How to track your media campaigns with DataMa ?
With this article, you will be able to give clear and quick answers to these business questions. So the time that was previously wasted in searching for insights can be reallocated to the implementation of solutions that will improve your acquisition.
Let’s find out how DataMa breaks down the problem and identifies the key points of the analysis.
Take a step back in the analysis of campaigns
The improvement of acquisition campaigns revolves around three issues :
- The amount of budget allocated to the campaign, it is interesting here to study the scalability of the budget, i.e. if we multiply by two the budget of a campaign will it multiply by two the benefits or has it reached a ceiling?
- Le volume d’exposition des publicités au public ramené au budget alloué, qui se mesure avec le CPC (Coût Par Clic) ou bien avec le CPM (Coup Pour Mille impressions), lesquels sont des indicateurs de la qualité du trafic ciblé sur les différentes campagnes et de la concurrence existantes sur ces campagnes.
- And finally the final objective which is the conversion of users having clicked on the ad in the form of purchases (measured by the revenue) or leads depending on your business.
Build the market equation
The three issues mentioned in the previous paragraph can be translated very well into a market equation. The latter allows us to break down a global problem into sub-indicators in order to gain in readability, to provide a precise analysis which will give in a few key points the priorities to work on.
Let’s take the example of an e-commerce site, the main KPI (Key Performance Indicator, sometimes called “North Star Metric”) is the revenue generated by the acquisition campaigns.
By breaking it down by steps, we have in a very general way the expenses on the one hand, and the return on investment (ROAS) on the other hand, which we can itself break down into cost of exposure, conversion rate and average basket, as seen previously:
Built the data source
Gathering all these metrics in a single source is not an easy task. Indeed, they come from several sources, each of which may have different schema and level of aggregation:
- The consolidated budget is sometimes managed in a simple spreadsheet or can be accessed in some tools, but are not necessarily distributed across all campaigns, keywords etc.
- Spending, impressions and clicks can be deployed on different channels (e.g. Google Ads, Facebook Ads, LinkedIn Ads and many others) which are all sources of data to be gathered, which is often done in a data lake, via tools such as Supermetrics or Funnel.io, or reinjected directly into a web analytics tool (such as Google Analytics)
- Sessions, conversions and online revenues can come from a web analytics tool, which is most often subject to the consent problem and the breakdown by campaign/source of conversions assumes that the attribution model problem has been resolved.
With DataMa you have the possibility to combine these different data sources, either manually or via the DataMa Prep module.
Once combined in a single data stream (possibly via a DataMa Prep data flow), we can start the actual analysis.
Analyze the campaign performance
By compiling the data in DataMa Compare, we obtain the variations of the different indicators in the form of a “waterfall”.
In the rest of this article, we will focus on understanding a variation in revenue generated by our campaigns between two months.
The results are as follows:
The revenue attributed to media campaigns decreased by 4% between July and August 2021. This change is explained through three effects:
- The first is the increase in expenses relative to the budget of nearly 24%, especially on the Social Channel:
- The second, linked to the first, is a drop in CTR of nearly 12%. This can be explained in large part by the increase in spending on the social channel, whose impressions structurally have a lower click-through rate than the other channels, which induces a “mix effect”: the average click-through rate/impression drops simply because of the increase in the share of social in impression, to the detriment of SEA and Video
This “mix effect” can simply be visualized on a matrix representing the evolution of the share of impressions and CTR :
- Finally, the drop in the conversion rate (and the average basket) are linked to country issues: the conversion rate in Spain has dropped by nearly -36%, which alone explains more than the entire drop in the conversion rate
With DataMa, we were able to perform a global analysis of all campaigns, whatever their source, and we were able to quickly identify the campaigns and sources that explained the variation of the indicators..
Once this follow-up is automated, it will be possible to do it on a regular basis (daily, weekly…). DataMa can even automatically generate emails or notifications on Slack for example, to keep you informed of the evolution of your campaigns!
? Did you like this analysis?
Do the same at home ? !
- Create an account on solutions.datama.fr
- Adapt and upload the dataset used in this article
- Discover your own insights in the Datama app, as you can see below !