Introducing SAYG, our marketing ROI partner

Q: Please give us a little background on your organization, the types of clients you work with, and the key issues you look to solve for those organizations? 

SAYG was established at the beginning of 2010 and has been a proud Partner of ScanmarQED ever since. We are privileged to work for a broad mix of blue-chip clients across a variety of industries (automotive, consultancies, electronics, financial services, FMCG, marketing service, and telecoms amongst others).

SAYG aims to identify, develop and implement solutions driving marketing ROI.

 Q: Are there any particular industries in which you specialize?

Not really. We are fortunate to have clients/experience across multiple industries.  

Q: What are some of the key marketing-related issues in your market right now which are challenging your clients?  

Currently, it is certainly the management of the outfall of the pandemic, where not a few industries have fared much better than originally expected (e.g. in the FMCG area), whereas others have been severely hit (notably in the tourism and travel industry).

From a broader perspective it is the establishment of a more holistic view in marketing analytics (with, e.g., the help of nested modeling).

Q: Can you give an example of the kinds of analytical insights you think are most important to your clients? What are the things that make a difference and create real incremental value? On the flip-side of this, what are some of the things you think clients need to avoid when implementing a new analytical solution?  

A major output is certainly (still) the proper reflection of the impact (marketing ROI) of an activity or – in the case of a competitor activity – its true impact. Once this has been established, the optimization work (on brand and/or portfolio level) flows from it accordingly.

A pitfall we are seeing time and again is the trend to make things more complicated than they need to be. A compact data set with the relevant data series is usually much more helpful than one with a massive amount of data series, where a not insignificant number is not too helpful in solving the underlying marketing questions.

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