Marketers are tracking an increasing number of data points while the best practices for tracking user behavior and attributing online sales changes with shifting trends in privacy technology.
Does more data equal better results or are we burning extra cycles with diminishing returns? The marketers that will increasingly stand out from the competition will empower their campaigns with coding and data science strategies in order to draw actionable conclusions from their data.
PPC advertising is most effective when remarketing strategies are used. Remarketing means targeting a campaign to people that have already interacted with you in some way. This interaction could be website visits, video views, app installs or a combination of factors.
These data points can be used to create custom audiences in Facebook ads or other advertising channels to target these specific audiences or lookalike audiences can be created, made up of millions of people with similar interests.
To see the remarketing in action:
- Create a landing page with a Facebook pixel installed
- Drive traffic to the landing page with a generous free offer
- (Optional) Collect email address in exchange for the free offer
- Create custom audiences based on the Facebook pixel and email address list
- Market your next high ticket item directly to your new custom audiences for impressive results
Where does the coding come in? It’s helpful to know a few things when you’re stitching the plumbing together like optimizing your landing page and email capture form or testing that webpage hits are correctly attributing to the Facebook Pixel.
App Stores give devs access to some data that their users opt into like general demographics, device information and crash logs. Beyond that it’s up to developers whether they decide to implement additional data tracking measures like pinging a server and sending information about a user’s session, so long as they abide by that store’s TOS.
This data could take any shape and can be cross-referenced with other user behavior in a data visualization application like Google Data Studio to illustrate trends and create targeted charts identifying common traits among users high LTV (Lifetime Value)
Still with me? Good! This part is extra fun.
Use Data Vis tools like Google Data Studio to make sense of all this data and communicate it with your team. Include interactive filters empowering your team to shape and play with the data in real-time
Data connectors are available for a growing number of sources:
- 16 Google services like Analytics, Sheets and YouTube
- 161 partner services for Twitter Ads, Amazon, Quora, Reddit, etc.
Marketing organizations including Google claim new marketing developments will be lead by Machine Learning and predictive algorithms. Google claims marketers can model their own audiences, predict individual purchase conversions and predict Customer Lifetime Value (LTV) in the article Predictive marketing analytics using BigQuery ML machine learning templates.
The technical side of digital marketing is moving fast, stay informed and gain exposure to statistical data modeling tools and you will succeed.