I sometimes find it challenging to measure the true impact of my paid social campaigns on PPC performance. Despite not always seeing conversions directly within the social platform, these ads can significantly influence my overall marketing efforts.
To truly understand how paid social affects my other marketing channels, including PPC, I’ve found a few strategies that help me set up and measure effective tests.
Step 1: Determine Your Hypothesis
I always start by clarifying what I want to learn from my tests. Defining a realistic hypothesis that I can evaluate with available data is crucial.
For example, I often use the following hypothesis to measure the influence of social traffic on PPC:
- Search lift hypothesis: Increasing social media spend will boost brand search volume and PPC CTRs.
- Logic:
- Social ads build brand awareness, prompting more people to search for my brand during research and purchase stages.
- As more people become familiar with my brand, they tend to click on PPC ads more, regardless of search terms, enhancing both brand and non-brand CTRs.
- Exposure to my brand boosts trust, potentially increasing conversion rates.
- Measurement:
- Track impression and click volume for branded terms.
- Monitor CTR changes for brand and non-brand terms.
- Observe conversion rate changes for these terms.
My hypothesis varies, sometimes focusing on the lift from social spend or a surge in direct traffic.
Step 2: The Test
Setting up test parameters is my next step. It’s essential to avoid simply comparing results before and after changes due to possible seasonal effects. A geographic split test is typically my go-to method.
In this test, I increase social spend in specific geographies and analyze PPC data from these areas versus others. While selecting geographies, I control for various factors, such as regional televised sports events or confined TV commercials, to ensure my test results are valid.
It’s crucial to compare control and experimental groups by similar factors like income levels and region types. I also ensure my budget can accommodate anticipated increases in social spent, preventing budget limitations from skewing results.
Evaluating the impression share before and after allows me to ensure budget constraints don’t impact my outcomes.
Step 3: The Measurement
When starting measurement, I keep it simple, comparing platform data to see changes prompted by stopping social spend across all channels like TikTok, LinkedIn, Facebook, etc.
Upon halting social spending, I’ve observed mixed conversion rate results, with some regions showing increases and others decreases, though an overall drop in conversions was common.
Depending on my analytics setup, I delve into more complex analyses, looking at conversion touchpoint differences, visitor overlap rates between social and paid search, or different attribution models.
Before initiating any tests, I ensure that my measurement capabilities are robust enough to understand and interpret results accurately.
Step 4: Evaluation Beyond Test Criteria
While running tests, I measure results against my hypothesis but also look at additional variables that may provide further insight.
In one case, a brand I tested on believed they could cut down on brand advertising without affecting their search volume. However, a drop in common brand terms contradicted this. An evaluation across various factors showed unpredictable results that required expanded analysis.
I rely heavily on my experience to sniff out anomalies and conduct further internal evaluations.
When results seem unexpectedly drastic, I question whether it’s a quirk or if other factors, like recent AI-driven changes, are silently influencing outcomes.
What to Do With Your Social Impact Tests
The test setup is straightforward:
- Define your hypothesis.
- Choose how to test, preferably using a geographic split.
- Ensure you can measure the outcomes appropriately.
- Run the tests and evaluate the hypothesis-related metrics.
- Assess additional metrics for further insights or testing ideas.
For some, social channels like Facebook are top converters, while others see poor outcomes in isolation, necessitating tests to guide budget allocation strategies.
In these scenarios, companies with substantial social media spending reduce to test impact, while others might increase spending to assess performance changes.
Results vary widely across companies, with some seeing significant performance lifts and others noticing minimal changes, underscoring the need for personalized testing.
Conducting geographic split tests can offer incredible insights into how social media campaigns bolster or detract from other marketing channels.
Inspired by this post on Search Engine Land.
