Segmentation is one of the key pillars in marketing today. Put simply: when you don't segment you don't personalise; when you don't personalise you hugely reduce your connection with the people you're trying to reach; when you don't connect with your audience you limit your impact, no matter how much marketing you're doing for your product.
Marketers, like us, use segmentation for different purposes:
Sometimes Digital and CRM segmentation are even smarter by clustering data (see more on the difference) and generally speaking CRM segmentation + Digital segmentation = the answer to the question “what’s the most valuable customer conversion path?”
By connecting your CRM to Advertising platforms, you are able to acquire a larger percentage of those valuable customers you identified, or retarget particular segments you identified (e.g. “non-converted warm leads”). This is the bread and butter of advertising, whereby you are retargeting people you have already identified or similar people to them. It becomes a vicious circle, whereby you acquire traffic, pay again to retarget the same non-converted traffic, and so on… which is why advertising becomes an expensive trial and error method of channel optimisation.
Nevertheless, one problem with 1st party data (e.g. your CRM data, or your own Digital data like cookies/device ids) is that you’re restricted to a view based on your limited data!
What if you wanted to understand and acquire “Japanese Millennials who love cricket”, but you have no data on them? What would you do?
You are left with pre-defined targeting categories to suit your campaign objective, from demographics (age, gender, …) to interests and behaviours; the greater number of categories used, the more specific the audience definition becomes. However, there are a few pains that arise from this approach, one being the disconnect with the creative or content development, another being the loss of real control over the targeted audience in the context of your strategy.
At Audiense we believe the answer is in Social Data which allows you to segment based on how individuals are interconnected, by applying “clustering” to those connections. These connections are the whole fundamental basis of how Social spreads the word virally. Therefore using these connections is the most efficient way to understand and reach any segment.
As social data allows you to “cluster” by connections, it means that trends are uncovered revealing what common characteristics hold a collection of people together, without preconceptions or bias.
In Influencer Marketing this is even clearer. Here’s an example: we might say Javier Burón (our beloved CEO) is a Marketing specialist and we could tag him as such (like Onalytica or Buzzsumo would do). But if we define an audience with Audiense employees, understanding how that group is interconnected would identify Javier as an influencer. There’s simply no scalable way to tag everyone in niche situations like this one!
That’s why insights derived from social data in this way can be used tactically and strategically e.g. how do I uncover audiences to continue market growth and inform the development of our strategic and acquisition plans. Ipsos and Kantar TNS are the ultimate examples of Strategic Insights.
Social Data sits somewhere in the middle of tactical and strategic, helping build enough hypotheses to continue developing your consumer or acquisition strategy, for both B2B and B2C. In one of my favorite books, “Crossing the chasm”, Geoffrey Moore sets up the playbook for technology adoption and market development, and he suggests that you can only start by targeting “beachhead” segments - by this he means starting with a small specific group of people, rather than a whole market at once. One fundamental characteristic is that “they can reference each other when making a buying decision” - they therefore must be a narrow and niche enough group, and, no surprise here, interconnected.
Long live Social Data! ( even after 25th of May ;-) )