Following our participation in the Google talent Academy in Paris, we wrote about three common mistakes publishers make in a complex/multiproduct fragmented digital advertising ecosytem. When dealing with many products and sales channels, it can get difficult to align prices, and ensure floor setups and PMP pricing strategies generate the optimal revenue.
We also wrote about our first piece of advice for these publishers in order to increase yield: working with a clean database, made of actual data (no estimates, no “fake” CPM line items, actual understandable naming convention…).
Today, we’ll present our second piece of advice: implementing a holistic monetisation management tool. In doing so, we’ll focus on two things:
- Making sure your monetization management system is actionable!
- Putting an end to “empirical” measures.
Make sure your system is actionable
As a publisher, you need visibility into your key metrics, whether it’s internal data or data from an external provider. That said, when faced with the current complex advertising ecosystem, it’s easy to get lost in the many dimensions/combinations and end up with a 25-tabs-2000-lines excel file that no one will ever take a look at, unless forced to do so. To avoid that, we advise to:
- Clearly define the objective – are you looking into brand spend report per week, or is the target better choosing the formats you put on page?
- Carefully select the data/dimension that you need to track in order to achieve your goal. Maybe you don’t need bidder, brand, buyer, SSP, country, format, ad request #… to provide insight to your sales teams.
- Carefully choose the time frame: if you’re not looking into the data every day, maybe daily figures will be a lot to process.
The report below, albeit super simple, is very actionable for a commercial team for instance:
US Publisher – Excel file sent to the Sales team on a weekly basis to detect programmatic leads
At Adomik, we can easily provide publishers with customized data feeds to fuel their data lakes or CRM systems. This is an example of our setup document:
You select: your demand/supply dimensions, the transaction information that matter to you, your key metrics, your chosen time-range and regularity… and you’re good to go! You’ll receive your custom file to fuel your reporting or other applications.
Do away with empirical measure
Once your reporting is communicated to all stakeholders , your teams will start taking actions to finetune your setup and increase advertising revenue. However, such actions should not be taken without a solid measure framework, to make sure that they’re translating into incremental revenue across your entire stack. For instance if you decide to raise your floors on two formats, you need to make sure the new floors are generating more revenue than the previous setup, all other things being equal.
We still see publishers measuring results with a before/after analysis, however such approach can be very erroneous;
- Seasonality is not taken into account – and you know the market IS seasonal. If you try something the first week of April, you’ll probably realise that you’re doing less than the end of March and might end up discarding the initiative… even if the results only show an end-of-quarter/beginning-of-quarter effect.
- Any unexpected market change can bias the results: new campaign starting, one campaign stopping.
Long story short, we believe every publisher should systematically experiment on a portion of their traffic. This form of A/B testing is used across many industries, including ours. The use of A/B testing is not as widely used as it should be despite its ability to test an create successful initiatives to increase revenue.
During the Google Talent Academy, we selected to examples of good implementation of this:
Example A: This setup, with 1% placements combinable for testing purposes, from a German publisher
Placements names:
4467 > pubname_country > banner_right > pubname_abtest_expo976_default
4467 > pubname_country > banner_right > pubname_abtest_ads976_b
4467 > pubname_country > banner_right > pubname_abtest_ads976_a
4467 > pubname_country > banner_right > pubname_abtest_ads918_default
4467 > pubname_country > banner_right > pubname_abtest_ads918_b
4467 > pubname_country > banner_right > pubname_abtest_ads918_a
Example B: This A/B test double framework with key value pairs and/or cookie segmentation, from another German publisher.
Conclusion
The second “way” to rethink and increase your yield with a complex stack is to rethink your management system: focus on what matters to you and what is most actionable, and actually evaluate initiatives using an A/B test setup.
Coming next: our 3rd piece of advice, at the organisational level this time: adapt your decision-making process. Hint: we’ll speak a lot about the opportunity cost – stay tuned!