As marketers, we 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 aggregated data about which visitor acquisition channels are working.
We see reports on paid, organic and referral channels as well as our cost and conversions.
But what about the user data? It’s no secret that harnessing large volumes of data quickly and accurately during these phases:
- Pre-purchase (attract users)
- Conversion path (engage users)
- Retention (grow user base)
will positively affect your revenue stream.
Web Analytics Reconsidered?
The problem is that web analytics tools such as Google Analytics don’t focus on user journeys. They focus on sessions while prioritizing them over a detailed user journey.
Yes, there are user flows. But still web analytics largely is based on page views, sessions and fixed properties when creating traffic segments:
…whereas a user focussed segment in Intempt could look like this:
Web Analytics’ Focus on Sessions Affect KPI Calculations and Insights
This focus on sessions also leads to other problems. Not many marketers are aware of the fact Google Analytics and other web analytics tools use sessions to calculate KPIs that matter to them, e.g. the goal conversion rate:
Google Analytics calculates the goal conversion rate as follows:
Goal conversion rate = Number of unique goal achievements per session / Number of sessions
…whereas most likely you would rather like to investigate this conversion rate:
Goal conversion rate = Number of unique goal achievements per user / Number of users
A lack of visibility into the user’s footprints leads fuzzy understanding and vague idea what is going on your website.
In this post, I will explain how marketers may use user analytics to tackle the complexity of a variety of user interactions to get better insights and to make more successful decisions.
The User Journey – More Than Just Pageviews
Gaining deeper insights into your user behavior helps you to eliminate drop-offs and drive conversions.
Web analytics such as Google Analytics may give you raw ideas, but you would need to look at a granular level to really draw decisions that factually matter.
Why? Here are some reasons.
Most Users Convert After a Few Sessions
Depending on the nature of your business, the average number of sessions leading to a conversion naturally varies. Most marketers would agree though they rarely see conversions happening on the first visit.
Harvesting data from all user sessions combined instead of a single session allows marketers to see user journeys holistically.
Users Are Not Always Identified Properly
Users may sign in on a website, come back later as a logged-out user and complete a purchase. Users may also visit more than one domain within the same brand experience. Full user journey insights require to to collect data across multiple domains.
A 360 degree tracking retrieving data from all angles is required to provide detailed user behavior based insights:
Users Create Complex Journeys
Users often follow their own complex way towards a conversion point. Web analytics tools such as Google Analytics may not be able to reflect this complexity in their funnel visualizations:
Web Analytics’ Shift Towards User Analytics?
Web analytics explains the game, user analytics explains the players. Traditional web analytics tools have attempted to respond to this challenge of understanding users.
But all behavior must be tracked and associated with a single identified user. Web analytics systems were not designed to do so without significant aftermarket customization and they also ignore most user data generated aside from the website.
As mentioned above, you may use user flows (in Google Analytics) to get closer to a user focussed perspective. You may also sent custom events to the Google server if a user performs an action that is different from a page view (e.g. a button click).
While this all is possible, it needs to be implemented properly and constantly monitored making it become a hassle.
Combine Web Analytics With User Analytics For Best Results
Pairing your web analytics with user analytics allows you to share different perspective for best possible insights. But what features does an user analytics system need to have?
Let us take a look at some requirements:
Full Resolution – Broad Capture
All user data needs to be captured at scale and granularly (broad and deep) without sampling.
Web analytics’ segments are based on simple attributes like location, browser or session duration. A behavioral 360 user segmentation instead may use data from all angles to deliver best possible insights:
|SITE BEHAVIOR / PURCHASE VARIABLES||ENVIRONMENT VARIABLES||REFERRER VARIABLES||TEMPORAL VARIABLES||CRM VARIABLES|
|Customer/prospect||IP address||Referring Domain||Time of day||LTV data|
|New/ Return visitor||Country of origin||Campaign ID||Day of the week||Purchase History|
|Previous visit patterns||Time zone||Affiliate||Recency||AOV data|
|Previous product interests||Operating system||PPC||Frequency|
|Searches||Browser type||Organic search|
|Previous online purchases||Screen resolution|
|Previous campaign exposure||Device|
Autotrack – Deep Capture
Autotrack is opposite to manual tracking you may be dealing with currently. Any user analytics platform should track as much user data as possible (such as page views, clicks, form fills, etc.) without the need of manually setting up these events.
Retroactive – Looking Back in Time
Unlike Google Analytics and other web analytics platforms, user analytics should allow to use all previously collected data to create funnels post hoc at any time. Tracking doesn’t start when you define the funnel – it starts when the tracker is installed.
User Journey Viewer – Greeting John Doe
Why should we look at one user journey instead of a segment? Picking and spot checking certain users may inspire you to formulate powerful hypotheses that lead to actions.
Try Intempt’s User Analytics
Intempt employs machine learning to chomp through large swaths of data, analyze your user’s unique fingerprint and build a profile that not only targets users in real time but also predicts their future behavior.
All in one platform – without writing a single line of code.