Is it safe to say that every business spending money on advertising inside Facebook and Google Ads would like to experience the most affordable and best way to find everyone in the world who is looking for their product or service? We think so. We also think those same advertisers would also like to significantly lower the cost of clicks, leads, and customer acquisition from their ad spend. These are the main two benefits from IDENTYO.
- Significantly lowering the cost of clicks, leads, and customer acquisition.
- Increasing reach while improving the quality of your target audiences via Facebook and Google’s look-a-like machine learning algorithm. Basically, being able to find everyone looking for your solution, B2B or B2C.
Custom audiences are the doorway to finding your perfect look-a-like audience. However, adding unqualified people to your custom audiences will dilute your ability to produce to lowest cost clicks, leads, and customer acquisition. One of the main keys to leveraging Facebook and Google’s look-a-like machine learning algorithm operates on the old computer principle of garbage in garbage out.
Behaviorally scored audiences built in real-time are key to powerfully segmented includes & excludes. The exclusion of unqualified people (garbage data) in your custom audiences will greatly improve the quality of the look-a-like audience.
The typical approach for most data-driven companies using custom audiences and/or look-a-like audiences is to onboard first or third party data into an ad platform, run ads, segment audiences and refine retargeting down to the most likely candidates. Rinse and repeat. As new channels are added, audiences are not connected/synced via channel or portable and new data silos are created. The right channel is unaware of who you’re targeting in the left channel and so on. The good news is there’s a better way.
Is onboarding your first party data good enough? Some marketers rely on the many data companies who claim they’re able to provide the perfect audience. Some of these companies are multi-billion dollar companies because obviously their solutions work to a certain degree. In an industry saturated with moderate success while using custom audiences, it’s understandable how marketers could be skeptical of the upside potential of a better way.
So let’s do a logical comparison. On one side we have Facebook and Google where 80% of all digital media spend exists. On the other side, we have data companies, any and all. Think about this, Facebook and Google have their retargeting pixel on more websites globally than any other company in the world. B2B and B2C websites all included. That massive global footprint of retargeting pixels functions as a real-time sensor network of behavior. This feeds a machine learning algorithm that knows if anyone is in the market for a specific product or service, they know. I guarantee it. Can any other company compare to Facebook and Google’s ability to sense when someone is in the market for anything?
Facebook and Google have massive power. IDENTYO leverages that power. Keep in mind, Google’s forthcoming cookie apocalypse, rendering third party cookies tracking useless has already started to roll out. Google will become even stronger and will force the world of programmatic advertising to pivot and change or perish.
Facebook and Google’s massive global footprint of retargeting pixels that functions as a real-time sensor network of behavior has an ebb and flow key factor, time. It’s not just about who’s in the market. It’s also about when someone is in the market.
The timeliness of data as it relates to leveraging Facebook and Google’s look-a-like machine learning algorithm is paramount to maximizing success. If you’re loading old data files or not building your segmented audiences in real-time, your campaigns will produce less than because of data decay.
When you add unqualified people who are unengaged with your brand (garbage-in) to your custom audiences, you’re essentially asking Facebook and Google’s look-a-like machine learning algorithm to give you more of what you put in, garbage-out. Thus, producing campaigns that will perform less than what they could be doing.
Custom audiences/Look-a-like audiences suffer when you add even a small number of unqualified people who are unengaged with your brand. Compound the effect with data decay from non real-time audience creation and it’s a recipe for underperforming campaigns. Even if you think your campaigns are optimized to rock-star status already.
This is where data companies building custom audiences fall short and why we can outperform the results you’re currently getting now. How do we know? Because our batting average of hitting home runs for our Clients is better than Babe Ruth’s.
