How do you collect and analyze online advertising data from different sources and tools?

Campaign objectives reflect a business need, e.g., launch a new product, expand market share, or expand into new markets. The business strategy defines campaign objectives that are translated into media tactics. For example, the media strategy for launching a new product includes top-of-funnel branding/awareness and consideration tactics, including broader reach, look-alike audiences, and higher frequency. Mid-funnel and conversion activities, including re-targeting and revising creative with different call-to-action (CTAs). Different tactics for top- and mid-funnel media buys require different metrics and benchmarks. Metrics are not measures of success. Success is measured by exceeding the KPIs for the campaign.

For Video Ad Buyers: Video ad-buying is complicated. For the first time, there's no bread-crumb that ties specific users who have seen your ad to users who end up engaging with your product, brand, or service. This is because many of the technologies used by the sell-side aren't tracking discrete events, such as impressions. That said, these technologies exist, and should be used. The advice I'd give to ad-buyers is to work directly with sell-side publishers who are able to give an impression-level report, where each impression is tied to 1st party (and ranges) as well as what page/article/app/video, time/device/location, and view-ability/attention signals, and work towards both outcome analysis and to adjust targeting parameters.

Don't forget to define separate metrics fot each funnel step, example: - Awareness: Reach, Impressions etc. - Consideration: Clicks, Cpcs Soft Conversions etc. - Conversion: Hard Conversions, Cost/Conversion etc.

Before even setting any goals I would first start with overall business strategy, this involves research into any previous efforts made, market research, trends and the most up to date statistics. Based on this data as well as the business goals, this is what ends up shaping the actual goals that are set. What is also very important is to define which are primary goals, and which are supporting metrics.

Consolidating cross-channel advertising data takes organization, like a librarian cataloging resources for easy discovery. Build processes to centralize metrics across tools into unified reporting. Tag campaigns consistently for accurate attribution. Enrich with qualitative feedback. Establish insights cadence to share learnings. Connect the dots between efforts for full picture optimization. Soon you’ll operate a world-class advertising research library! Now go gather those measurement manuscripts and book major marketing ROI!??

Tooling is somewhat of a straight forward deal. What gets tricky is how do we find parity if we are using multiple platforms, we need to find or define our "Rosetta Stone", for example, are clicks on Facebook the same as clicks on Twitter? If we are using custom metrics we also need to define and standardize those across platforms if possible. A great tip is managing a "Data Dictionary", a very simple document that clearly states what metrics, from what platform, and what they mean to the strategy. E.G. Platform = Instagram Metric = Reach Measure = Individual accounts Meaning = Individual people that saw our stuff

Data collection has a couple of key factors to consider: - What data - Why? this is a very overlooked question, WHY do you need this data? data collection takes time effort and once defined should be automated so before taking another step, - From where? Websites, social, TV? - What method? [1st, 3rd party? cookies, pixels?] - How much? [data] is it just numeric or do you need visuals [banner ads etc - How often? daily data drops, realtime API integration? - For what? Is this to run regression models in R where you need very specific data? or are you just reporting on vanity metrics that support another goal?

En recopilación existen varias alternativas en mi experiencia. La que más me sirve es centralizar mis datos en un Google Sheets con toda la información de las campa?as de publicidad online. Si la fuente de tráfico tiene integración directa mucho mejor con herramientas como Supermetrics o Portermetrics se puede lograr integrar de forma sencilla estas fuentes. Finalmente, puedes mostrar gráficos usando Looker Studio o PowerBI lo que te hará tomar mejores decisiones.

Quantitative analysis of course if the first course of action, but its very important to factor in expert qualitative perspectives. Turning metrics into actionable insights requires a lot more than just adding data to a report. Several factors such as psychology, sociology, business, culture are key in really pulling out real value from analytics. Always consider having a strategist that understands all of this on the analytics team when drumming up insights for clients. It's not about what the data says, it's about what the data means.

They key here is; do all of these hold the same weight independent of the platform that contribute to the overall goal? This is highly unlikely due to several factors, but we can work within accuracy percentages specific to our own definitions. For example; If we know that Twitter has a variance of 15-25% when it comes to "click" measurements next to Facebook, then we can consider that when we receive Twitter metrics we can take into account that delta when we draft our final reports. Make sure to disclose that these are not exact numbers but a close alignment using this methodology so that the client is informed. This can be refined as more data is gathered and reported.