With limited attention spans and limitless competition, a marketing strategy aimed at anyone and everything can’t succeed. So you segment your audience: you serve ads to people who have visited your site, you focus email campaigns on people who have engaged on certain parts of your app, etc. Is it enough?
When putting together a strategy to segment your audience, you need something to segment on. OK, you know that. What might not be as obvious is that the deeper data you have, the more effective your data-driven marketing will be.
If you look below, you’ve got a grid of fan data. If you just extend the top row with a single data point like email address, that’s great (more people to email!), but having more emails alone won’t make it easier to target people.
But now start adding to each column and you can get a more complete version of a person:
Traditional demographics (age, gender, income)
Preferred marketing channels
The number of data points you have about a single fan is their depth of data, and the deeper you go, the more personalized your messaging can be, the more targeted both sales and marketing outreach can be, and the more cost effective your whole program can be.
Going deeper with what you have
There are two main steps in increasing your depth of data. The first is unifying your data points from various sources, whether it’s email, social, ticketing, or app activity. Not doing this essentially leaves all of your channels stranded with limited visibility. Each channel has some part of a story, but is incomplete—like flipping to a page in the middle of a book and expecting it to make sense.
By bringing all that siloed data together, you can exponentially increase your depth of data and get a more complete view of your fans. As fans come into focus, it’s easier to find segments within your database that you can target with relevant marketing and advertising, regardless of where the data initially came from. Here are a few examples of taking an omni-channel approach to marketing:
Emailing fans who like Star Wars on Facebook about an upcoming theme night at a game
Showing display ads for people who haven’t visited your app in a while
Targeting general admission ticket holders on social media for a VIP upgrade
The size of these segments is also useful in itself: if your overall brand voice is geared toward an older audience, but your largest audience segments skew younger and brands like Taco Bell and Nike appeal to them, that could signal you to make bigger shifts.
The second step in increasing data depth is ramping up your data collection strategy. One thing to consider is to vary the questions based on the type of offer (i.e., the bigger the offer, the more data you can ask for). If you use a marketing automation solution like Eloqua, make sure you use progressive profiling or smart forms, so if you’ve already collected someone’s email, don’t keep asking them for the same information (for example, ask for their zip code).
Using a social login like Facebook or Twitter can help you get deeper much faster than filling out a form. Facebook login (see below), for example, offers marketers fans’ names, emails, birthday, and Facebook likes. That’s an average of over 40 data points that you can collect in a single click. (Just make sure you have a tool on the backend to get that data out of the Facebook and Twitter apps.)
Segmenting by any factor
If you’re a sports team, you might wonder why you should care why somebody likes Dunkin’ Donuts. The most straightforward reason would be that you have some of tie-in or sponsorship with a brand and you want to advertise to people who engage both with your team and that brand (going back to the example of advertising a Star Wars theme night to Star Wars fans). You could also compare brand affinities before and after a sponsorship activation and show ROI to your partners.
Likes shouldn’t be looked at in isolation, though. If your brand doesn’t have a relationship with Dunkin’ Donuts, then a fan liking Boston Kremes doesn’t necessarily matter on its own. As mentioned above, you can use affinities for brands as guideposts for the messaging you use for that segment (or specific prospects in sales conversations). Implementing even more sophisticated strategies, if you find that season ticket holders have a higher likelihood of liking Dunkin’ Donuts, you can combine that with other data points like age, zip code and others to search in and out of your database for fans who look like your most valuable segments.
If you don’t have enough data, you’ll have a hard time finding those potential customers because you just don’t have a clear enough picture of who it is they’re supposed to look like. But as you learn about every concession purchase, favorite musician, opened email, or whatever else you’re able to collect, you get a fuller vision of your audience that you can act on, sending the right message to the right people at the right time.