It is clear that marketing measurement is critical for determining campaign success, optimising marketing spend, and driving business growth. Today marketers manage multiple campaigns across diverse media channels and through multiple devices and platforms, so accurate marketing attribution and effectiveness are more important than ever.
Reconcile multiple measurement approaches
The complexity and range of methods represent a huge challenge for marketers and analysts. Companies relying on online and offline channels should consider a combination of both approaches to maximise the advantages of each methodology and to mitigate their limits at the same time. Attribution modelling will assign credit for all sales to digital marketing channels, whereas econometrics will take into account offline channels and non-marketing sales drivers, like price and seasonality. This means they can lead to very different results for the major digital channels, making it very hard for marketers to calculate the true ROI of each channel.
A holistic approach to measurement
Here at Croud, we have developed a holistic approach that allows us to combine econometrics and attribution in a single reconciled view of marketing and market drivers. This unified marketing measurement method adopts a data-driven approach that combines the aggregate data obtained from the marketing mix modelling and the person-level data offered by multi-touch attribution into a single comprehensive view.
The aim of this holistic approach is to collect relevant information from all the marketing campaigns taking into account the granular data (MTA) while still considering the broader marketing environment and external factors (MMM). In other words, we get the benefit of granularity and accuracy through digital attribution while also taking full account of the non-digital environment.
Ensure accuracy through AI and modelling
Crucial to getting this right is ensuring that attribution is accurate and we achieve this by building predictive attribution models using AI (machine learning) whereby we can say that a model that better predicts sales is more accurate than another. This, by the way, is the same standard that is used to evaluate econometric models. Analytical techniques like the use of ‘hold-out samples and diagnostic metrics help ensure the models are robust.
In summary
Using the strengths of both models and combining the different types of data helps to produce consistent ROI results for marketers, allocate marketing budgets with efficiency and drive sustained growth. Great skill and expertise is needed as every business has a unique combination of online and offline factors which drive growth online.
If you want to learn more about expert data analytics using AI, or are interested in how Croud can help you use your data to grow online, get in touch.
