Forecasts Frameworks for Metrics in Online Marketing
Create a validated forecast for online marketing metrics: traffic, conversion developments, synergy effects and goal achievement roadmaps.
Predictive performance marketing is a method that allows you to monitor likely marketing developments 6-12 months in advance . The following framework will help you to find the right measurement concept, the right metric and the right technology.
What can you predict
Choose the regression analysis environment you want to use to create the forecast.
Create forecast with Microsoft Power BI for Desktop
Create forecasts via Vertex AI on Google Cloud
Create forecast with IBM Cognos Analytics
Select some metrics that you want to forecast
Forecasts can be compiled from any modules of metrics and channels – either individually or from all calculated synergy effects.
SEO forecast for organic search engine metrics
Data from Google Search Console, Bing Webmaster Tools, from Google Analytics with organic traffic segment, Matomo, Adobe Analytics.
Ads forecast for paid traffic metrics
Data from Google Ads, Microsoft Ads, LinkedIn Ads, Facebook & Instagram Ads.
Organic development social media forecast
Prepare data from social media portals as a data set: This can be platform-internal data or your own website metrics.
Forecast for customer lifecycles
Data from analytics and CRM systems.
UX forecast for UX metrics
Forecast for the overall impact of UX optimizations on website usage behavior. Useful for users who do a lot of A/B website testing, for example with Google Optimize.
Historical forecast for previous data
A historical forecast – i.e. the examination of existing developments based on previous data – allows the validity of the calculation model to be reliably validated. During validation, a reliable seasonality value is determined in order to be able to create a realistic forecast.
Use validated seasonality value and finalize forecast
As soon as a seasonality value has been determined, which in the past forecast provided realistic trend developments in comparison with the real development, this must now be included in the real forecast.
Validation of seasonality with past forecast
Interpretation of the data
The further a forecast goes, the less realistic it becomes. A model based on a regression analysis can therefore only partially predict certain realistic patterns and of course cannot predict reality itself.
Forecast period: It is most reliable to forecast a development up to 6 months in advance – with less realism up to 12 months. A forecast period of about 120 points (days as data entries) is a good place to start. From there you can do custom testing.
Seasonality: A seasonality of 200 points (days as data entries) is a good start. From there you can do custom testing.
Interpret forecast data
Done: Now we can draw conclusions from our forecast. This applies above all to reaching certain threshold values after a certain month. If, according to the forecast, a value is permanently undercut from a point in time, there could be a need for action here.
From this we can also set up a manual monitoring system with alerts in a team . If monthly forecasts are made for selected performances and, for example, the organic development of website clicks from Google search threatens to fall below a threshold value, a standardized e-mail with a recommendation for action can be sent directly.
The predicted values are to the right of the red dividing line and show the potential opportunities for an extension of the upward trend using the example of organic traffic on the website.
It is possible to anticipate downtrends before they occur. A manual monitoring system with a team that takes over the warning function helps to warn our acquisition channels at an early stage if certain threshold values are drastically undershot in 6 – 12 months.
The forecast values are to the right of the red dividing line. In this example, if we aim for a certain threshold, we can estimate how long it would take to reach this goal or at least approach it.
In addition to the forecast data, exciting scenario simulations for KPI target achievement can also be carried out: Which advertising budget could be used under which conditions, and what team strength would be required to create organic SEO content in order to achieve a certain range.
Simulate team resources in online marketing with forecasts
What is Predictive Performance Marketing?
In this article you will learn how to create forecasting dashboards for holistic predictive performance marketing with IBM Cognos Analytics or Microsoft Power BI for Desktop:
What is Predictive Performance Marketing?
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