What is Data Differentiation?
Data differentiation means understanding your data at a level of detail that enables you to find and target your audience effectively throughout the sales and marketing funnel.
Why is that important? Well, I believe good data equals good marketing.
I also believe that there is an opportunity for businesses of all sizes to think more critically about both the quality and utilization strategies of the data sets at their disposal so that they can run more effective campaigns.
Couple caveats before we really get started:
I am not a data scientist – even if I did dress like one when I was 9 – and I certainly don’t know everything there is to know about the still inexact science of marketing.
I should also acknowledge that data collection and segmentation certainly isn’t the only piece of the growth marketing puzzle – you still need creativity and great content, solid ad copy, engaging website copy, and a strategy for putting it all together.
But for me as a digital marketer and agency owner, the content is the gas, and I believe having actionable data about your target audience is the foundational element of marketing success; it is the engine that drives the car.
It’s also important because in this industry there are so many businesses taking data for granted or underutilizing what they have:
- Large businesses with huge data sets doing nothing with them
- Small businesses with just a few data points, struggling to connect the dots in small sample sets.
Basically, there is a gap between the collection of data and the effective utilization of it to drive marketing excellence, and in turn, profit.
In this post, I would like to provide some insight into why I think that happens, and some strategies that any size business can use to address it.
Before we can talk solutions, we need to acknowledge the problems, and there are many.
The Touchpoints Are Saturated
First, while data feels ubiquitous, it is not. Small and medium-sized businesses, in particular, have a select few options at their disposal – Facebook, Google, Instagram, LinkedIn, etc. I can count on 1.5 hands the number of platforms available to advertise on and collect data from and to target consumers with. These are all the touchpoints!
And as we are all very well aware, they are saturated with advertising.
Everyone Has the Same Access
Having a limited number of touchpoints also means we all have the same access to the same data sets: I remember when I first opened up the Facebook ads platform it felt like a treasure trove. I was astounded by the possibilities (interest targeting overlayed with demographic targeting overlayed with site behaviors? Gold mine!), figured I had pretty much solved this marketing thing, and was all ready to show off my shiny new toy.
Fast forward a few years and, spoiler alert, Facebook targeting didn’t solve marketing. Because no matter how many different interest targets or demographic options there were, it was still just limited to people on Facebook.
Additionally, and more importantly, everyone else had the same new toy – your competitors also have a Facebook page, and they all have the same access to the same data sets.
What has ended up happening is businesses scrambling to advertise on this narrow platform using all the same data sets provided by the platform – essentially just testing the same data sets against each other, over and over. And when one company owns both the data and the platform, who wins (hint: it’s not consumers and its not the advertisers and marketers)?
This leads us to the next problem, which is that unfortunately….
You Have No Choice
Marketing is omni-channel because people are on these platforms – this is where the party is happening. Not only that, they expect personalized connection at each touchpoint; showing up is not optional, AND you have to show up in all the right ways. On Facebook and Instagram, that means posting to your stories, giving context to each image, etc. On Google, it means writing optimized content that is shareable, links out easily, and is optimized for thousands of unknowable ranking factors. And so on.
The Data Sets Are Not Even Yours
The data on these platforms is served to you in easy to use buckets so everyone feels great about using it (flashback to me overly excited at discovering my new toy), but ultimately you don’t know how good it is or where it came from, and sometimes neither does the platform (see Cambridge Analytica). But as marketers, we trust the data to be good and viable.
Which is crazy because, as an example, only about 28% of Americans trust Facebook with their data:
And if you thought there would be some sort of correlation between that trust Facebook’s ad revenue, think again:
Facebook is happy to let you duke it out on their platform over the same limited space and targeting capabilities.
So, on the one hand, we have the consumers, who don’t trust these platforms with our own data, and on the other we have marketers use the same data as gospel and throw hoards of money at Facebook’s platform, based on trust in their data.
Why are advertisers spending more on ads if the majority of Americans don’t trust them with their data? Seems like a bit of a disconnect, right?
Facebook is an admittedly easy target, but it’s not just them; Google also has sizable control of search data, and sometimes they even compete against you with your own content:
Have you done a Google search for lyrics to a song recently? This is what you get, more often than not:
Let’s take a minute to acknowledge why this is a problem. Google, a company that has long incentivized websites to allow them to crawl their pages for use in their search engine by paying out for ad space, has now become so powerful that they are simply scraping that data and serving the info directly in search results. Lyricfind, in this case, not only doesn’t get a click, but they also are missing out on ad revenue that they would get if people clicked through to the site and served an ad impression.
But I digress…
Really what this all boils down to is this:
Digital marketing today consists of a few monopolies that mostly control both the data and the platforms.
As an advertiser/marketer you really only have three options:
- Accept the status quo, be lazy and keep using the same tired data sets that everyone has
- Take your ball and go home
- Use the third-party platforms as a starting point for building your own ecosystem of first-party data
Since I hate lazy people and I’m not about to quit on my clients, I am choosing option three. Here is why you should too.
Gather First-Party Data
Gathering first-party data to build audiences around is the only way to truly serve personalized, contextual content that differentiates you from everyone else.
Gathering first-party data to build audiences around is the only way to truly serve personalized, contextual content and advertising that differentiates you from everyone else.
Marketers and business owners need to stop being so reliant on these few platforms and start building their own ecosystems of their own data that they trust.
Segment Your First-Party Data Into Usable Lists
Then, segment segment segment. Create lists for all the actions taken on your site with your content by your leads and prospects: website interactions, email interactions, content interactions at various stages of the buying cycle, etc.
For example – let’s say you sell retreat packages, and you are a little light on attendees for an upcoming Budapest trip. You want to see people that have not yet booked with you in 2020, who are from California, and have visited one of your pages on Budapest – you can segment all of these contacts to create a very targeted list. From here you can either email them, engage with them directly using a local ad strategy, or you can combine that with the data provided from the aforementioned platforms.
Customize Third Party Targeting Using Segmented First-Party Data
Remember you don’t really have a choice – you have to be there. So you may as well be there with as targeted of a list as possible. And just because the data platforms are saturated doesn’t necessarily mean they are worthless, they just need some customization, layering on your own information.
In order to accomplish this, you need tools to collect and segment your first-party data.
Tools You Need: CRM
To start at the very beginning, you need a website with analytics. That is 1A. 1B is a CRM. Each of these platforms has their own pros and cons, and I won’t get too far into it for now. Just know that choosing the right CRM really depends on your budget and your integration needs, but there are a few key points to consider: price, integration, and internal resources.
These are not simple CRMs – with the right set up, these are marketing automation platforms that can use email data, IPs and site tracking cookies to give you individual, personalized information about your customers. You can then group this information together into segments and hit them with content and ad copy at every stage of the buying cycle.
Larger companies that have huge data sets have an advantage in CRM segmentation for a few reasons, but mostly because larger first-party data sets generally means more accuracy when translating to third party platforms. Their segmented lists are large enough that they can be uploaded directly into third-party platforms like Facebook, Google, or a Demand Side Advertising platform to create absolute audiences of those exact people. Smaller data sets won’t work for that, because the audience has to be large enough to feel anonymized; I can’t upload a list of 30 people and expect to get a full match rate, and also the platforms don’t like it when the ads get too personal).
Secondly, larger businesses typically have other means by which they can acquire data – they can get wider distribution through Demand Side Platforms, they can buy lists that more perfectly match their existing segmented lists.
The disadvantages for larger companies are that large data sets can be pretty unwieldy – they require (actual) data scientists and specialists to parse through all the different interactions and create workable automation for outreach and targeting.
Tools You Need: DSPs
Demand-side platforms (DSPs) are advertising platforms that allow advertisers to buy into programmatic advertising. They have similar internal data sets for targeting (demographics, interest targeting, purchase intent, etc.), but once again the true power of using these platforms is when you can combine the basic targeting capabilities with first-party data; cookie data, CRM data, purchase data, etc)
Like any other platform, the effectiveness of the targeting depends mostly on how deep your first-party data is data goes and how well integrated it is together. The better the data, the more granular you will be able to get and the more relevant your ads will be to the target audience.
Of all of these – we probably use Criteo DoubleClick, and the Trade Desk most often. These are definitely on the enterprise side of our solutions, mostly because of the traffic and spend minimums required to participate (The Trade Desk, for example, has a $20k min monthly spend qualification).
Smaller businesses don’t have the advantage of using huge data sets, and they simply don’t have a large enough audience to do any meaningful third party layering. For example, a business that only gets a few conversions can’t build a meaningful list for these kinds of mass targeting strategies. They can’t just upload their list of 30 or even 300 people to Facebook or Google or a DSP and then target those people – its too small of an audience and, as we mentioned, won’t be able to match enough profiles for it to be viable.
So, they have to be a little more creative, but they can still use this powerful combination of first and third-party data to differentiate their targeting.
Create lookalike audiences by uploading your segmented email lists to third-party platforms like FB – this involves taking a list and uploading it to FB, then FB matches those emails to profiles and creates a list of people that look like those people. You can then target these “lookalike” people with curated messaging based on the list segment. Were they interested in a certain product or service offering? Build a lookalike audience of those people, then refine it through third-party data like interests or demographics.
Site visitors can get cookied and then retargeted through any number of advertising platforms, including Google, AdRoll, Criteo, Bing, Facebook, etc. Savvy advertisers will retarget at the page or product level and keep an eye on frequency metrics to avoid feeling too intrusive or creepy.
Small businesses struggling to get enough traffic to build first-party data sets for retargeting can share pixels with collaborative businesses that share similar audience targets. This will allow you to grow your list faster with psuedo-first-party IP data.
Also, small businesses should take advantage of the fact that they have fewer contacts by working to make those contacts that much more meaningful. They can:
For smaller businesses that may not have the means to use a full CRM, using a chat function on their site to engage one-on-one and collect user-level data for future engagement can make a difference in creating a valuable connection.
Warning: Installing a chatbot on your website is simple. Building a process around responding to chat requests in a way that leaves customers feeling supported and heard is an entirely different thing.
Some of the leading chat technology out there is Mobile Monkey, a chatbot specifically for Facebook Messenger, Drift, a chatbot that integrates with many CRMs and has powerful automation tools, and even Hubspot has its own native chat that integrates directly with Hubspot workflows.
Obtain Call Data
Phone-based businesses can get in on the data collection game by installing simple scripts onto their site that dynamically swap out the phone number that appears on the site based on the source of the traffic. Then business owners can pull reports for each phone source to determine the effectiveness of a given channel or campaign.
There are a lot of ways to reach your customers. Unfortunately, they all tend to hang out in the same places. Creating unique data sets to target them where they are will help you stand out in a saturated and monopolistic environment.