My most recent post covered strategy & planning for digital marketing agencies, specifically from an SEO perspective.
In my mind, one of the most compelling questions was this: why aren’t there more success stories with SEO agencies? From my young perspective, I call out 5 things successful entrants can do for the best shot at success.
We may also find that these pillars of success (ex: depth of product vision, operational efficiency) rarely exist in isolation.
In their ebook, Informatica notes that data lakes are well-suited for large enterprises at $100MM+ and above. I would largely agree (for many reasons) and would also assert this leaves ample learning opportunity for brands below that threshold.
For the large enterprise above, there are often prerequisites to reaching that level. Specifically, you’ll often need to achieve certain sophistication before advancing to a level of ad hoc ML analyses of unstructured data in the cloud.
If we’re being honest, I’m probably bending and abusing some terminology here, but bear with me! For the vast majority of businesses (which also site below that revenue or capitalization threshold) – there’s a lot of power in data lakes, for the purpose of building small data – focused data warehouses.
To the points of my earlier posts, you need cohesive data that can paint a clear, end-to-end picture of your digital business. To that end, consider the following:
With the proper platforms, API scripts/pulls and storage, you can amass all of your key marketing data into one, or a few areas. Depending on your tactical execution, SQL joins or other BI/dashboarding software can be used to unite your data by URL.
From this picture, you can easily pivot, filter, splice, etc. to see what has been performing, how, and where the impact is going to your business.
Ex: in one picture, you can immediately juxtapose log file activity on your site with Search Console data to see where Google got hung in your site, as well as quickly identifying how much it’s impacting your visibility, and also quickly seeing which keywords are most affecting your business.
To take it a step further, this can be scaled across multiple clients, where you may write the client name or vertical into separate columns into all of your respective data pulls. This also facilitates large-scale rollups of your data. It’s my estimation that a compelling example of this may be Merkle’s quarterly digital marketing reports.
As with previous posts, I hope to continue peeling back this onion of excellence in marketing. However, my next planned immediate posts may pivot slightly to operational efficiency in the context of providing excellent client service.