Some would say Google Ads is becoming more and more a self serving / learning channel able to do it all, almost all. Still, there are numerous data points and activation possibilities just ready and waiting on the business side of things. So this would be a story on which of these points can and should be used in our optimization efforts together with some useful examples and caveats using an ML SaaS platform even if not in the hands of a data scientist - or is it just too dangerous?

How does an analyst help out or better say collaborate on client accounts making them more efficient? What kind of deliverable is expected and how it is utilized and later on automated so that it benefits the entire organization?

What does it take to evolve into a client centric agency where tools, technicalities and stats become a secondary priority. What are the essential skills and exercises for your entire team and how does the business adapt to everyday challenges.

Zorin's personal experiences gathered with small family businesses to large enterprises in terms of expectation management. If we look at Analytics through ages, how far it has gone, the massive tooling around it and the widespread ability to test, predict and sustainably create mistakes or better say learn one would think the expectations should be easy to manage. One would be somewhat wrong or?

Do you want your sister / brother marketeers worshiping your every single tag, email, insight? Well then widening may not be the answer to that but it is most certainly a step in the right direction. Regardless of the industry vertical there will always be a need to add more and more context. The widening procedure may be considered, and usually is, as a pure technical nuisance yet the benefits, if done properly, are enormous. Help your colleagues report faster, understand better and act with this easy to digest data.