Focusing on your own, or first-party, data assets can help you save money and assist…
When speaking with CMOs about their data strategy, I am met over and over with a similar sentiment. At some point in our conversations, they often have a similar complaint: “We have no first-party data.” Or, “We don’t have any actionable insights into our consumers.” “We have no access to know who is making a purchase.”
Lacking access to a consumer transaction event (such as a purchase) is a key part of this challenge— and the goal of every CMO. Who wouldn’t want to know exactly how much revenue was a result of that ad campaign from last quarter? Similarly, lacking meaningful web strategy and engagement deprives the marketer of direct first party data.
So what are we to do?
For all you CMOs out there with no first-party data—fear not. Below is a basic data management strategy for you, to help you start planting the seeds to generate meaningful and actionable first-party data. Harvesting first-party data where there is little-to-none can be approached by following the steps below.
At first glance, you may think you don’t have a lot of first-party data. But even with simple brand sites, there’s still information to be gleaned about who your consumers are. While it may not have scale, there are ways to grow this valuable seed audience (we’ll cover that in #3 below).
Beyond your main website, there are many other places that a marketer can find first-party data. Have you considered any of the following options:
Start with what you know about your consumers and utilize third-party data to make those audiences even bigger. While demographic data seems logical, consider other attributes related to your “brand” as well, but don’t go too granular. One size does not fit all of your consumers, so look from a product/service perspective. As long as you aren’t aiming for “left-handed dentists from Ohio”, there is probably additional third-party data to leverage to meet your targets, including mobile, optimized age/gender, TV viewership, shopping, retail, movies, TV genres, etc.
As defined in AdAge, Lookalike Models are used to build larger audiences from smaller audience segments to create reach. The idea is that if you have already identified a small group of your ideal targets, you can use machine learning to grow that target list by finding additional consumers who look or act like them. So if you have a small group of consumers who clicked on your ad, you can feed that audience into the platform to identify additional new users who might also be likely to click on your ad. Your starting “seed” audiences can be anything from a low-volume first-party data segment to an audience composed of consumers who took action “X”, to a list based off your offline data.
Once you have built your target audiences, run your campaigns and use that information to capture additional insights. Whether you are tracking click-throughs, view-throughs or other events, make sure to pay attention to what works and what doesn’t. Accessing insights from your campaigns will help identify “behaviors” associated with the consumers raising their hand via the KPI’s you tag, which you can use to finesse your next round of campaigns.
Still think you don’t have any first-party data? You might just be looking in the wrong place. Take it step by step. It’s all about starting with seed audiences to work from, which you can use to collect additional first-party and campaign data. Best of all – your starting point does not require tens of thousands of unique users to commence a results-driven data management strategy.
If you want more information or help getting started – let me know. I’d love to help. You can reach me on LinkedIn here.
by Jeff Burak, VP Western US Sales, Lotame