In the year that I’ve now worked for Evergage, rarely has a day passed when I haven’t heard someone talk about the “deep” capabilities of our real-time personalization platform. At times, however, I’ve struggled to fully appreciate just what that really means – at least in the context of helping companies understand their visitors and users. After all, I’m accustomed to using the word “deep” to describe bodies of water or voices, not analytics.

So, in an effort to wrap my head around this oft-used term, I set out to conduct some informal research in the world of analytics. What follows is a summary of my findings and an attempt to frame what exactly “deep” means – when applied to analytics – and why it’s so relevant and significant to today’s digital marketers.

First, a little context

Let me start by saying that I’m not a statistician, I don’t have a masters in mathematics, and thinking about analytics data has never kept me from getting a good night sleep. Rather, I’m just a humble marketer doing my part to help promote Evergage while using the tools and resources at my disposal, one of which is analytics. Nonetheless, I understand the value of analytics for making business decisions as well as its relevance in the digital world we live in. Okay, moving on….

Almost by osmosis, when working at Evergage, you quickly realize that not all analytics data is created equal. In today’s hypercompetitive business environment – where marketers are trying to optimize every touch point with visitors, customers, users or accounts – having the deepest possible data often yields unprecedented insights that can be used to drive engagement and desired outcomes.

After seeing firsthand the types of data Evergage is able to collect – and the speed at which we’re able to do so – I can confidently say that companies are doing themselves a disservice by not investing in a deep analytics solution. Quite simply, deep analytics data offers an entirely new dimension on digital interactions designed to help your company win!

But back to the original question: what exactly is deep analytics data and how does it relate to other types of analytics data? To help put things in perspective, I’d like to propose a Simple Hierarchy of Digital Marketing Analytics, an outline of sorts that help frame four types of analytics data used by today’s digital marketers.   

Aggregate baseline data

Baseline data is what typically comes to mind when we think of web analytics. Aggregated website (or mobile app) data – the number of visits, length of visit, bounce rates, average time on site, page views etc. – are pooled together and often presented in neatly organized dashboards. As I’m sure most can attest, baseline data is ideal for obtaining an overall view of your website traffic for, among other things, measuring month-over-month or year-over-year traffic goals. Generally, none of this information is particularly time sensitive. Checking these stats on a weekly or monthly basis is sufficient for most companies.

Aggregate source data

A natural extension to baseline data, source data provides aggregated details on where traffic originated. Source data typically reveals the referring site, email or ad campaign and/or visitor geographies. Like baseline data, source data provides a high-level view of your traffic and is not very time sensitive. However, this information can be incredibly useful for allocating marketing spend and where to focus a company’s tactical efforts. For instance, with source data, you can identify which campaigns are most (or least) effective at driving traffic or whether it makes sense to localize your website to cater to people visiting from a foreign country.

Individual, high-level activity data

Different from baseline and source data, individual activity data begin to look at each person accessing your digital properties, where they are coming from, and the unique actions they take when on your site. These explicit data points play an important role when beginning any type of personalization effort – particularly with rule-based segmentation and targeting. As an example, using individual activity data, you can determine if the person on your site is a first-time visitor, what pages or products they viewed, how much time they spent on a page and, if applicable, which campaign they originated from. In an effort to respond to visitors in the moment, it’s important that this information be available as soon as possible.

Individual, “deep” behavioral data

The final category represents the crème da la crème of digital marketing analytics. When it comes to truly understanding each and every person visiting your website or mobile app, individual behavioral tracking – more specifically, “deep” behavioral tracking – is unprecedented. Neither aggregate baseline or source data, nor individual high-level activity data, can reveal much about an individual’s true preferences, interests and intent. Deep behavioral data can.  And more than any other form of analytics, individual behavioral data offer insights marketers need to deliver true 1:1 personalization.

The best behavioral data exposes what people are doing when they are on a web page (or in a mobile app) – scrolls, hovers, clicks, active time spent per page (not just time on page), both in the current session and for all previous sessions – to provide marketers with all the implicit data critical for confidently determining interest and intent. Only deep behavioral tracking can illuminate each visitor’s preferred categories, brands, price points, tags or topics, in the context of explicit data like referring source and geographic, firmographic and demographic information. Whether personalizing user messages, making inline content changes or introducing individualized product/content recommendations, it’s critical that deep behavioral data be captured and instantly available and actionable.


Simple Hierarchy of Digital Marketing Analytics

Putting it all together

At Evergage, our clients utilize every level of this hierarchy to provide their customers with the deepest possible understanding of each and every visitor’s, shopper’s or user’s engagement with their website, mobile app, logged-in environment or SaaS application. We live and breathe this stuff every day because we know just how important this information can be when it comes to influencing visitors and delivering automated 1:1 personalization.

When considering whether you truly know your audience, remember this Simple Hierarchy of Digital Marketing Analytics. If you’re not collecting data at each level and able to execute on individual action, contextual and behavioral data in real time, you’re missing an opportunity to truly engage your audience.