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A Better Attribution Method - Treatment Control Testing

As digital marketing evolves, we must find better ways to track the impact of our efforts. With treatment control testing, we can get a more accurate view of attribution and improve our campaigns.

Alison Sperling

Alison Sperling

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Effective marketing requires a constant and consistent analysis of data. In particular, you need to know where your customers are coming from and how they are getting to their purchases, otherwise known as attribution. In short, marketing attribution refers to identifying the touchpoints your customers interact with to get to the final purchase. This allows you to better understand which channels are the most effective at getting customers to decide to convert.

Attribution is a huge headache for all marketers, as on average 30-40% of customers are unattributable each month. These unattributable customers create a huge blind spot for marketers as they don’t know what’s working so it makes it hard to allocate spend and efforts to campaigns and channels to drive more results

With the recent and continued dismantling of tracking like cookies and email statistics, the lack of attribution is only becoming more of an obstacle. There has to be a better way. There is, let’s dive into treatment control testing. 

Treatment Control Testing

What is Treatment Control Testing? 

Since marketers can’t rely on some of the more traditional methods of gauging attribution, a new method called treatment control testing is a reliable way to determine the results of your marketing efforts. This is when a marketing team creates two cohorts. One group receives the marketing campaign, while the other doesn’t.

The group that doesn’t get the material is the control group. By setting the parameters and tracking the results of the group that receives the campaign - the treatment group, you can confidently attribute those results to that campaign. This allows you to track attribution accurately and build future campaigns accordingly. 

You should also dive deeper on the results like what percentage of the group made a purchase, and the average order value. Treatment control testing is a highly effective way of using the actual results in your data warehouse to understand if your campaign was instrumental in driving sales. It can guide you to the answer to the age-old question, “How is marketing going?”

Pros of Treatment Control Testing

  • Quick and easy to implement and monitor.
  • Decrease reliance on dying attribution methods.
  • Measure revenue lift and key results faster.
  • Utilize results to adjust campaigns and spend accordingly.

Cons of Treatment Control Testing

  • Less focus on offline marketing and word of mouth.
  • Chance of retargeting customers that are already highly engaged.
  • A need for a larger data pool for significant results.

Test, Learn, and Grow Faster

Flywheel’s platform empowers marketers with the ability to quickly build targeted audiences utilizing the source of truth data in the data warehouse. As marketers build their audience in Flywheel, the audience size updates automatically and you can easily choose the percentages for your own treatment control experiments. From there, the treatment audience is sent to the marketing channel to receive the campaigns. This gives marketing teams a way to run their own treatment control tests and apply them to the destinations they use the most frequently. Then, Flywheel measures your experiment group performance and determines if the treatment performance was statistically significantly better than the control group. 

With Flywheel anyone on your team can access data from your warehouse to build powerful tests without any coding knowledge. The ease of experimentation and measurement means your marketing team can move faster, launching dozens of experiments a quarter instead of just a few. Taking the learnings from these experiments and applying them at scale will help accelerate your growth. Learn faster, grow faster. 

Creating a Test and Learn Growth Mindset, Automatically

Marketers are utilizing segmentation and audience building to drive results across platforms and especially paid media. To get data on the audience performance level, you’re relying on the tracking and reporting in the paid media platforms. This becomes problematic for a couple of reasons. 

For marketers, especially in eCommerce, that have hundreds of audiences in Facebook or YouTube, it can be challenging to keep track of which audiences are the winners and what to double-down on. Now combine this vast quantity of audiences with the diminished cookie tracking that paid media platforms are utilizing and you just aren’t able to see the true results from these audiences.

Flywheel’s Automated Experiment Evaluation takes the guesswork out of deciding which audiences to test and where to track the results, because it happens automatically. After you build an audience and export it, this audience group is then automatically stored in Snowflake with an identifier. This enables customers to automatically see revenue lift for any audience created in the Flywheel platform and exported to paid media, email, or CRM destinations. 

revenue lift performance by audience

With statistical significance testing baked in, you can see lift in revenue and other key metrics (ie. Logins, add to carts) automatically for each audience you’ve exported from Flywheel. This is essential for marketers to dive into granular data on what audiences are actually driving results and revenue.

Better Attribution with Flywheel Software

The no-code approach allows for instant company-wide use and enhanced security. Instead of creating individual data files and sending them between departments, all of that data can be accessed quickly and sent directly to more than 30 Flywheel destinations

Take your marketing to the next level with Flywheel. To learn more about what our platform can do to optimize and increase your experiments this quarter, contact Flywheel today

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