So I promised that I would finally put fingertip to keyboard and talk a little bit more about using Visitor Scoring…to finish up the series that I started a while ago.
If you’ve read my previous posts, you’ll know that we implemented a series of metrics for engagement measurement, culminating in a per-visitor score.
I wanted to share with you some of the insights and benefits of doing all of this, particularly in Discover.
An Engaged Segment
If you remember from the previous post, we talked about using a total of seven different metrics to try to ascertain engagement, or, when used in combination, could identify an engaged user segment.
The final metric was a measure of interaction and there were two ways to implement; either by just counting the number of visitors that participated in specific actions on your site, or the second way; by creating a scoring methodology at the visitor level and leveraging that as the final metric.
A little tougher to do, as it involved some additional code across the site, but definitely implementable, and now, we’re able to use that information to gain even greater insights.
The reason you should segment is because not everyone on your site converts – in fact, probably only around 3-5% actually do what you want them to do. So you use segmentation to analyse those that have converted and try to understand what made them different, what their different behaviours were, and if possible, try to use that as predictors of future behaviour by the other 95-97%, so that you can lift conversions.
Creating the engaged segment
Using Discover we created a Visitor container that contained the following rules, which we had previously determined worked best for our business.
The rules applied are from 6 of the original 7 indexes that defined engagement for us:
- Click Depth Index – which captures the contribution of page and event views
- Duration Index – capturing the contribution of time spent on site
- Recency Index – which captures the visitor’s “visit velocity”—the rate at which visitors return to the web site over time
- Loyalty Index– the level of long-term interaction the visitor has with the brand, site, or product(s)
- Brand Index – the apparent awareness of the visitor of the brand, site, or product(s)
- Interaction Index – visitor interaction with content or functionality designed to increase level of Attention
Notice the counts at the bottom of the segment creator – those seem to be a bit confusing as the number on the left (216 Visitors) represents the number of visitors that meet the criteria of the segment, but the number on the right (1,071,458 visitors) does not appear to be a count of visitors for the timeframe (for some reason I have yet to figure out).
Additionally, for this particular segmentation example, I wanted to ensure that our engaged visitors have actually become a lead (one of our primary goals). So our segment includes that, as well as them having a score of greater than 200, which means they’re interacting with a fair amount of content or doing a fair amount of activity (such as applying to come to study with us).
Saving the segment, it’s then ready for use.
Using the segment
One of the first things I looked at was where our engaged users were coming from, their average scores, conversion rates and application rates (or purchase rates), compared to all visitors who are not staff or students.
And I fell off my chair.
While the number is quite small comparatively, due in part to the timeframe I’m using, the score, lead conversion rate (leads/visitors) and application rate (applications/visitors), are significantly higher. This is to be expected due to the segment, but I wasn’t quite expecting the numbers to be that high.
Visual Site Analysis
I was keen to understand, visually, where these highly engaged users go on our site, so I used the Pathing Site Analysis report in Discover (one of my personal favourites).
Firstly I started with all visits to get a sense of what they do across common pages that we want them to interact with:
Fairly widespread usage of key pages. The thickness of the arrow indicated volume of traffic from one page to another – the bulk of traffic goes to the Courses homepage, from the site home page.
Next, I applied the same engaged segment:
Ok, I must need velcro pants because I fell off my chair again.
It appears that our highly engaged users take a whole different path. The colour indicates their propensity to become a lead. The big red steps are basically the lead capture process through our main tool – Figure Out Your Course.
And they seem to browse around first within courses then become a lead – which is also good to know. But once they become a lead, they tend to leave the site – which means we need to ensure that we’re effectively communicating with them through other channels, such as email at a later date.
Breaking down the traffic sources
In breaking down the Organic Search by time spent, we also see that a significant portion of highly engaged visitors spend more than 10 minutes on the site, and are more likely to convert to leads having done so, as compared to all visitors – another significant insight for us to use.
This is just one example of many that could be used on your site. Once you’ve identified your engaged users, you can segment them further by demographics, or by customer type, or by content viewed, or by member/non-member and so forth.
You can view revenue by engaged/non engaged (bound to be vastly different), or average order value etc.
If you use Test&Target, you’ll be in a great position to leverage the engaged user segment, targeting non-engaged users differently to increase their engagement levels.
All of this will help you gain better understanding into their behaviours, so that you can then further optimise your site to improve those conversions.
I’m keen to hear from others that have used Visitor Scoring, or Engagement Metrics across their site, coupled with Test & Target to lift conversions. Let me know what your thoughts or successes are.