Benchmarking is a method of evaluating campaign performance based on historical behavior. When implementing any form of advertising, whether it be social media, Programmatic, CTV, etc., it is important to have a goal in mind that is reflective of how previous marketing efforts performed.
At Code3, we know that benchmarking is an essential part of how our clients operate and what we do to help them succeed. Marketers need to know about benchmarking to help their clients through this part of their strategy.
Keep reading for an overview of benchmarking, how it works, and what marketers need to know.
Benchmarks: Definition, Types, and Where They Come From
There are several types of benchmarks. When it comes to advertising, the two main types of benchmarks are industry benchmarks and performance benchmarks.
Industry benchmarks are generally created by advertising platforms such as Meta, Snapchat, and Twitter. These benchmarks are metrics that inform how other brands within the same vertical perform on a specific placement or objective.
Code3 has implemented a Benchmark dashboard within Datorama, where a user can select which vertical they are interested in (ex: Luxury Fashion, Food & Beverage), and it will show the industry benchmarks based on internal data that has been filtered. This type of benchmark is useful so that a brand can understand how they are performing compared to their competitors.
Performance benchmarks are created based on the behavior of historical campaigns, ad sets, and ads and can be as broad or granular as necessary. When gauging how a campaign is performing, it is most informative to use this approach. Performance benchmarks have the following benefits:
- Ability to see how well a campaign is performing.
- If it is meeting or exceeding the benchmark, budget shifts can be made to encourage more spending toward that campaign or ad type.
- If it is below the benchmark, it will be an indicator that optimizations need to be made to that campaign, or that a better tactic may need to be utilized for the next campaign.
- Personalized to your brand’s historical performance.
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- Industry benchmarks are helpful when wanting to see how competitors are performing on a specific platform, however, there is no way of knowing how much or how little these brands are spending on these tactics to receive that benchmark.
- If your brand is spending more than the competitors, your KPIs might perform better than the industry benchmark due to the ability to win more impressions, clicks, conversions, etc.
- Adjusted based on the seasonality that your brand experiences throughout the year.
Though these two types of benchmarks are different, we're going to talk about best practices and common mistakes that can affect both types.
More on Seasonality
Seasonality is an important factor to consider when creating a benchmark. The advertising space is very reactive to events that happen throughout the calendar year (ex: Promotions, Holidays, and current events).
For example, when considering a retail account within the few weeks before Easter, the cost of Impressions, clicks, and conversions are going to go up because the space is being inundated with advertisers attempting to get their Easter promotions in front of consumers.
There are several ways to check for seasonality within your data:
- Plot the KPI of interest across the quarter and note any positive or negative spikes that occur. Cross-reference the dates against any holidays, promotions, or current events that may have happened during that time period.
- Compare what happened in this quarter to the same quarter of the year prior. Holidays and climate changes are going to occur at the same time each year. By observing how the KPI fluctuated based on these events, you can create a more effective media plan, and receive a better, more accurate representation of how campaigns will perform.
Common Mistakes in Benchmarking
When calculating benchmarks, there are a few common mistakes marketers make that could potentially make your reporting less accurate.
Unclean Data
The first mistake is using unclean data. Utilizing the raw data set can include problematic observations that can skew the overall benchmark calculation such as:
- Including 0’s in the KPI of interest could lower the output.
- Naming convention errors, leading to incorrect filters.
- Missing observations, incorrect dates, and duplicated fields are all risks of using the raw data.
With these problems, the accuracy of the calculated benchmark goes down. As a result, the goal you’re comparing your campaign performance against is incorrect. This leads to poor decision-making as far as budgeting, optimization, and future planning.
Using Mean Over Median
The second mistake we often see in benchmarking is using the mean of the data over the median when making the calculations. Using the average of the data is the natural inclination when wanting to find the benchmark of a certain campaign type. However, the average is sensitive to outliers, which can affect the overall accuracy of the output. The median finds the middle of the data, which provides a clearer picture of how the campaign is going to behave.
Not Accounting For Seasonality
As we mentioned above, seasonality is a major factor in benchmarking. Yet, marketers often miss it or don’t bother calculating it. When calculating a benchmark, it is best practice to create them quarterly to account for the impact that seasonality has on performance. This is because if only looking at one complete year of data is being considered, it isn’t accounting for the natural fluctuations that occur throughout the year, and the events that can cause the KPIs to go up and down.
Things change often in our industries, so we have to keep up to date and make sure the data reflect that.
Code3's Capabilities
As mentioned above, Code3 has created a universal dashboard within Datorama that would allow a user to see how their campaigns are performing based on anonymized data within certain verticals.
Additionally, Code3 has a trusted methodology for creating accurate performance-based benchmarks. They can be pulled for all channels by platform, vertical, and funnel at a quarterly level. We ensure that all of the common mistakes that were listed above are addressed and take into account your brand’s specific seasonality to provide the most accurate representation of how your campaigns will perform.
Ready to learn more? Contact us to find out how we can help you manage data.