Long and loud have been the complaints about a lack of clear actions suggested by digital marketing data.
We’ve heard for years from CMOs, marketers, and analysts that they have lots of data, but little ability to do anything based on the data they have received.
Certainly there are instances where this is true, such as when strategic changes are needed, when business models need adjusting, or when new creative or copy are indicated because of flagging usership.
However, none of these can be fixed in any way by manipulating a digital analytics platform.
But does this ignore some critical and powerful changes that can be made using existing capabilities in digital analytics?
Continuing convergence in analytics suggests it does.
Certainly one of the most effective ways of looking at and taking action on data is through segmentation, also known as audience building.
In fact, it is difficult to imagine taking very much effective action at all without using segmentation in some manner.
And with the ability to export audience data into retargeting tools, the power of data collection begins to manifest in the delivery of very targeted and relevant communications that draw users towards conversion.
Generally speaking, segments are groups of users that either have or haven’t met certain behavioral criteria over a specific period of time. There are ways to configure segments based on sessions as well, but here we will only discuss user-based segments.
Segments can be interesting enough on their own. For instance, why are users from Michigan putting snowshoes in their carts in November, but not buying?
The real power of segmentation comes from your ability to communicate with that segment or audience with a message that helps them convert.
Perhaps you will want to entice them with a discount or free shipping, as in this example from moosejaw.com:
However, before you can get to this activity, you need to define that segment.
Your analytics tool should have a way to define segments by the criteria you choose. In the case of example above, you would select:
- A geographic location: Michigan
- An event: Placing it in the cart
- A date range: you can apply up to 30 days back, depending on your analytics tool of choice.
- A variation of “purchase amount = 0”
- A new page view without conversion
Your next step is making that segment eligible for retargeting. To do this, you will have to define the advertising network you want to use and assign the segment to one of the groups to which you’re marketing.
With your remarketing tool – which could be Adwords if you’re using Google – you will be able to see the segments that are available for campaign definition. From there, you can define the campaign parameters and the creative for the campaign.
Of course, you’ll still have to bid on the audience but if you target it properly you stand a better chance of getting the result you want.
In essence, what remains is repurposed data you collected about a group of users. Now you must decide how you want to reach them in order to convert.
There are limits to how heavily you’ll want to lean on retargeting to segments. You’ll need to set constraints on how long and how often you want to display an offer – leave it too long or show too many, and you will only annoy rather than entice.
If you’re targeting that segment which went to the shopping cart and didn’t buy, make sure that you are also not targeting those who did make a purchase.
Furthermore, you will want to make sure you targets are meaningful and you are targeting with purpose. The more specifically you can define your audience without making it too small, the better you’ll be able to format your creative.
Segmentation and retargeting can yield significant results with the right combination of data collection, but it’s a multi-step process that requires care and close attention.
Article and homepage images via Flickr.
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Data. It’s the latest ‘buzzword’ in the digital marketing world when it comes to content.
Digital has quite forcefully overturned the entire media industry, causing even the most traditional companies to adapt or be left behind.
What’s behind a successful data-driven marketing strategy?