Every marketing team gets hit with an unexpectedly high customer acquisition cost (CAC) at some point. This signals it’s time to sync exclusion or suppression lists to your ad platforms. Doing so almost always leads to increased return on ad spend (ROAS) because:
- You can avoid wasting money on ads that are targeting people who have already purchased from you. This will help you to focus your budget on reaching new customers and expanding your reach.
- Suppressing existing customers can help you to avoid alienating them with too many ads, which can lead to a decrease in customer loyalty.
- It can help you to better measure the effectiveness of your campaigns, as you will be able to track the performance of your campaigns without the influence of existing customers.
While targeting is often perceived as an inclusive effort, the criteria that defines exclusion is equally important. Marketing teams require the ability to accurately, consistently, and reliably remove certain users or audiences from campaigns. By removing these users, marketing teams often drastically improve their efficiency, brand perception, adherence to user preferences. Here are some tips for exclusion criteria:
- Identify user attributes and behaviors of low quality prospects
- Ensure that users who have opted out of marketing communications are excluded across all marketing channels
- Use a single model to enforce exclusion across all targeting platforms
A midsized CPG brand was looking to reign-in ad spend. They began by building a data warehouse model that identified clients that had opted out of their marketing before adding recently churned clients and low value clients to the model. Finally, they added clients who had made a purchase in the last 30 days. This model was their exclusion list.
They began syncing this model to all of their ad platforms using Census on a daily basis. This decreased their CAC and increased their ROAS within one week.
These use cases are extremely important, but many marketers run into issues implementing suppression lists due to siloed customer data, resulting in a fragmented view of the customer. The solution is to create a single view of relevant customer data in your data warehouse. This Customer 360 serves as the source of truth for all your marketing campaigns and operations. Here's how. 👇
Step 1: Identify customer data in the data warehouse
You have online event data from a website such as visits, products viewed, content preferences, and cart status. You also want to bring in offline point-of-sale data in order to have an accurate view of purchase behavior. In this case, the customer abandoned the online funnel, but purchased offline.
Step 2: Create visitor profiles
Next, let’s turn all the event data into a visitor profile through identity resolution. This allows you to map event data to one person so that there’s a place where the full view of the customer can live and be used. Once you have the visitor profile populated with behavioral data from online and offline you can begin to create audiences based on your view of customer behavior.
For the data in this example, two common audiences could be customers who researched a product but didn’t buy it, as well as an audience of customers who just bought a particular product. Each customer segment requires a unique experience, but without a full view you could place customers in the wrong group.
Step 3: Orchestrate customer experiences
Finally, use a Data Activation platform like Census to sync customer profiles to all your marketing and advertising tools. The final result of this is an orchestrated customer experience where ad budget is used more efficiently towards customers who have a higher chance to buy.
