Specific vs. Automated SegmentationBy Ricardo Bueno
Segments are essentially smaller lists of subscribers made up of the same type of persona. They’re important because, by creating segments, you can then start to create more relevant offers for each specific group thereby increasing your chances of conversion.
When a visitor comes to your website, you should be applying tags or custom field data about:
- Lead magnets downloaded,
- Products purchased,
- Survey answers chosen,
… and so on.
So when a visitor signs up for my Charge What Your Worth course, they self-identify - as a developer, freelancer, writer, etc. - and they’re tagged accordingly on my ESP (email service provider).
My typical subscriber who's been on my list for a few months has more than 100+ tags applied to them because I track literally everything. And you should do the same.
I call the above specific segmentation. "People with the tag 'customer'. People who have the 'business_type' custom field set to 'designer' as a result of completing a survey we send them."
There's also automated segmentation, which is a very interesting rabbit hole to explore. This is information like, 'What content consumption trends can we pick up on?', 'Where are they located?', or 'What key pages have they visited on our website?'
RightMessage allows you to combine both forms of segmentation data, specific and automated, in a pretty interesting way.
When you setup a change in RightMessage (like, "Swap out this call-to-action with X" or "Replace the hero image with Y"), you're going to tie that change to a particular segment.
And within RightMessage, you'll be able to create segments using the following criteria:
Browser - Information we get from the user's device
- URL Parameters (like UTM variables)
- Browser (e.g. mobile Safari)
- Device (e.g. iPhone 7)
- Cookie (any cookies that are set for the user)
- Language (browser's language)
Behavior - Information we get about how the user has engaged so far
- Original referrer
- Whether they're a first-time visitor
- Time since last visit
- Viewed X number of pages within Y timeframe
- Viewed a particular page, and whether they viewed it within the last X page views
- Original landing page
- Viewed X% of a particular page
- Content scoring - e.g., of the groupings of content of your website, like blog categories/tags, what trends can we pick up on?
- Key events being triggered (like the sort you send to Google Analytics, Mixpanel, etc.)
- Goals being triggered (like purchase events)
Contact - What internal data we store about the user
- Whether the viewer is known (true/false)
- Tag data
- Custom field data (name, email address, etc.)
Through the combination of the above, we could say something like:
- If the user hasn't yet opted in and has primarily read material on marketing, change the end-of-page CTA across all pages to be something marketing related, and on the homepage change the hero to be marketing-y.
- If the user hasn't yet opted in but showed up on our homepage from a blog focused on marketers, change all the CTA’s across the site and the core language to assume the viewer is a marketer.
- If the user is known but hasn't yet bought anything, change the CTA and the highlighted option in the navigation to point people toward the entry-level product.
- If the user is known to be a designer and has clicked a trigger link signifying that she needs help closing deals, when she's viewing our course sales page change the headline and copy to speak directly to designers, show testimonials from other designers, and when presenting the offer really focus on how the product will help her close more deals.
The above is pretty common sense. I don't think anyone needs to A/B test whether any of those bullet points, when implemented, would help increase conversions.
Over to you …
Are you properly tagging your subscribers to keep tabs on what they’ve downloaded? Are you properly segmenting your subscribers to identify customers vs non-customers and create more effective sales funnels?