors may visit more than one domain that are within the same brand experience. To understand a visitor holistically, it is now required to collect data across multiple domains. For example, Stitchfix has a sister site, stitchfixreviews.com, where a ton of buying decisions are made and executed on stitchfix.com (or not!)
At its core, visitor analytics is focused on people. Web analytics chases pageviews, while visitor analytics chases the visitor journey to create a personalized experience for the visitor. It’s no secret that web analytics and page level data have paired well with optimizing the overall web experience (via content management systems). It becomes more about creating insightful marketing and notification campaigns around the visitor being led through the funnel, and less about how to report on traffic.
Why don’t web analytics systems extend to this opportunity gracefully?
Traditional web analytics tools have attempted to respond to this challenge of understanding the visitor. They add visitor tracking as a feeble add-on feature — it’s not at all the principle foundation upon which their tracking is built. All of the visitor’s behavior must be tracked and associated with a single identified visitor, and web analytics systems were not designed to do so without significant after-market customization. We regularly see that our customers’ don’t know such tracking is possible to configure since it’s intricate, even if we talk about a facto industry standard for analytics, like Google Analytics.
Speaking of which, Google Analytics is a powerful tool that has moved the industry forward significantly. It aggregates huge amounts of data you can later manipulate (if you know what to look for it.). The trick is, even industry standard tools have gaps in understanding the visitor journey.
Let’s take a closer look at some of them.
Session Activity sans Logging In
Imagine that a person visits your website, leaves, and then comes back days later and registers. With Google Analytics, for example, only that last session is assigned to the user ID. The first session is essentially lost. What happened to all that data? The tool connects data from only the session in which the user was identified.
Eventually, we lose precious data and become victims of the last attribution bias. To avoid it, the visit and registration must take place in the same visit session. And that’s rarely the case.
Data from Identified Sessions
Google Analytics assumes that each visit is from a new person. The only way around this is to identify people in each session in order to see everything that person does.
For example, a visitor comes to your site, registers, logs out, and closes the browser. A week later, they come back without registration or logging in. How to stitch both pieces of data together?
Every piece of Google Analytics data needs a user ID attached to it — you’ll need to send the user ID every time there’s a Google Analytics hit. Every session needs a user ID defined in order to connect that session to a person. In the case above, Google Analytics would assume that the second visit was coming a different person.
Funnels
Funnels are possible in Google Analytics. However, the tool relies on manual tracking rather than an automated one. It means that when you set up funnels, you can view data for that funnel collected from that point forward. You will not be able to view data that happened before you set up the funnel. Additionally, you can track consecutive steps that visitors go through only if they are on the same visit. The data gets lost if people complete a process over multiple visits or drop out. You will struggle with building an accurate visitor acquisition funnel.
To sum up, web analytics tools may put you at risk of losing data due to its manual tracking as opposed to an automated one. Moreover, custom events often requires code and involve a JavaScript (JS) developer, making the whole process far from being operationally smooth. Also, Google Analytics is free — to some extent. If your data is big enough, but if you’re not ready to pay $150,000 for an upgrade, the tool will sample data by dropping hits and giving your trends instead of the full precise clickstream for every visitor.
What are the tenets of a next generation Visitor Analytics system?
Pairing your web analytics with visitor analytics allows you to take a closer look at pre-identified success milestones based on someone’s lifecycle stage, as opposed to generic event data. To spot a Visitor Analytics platform that will compliment your visitor marketing, look for 4 core competencies:
Visitor Journey Platform Competencies
Full Resolution
The Visitor Analytics platform captures all visitor data captured at scale and does so granularly. Every visitor action and every interaction are stored with no data sampling.
Autotrack
Autotrack is opposite to the manual tracking you may be dealing with currently. The Visitor Analytics platform tracks all visitor interaction data, clicks, touches, scrolls, form fills, etc. with no event definition. Nothing that needs to be captured has to be provided up front; all event, segment, funnel definition pulls against data previously collected.
Retroactive
Instead of making a decision based on a funnel, you can slice it up into events, segment, and other such precise behavior indicators. Defining these elements is done post hoc and is retroactive on all the data captured previously.
Visitor Journey Viewer
You may wonder — why should we look into one visitor journey instead of thousands? You want to know what is happening in a narrowly defined segment to formulate a hypothesis of how to affect their journey. Spot checking certain visitors gives you an idea of what patterns to look for.
Turning Visitor Journey Analytics into insights
When visitor journey analytics is at the tool’s core, you can see fallen off visitors quickly and precisely, and act accordingly. A visitor journey analytics platform like Intempt helps to find key drop off points and aim notification campaigns to stave these drop-offs. And the best part is — campaigns are backed with true visitor journey data, not hypotheses. The platform is using autotracking as opposed to manual tracking of the data. This means that once the tracker is set up for your domain, data is not going to slip away and will be stored for any potential use.
Intempt Tracker for a sample customer
Imagine you’re looking to identify drop offs between the funnel steps and address them with a notification campaign. First, we focus on the Event + Segment combo. We create a simple segment for a visitor that got into the funnel but didn’t make it to the end of the purchase.
“Fallen off visitors” Segment
You have just thought of the segment, and the platform gives you data of how many visitors have fallen into this segment already, thanks to the autotracked data.
“Fallen off visitors” Segment Audience
As you glance at your funnel, you first may formulate a hypothesis based on the UX of the conversion path and slice the segment accordingly. A typical conversion block for many websites is a form to be filled out by the visitor — the bigger number of required fields we set, the lower is the probability of conversion. We begin to look at your funnel steps that have a lead form and analyze it step by step. We define an event right before a potential drop off and see how many visitors counted against it:
Activity for a pre-drop off event
In the same manner, we create a second event for a potential event that occurs post-drop off and come to realize that there’s nearly 50% drop off rate between the two steps:
Activity for a post-drop off event
Now, we have backed our hypothesis with data and are ready to create notification campaigns against these particular steps.
Right after drop-off, you may want to spot-check a visitor to see any events that occurred prior to disengagement. You can search for a particular visitor and see their journey from start to drop-off. By clicking “Learn More”, you can see every event that has occurred on a particular day during one or multiple sessions.
Voila! When visitor journey analytics is at the platform’s core, you can see fallen off visitors quickly and precisely, and act accordingly with predictive notifications.
Final thoughts
Making decisions based on incomplete information has to end. You don’t need to replace you web analytics tool — you do need to pair it with a platform that focuses on the visitor. Intempt employs machine learning to chomp through large swaths of data, analyze your customer’s unique fingerprint, and build a profile that not only targets your consumer in real time, but predicts their future behavior. All on an easy to use Visitor Analytics and Notification platform. Without writing a single line of code.
