I was reading the other day the very interesting article published by Adam Greco about Creating Conversion Funnels via Segmentation for Adobe Sitecatalyst where he basically breaks down visits into four different segments, depending which section of the site a user visited during a session. One of the things that a couple of colleagues debated was the first segment called “Awareness”.
According to Adam;s post, we include in the awareness segment people who have come to your website, but never seen a product, attempted to download a trial of it or purchased it. They simply express an interest because they saw an ad, heard something or read an article about it.
Here is how the segment looks like (visit level) in Adobe SiteCatalyst Segment builder:
I was talking with a fellow analyst at my current job and he made a very good argument saying that he doesn;t find the segment completely right as he felt that bounces should be excluded from the calculation. The debate was around the word “awareness”, I guess if people come to your home page and do nothing there wasn’t much awareness happening around your products even though you have to be a bit careful when you use the bounce metric in Adobe, it is not the same with Google Analytics.
I then remembered a couple of years ago in a finance company I was working for, where audience targeting was key in terms of post-onsite conversion profitability and we wanted to understand the differences between audience that move further down the funnel compared those who bounce immediately and how that affects conversion rate (not rocket science you might think, and to be fair it isn;t).
What those two stories have in common? In both cases you can use the same metric to get the numbers you are after.
Bounce Adjusted Unique Visitor
New Calculated Metric: Bounce adjusted unique visitors (BAUV):
Formula of BAUV = Unique visitors * (1- bounce rate)
Definition: The number of unique visitors who visited the site over a given period of time, adjusted for Visitor bounce rate (channelized).
Metrics explanation
Unique Visitors (aka users): UNIQUE tag is fed by COOKIE data, so is not 100% reliable – if web visitors have cleared their cookies, then they will be recognised as a distinct new unique visitor. This also applies to those who access the site through multiple browsers, and devices (and combinations thereof). The UserID feature should be able to give you correct user journey data.
Bounce rate: This should be separated into:
Bounce rate (New visitors):
Formula should look like this (Bounce visits (new visitors)/Visits (new visitors). The percentage of bounced new visitors over total new visitor visits
Bounce rate (Return visitors):
Bounce visits (returning visitors) / Visits (returning visitors). The percentage of bounced returning visitor visits over total returning visitor visits
Here is how to make the calculated metric in Adobe:
I will use an example with Moz, which I know is a data driven company, however they choose to focus on as less metrics as possible in order to not get overwhelmed.
Let’s assume that in 2016-2017 MoZ wants to go out and acquire as many people as possible. So as an acquisition manager you are trying to understand how your landing page works beyond the standard session based campaign metrics (bounce/conversions/ % new audience / etc) but also how people behave further down the funnel.
Now as a digital marketing manager you have two challenges:
- Go back to your Channel/campaign/adgroup/keyword level and understand why certain visitors abandoned the web site (Bounced visits) without taking any further action (2000 new visitors)
- Investigate how your Bounce adjusted unique visits behaved and perform against your KPIs (6000 visitors)
How to use bounce adjusted unique visits and conversion rate
Conversion Rate to submitted demo/trial (-BAUV) (KPI):
Explanation: The percentage of conversion from BAUV to Submitted Demo App
Calculation: Number of Submitted Demo Apps / BAUV
Note: consider running this against overall conversion rate to submitted apps.
Conversion rate to actual submitted applications (live) from bounce adjusted unique visits (BAUV)
Explanation: The percentage of unique visitors who landed and submitted live application form
Calculation: Submitted applications / BAUV
Your funnel/Cart/application form report should present the following:
- Conversion rate to initiated application (demo) out of bounce adjusted unique visitors (BAUV)
Calculation: Bounce adjusted unique visitors (BAUV) landed on application (demo) / Unique visitors - Conversion rate to submitted application (demo) out of bounce adjusted unique visitors (BAUV)
Calculation: Bounce adjusted unique visitors (BAUV) completed application (demo) / Unique visitors
Follow the same practise for Live accounts and you are good to go.
